![Potential Energy Surfaces QC's computational capabilities have the potential to enhance the accuracy of PES calculations, thereby improving the precision of drug development [103]. Additionally, it helps identify transition states on PES, which is crucial for determining reaction rates and mechanisms. This quantumdriven precision accelerates drug optimization, synthetic route design, and complex chemical process comprehension, with applications spanning materials science, catalysis, and environmental chemistry. QC stands as a transformative force in modern drug discovery. For research purposes, studying potential energy surfaces and reaction pathways requires the application of quantum chemistry methods, computational algorithms, and visualization tools [104]. Researchers delve into the intricate details](https://www.researchgate.net/publication/378917271/figure/fig2/AS:11431281229135204@1710338144400/Potential-Energy-Surfaces-QCs-computational-capabilities-have-the-potential-to-enhance_Q320.jpg)
Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier xx.xxxx/ACCESS.2023.DOI
Recent Advances in Quantum
Computing for Drug Discovery and
Development
GAUTAM KUMAR1, SAHIL YADAV1, ANIRUDDHA MUKHERJEE1, VIKAS HASSIJA2,
MOHSEN GUIZANI*3
1Sophomores at the School of Computer Engineering KIIT, Bhubaneshwar, India - 751024, (email: 22053323@kiit.ac.in, 22053347@kiit.ac.in,
2205533@kiit.ac.in)
2Associate Professor at the School of Computer Engineering KIIT, Bhubaneshwar, India - 751024, (email: vikas.hassijafcs@kiit.ac.in)
*3Professor of Machine Learning at Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE (e-mail: mguizani@ieee.org)
ABSTRACT The preservation of human health is of utmost importance, and unrestricted availability of
medications is essential for the sustenance of overall wellness. Pharmaceuticals, which consist of a wide
range of therapeutic substances utilized to diagnose, treat, and improve various diseases and conditions,
play a crucial part in the field of healthcare. However, the drug research and development process is
widely recognized for its lengthy duration, demanding nature, and substantial expenses. To enhance the
effectiveness of this complex process, interdisciplinary groups have converged, giving rise to the field known
as “Bioinformatics”. The emergence and future advancements of Quantum Computing (QC) technologies
have the potential to significantly enhance and accelerate the complex process of drug discovery and
development. This paper explores various disciplines, such as Computer-Aided Drug Design (CADD),
quantum simulations, quantum chemistry, and clinical trials, that stand to gain significant advantages from
the rapidly advancing field of quantum technology. This study aims to explore a range of fundamental
quantum principles, intending to facilitate a thorough understanding of this revolutionary technology.
INDEX TERMS Computer-Aided Drug Designing, QC, Molecular Docking, Quantum Simulations,
Virtual Screening, Ansatz, Ab Initio Methods.
I. INTRODUCTION
The contemporary challenge that confronts us pertains to the
intricate realm of drug development and discovery. This issue
is underscored by the time-intensive and exorbitant nature
of crafting effective pharmaceuticals, with costs potentially
soaring to a staggering one billion dollars [1]. The urgency to
tackle this conundrum is rooted in historical instances such as
the prolonged 35-year endeavor to develop a Malaria cure [2],
which resulted in numerous fatalities due to the prolonged
absence of a remedy. Consequently, a compelling imperative
arises to expedite drug development within constrained time-
frames and budgets.
Drug development is a complex process that includes
target identification, hit screening, lead optimization, pre-
clinical testing, and clinical trials [3]. While AI deployment
is not without its challenges – encompassing data quality
issues, and biological system complexities, it still proves
advantageous by expediting drug development compared to
traditional methods.
The prevailing endeavors to address drug development and
discovery challenges using AI encounter notable hurdles.
These encompass the scarcity of high-quality data, especially
concerning rare diseases, and AI’s opacity, which may lead to
safety concerns and inadvertently incorrect predictions [4].
Ethical dilemmas may arise due to AI’s limitations in mod-
eling the intricate and dynamic nature of biological sys-
tems, potentially leading to inaccurate outcomes. QC offers
a potential solution, leveraging its superiority over clas-
sical computers. Quantum computers can tackle problems
that even today’s supercomputers struggle with. Google’s
‘Sycamore’ system, containing 53 programmable supercon-
ducting qubits, achieved quantum supremacy in 2019 [5], [6].
The evolution of QC holds promise for drug discovery
and development [7]. Quantum technologies can potentially
revolutionize machine learning, financial modeling, cryptog-
raphy, and crucially drug discovery [8]. Quantum generative
models offer advantages by comprehensively covering distri-
butions due to their intrinsic probabilistic nature [9]. Quan-
VOLUME X, 2020 1
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
tum computers excel at molecular simulations, predicting
drug behavior and properties, thus enhancing in-depth drug
understanding as they bolster drug design with more precise
predictions [10]. Furthermore, QC accelerates machine learn-
ing algorithms by rapidly processing extensive data volumes,
managing complex computations, and generating more pre-
cise predictions [11]. Quantum computers’ speed accelerates
solving complex problems compared to traditional methods
and AI [12].
The primary motivation behind this research lies in ex-
pediting drug development, reducing costs, and redefining
the foundational approach to creating new drugs, diverging
from conventional methods. QC’s unique advantages extend
to chemistry simulations [13], opening avenues to explore
its potential in medicine. This research delves into quantum
computers’ medical capabilities in medicine, analyzing drug
behavior under diverse conditions using tailored algorithms.
The organization of this paper is as follows. A summary of
previous works and their contribution as well as their advan-
tages and disadvantages have been covered under Section II
with their core technology being discussed in their work.
Following that a basic overview of core and fundamental
quantum technology is discussed in Section III. Section IV
comprises of various steps of simulations in the process
and discusses quantum integration at each subprocess and
Quantum chemistry is discussed in Section V. Section VI
discusses the complete pipeline of the quantum-enhanced
drug development process. Moreover, in Section VII we
discuss the potential use of quantum computers for final
stage trial and testing for human use. Despite having nu-
merous advantages quantum computers still possess various
technological and ethical challenges which are explored in
Section VIII. Section IX states some future prospects and
further new applications. And lastly Section Xconcludes and
summarizes the work of this paper.
II. RELATED WORKS
The field of QC has witnessed remarkable advancements in
recent years. Historically, computers were not extensively
employed in drug discovery, but a noticeable paradigm shift
has occurred with the emergence of new terminologies in
the realm, including Computer-Aided Drug Design (CADD),
Computer-Aided Molecular Modeling (CAMM), and the
overarching concept known as Computer-Aided Drug Dis-
covery and Design (CADDD). Presently, quantum computers
are poised to serve as the next frontier for CAD.
Numerous researchers have undertaken extensive investi-
gations in this domain. QC, as a subject, has been under
deliberation since the 1980s [17], and a substantial body of
research already exists [18]. This section aims to provide
succinct summaries of key prior works and research papers,
offering a comprehensive overview of the extensive ground-
work conducted in the field of QC for drug development and
discovery.
Wang et al. [3] provide a brief overview of the various
steps involved in the process of drug development and de-
FIGURE 1: Sections of the paper
signing. Their work explores steps such as quantum sim-
ulation, molecular docking and QSAR. Their paper dis-
cusses how quantum computers can be used to combine the
knowledge of bioinformatics, cheminformatics and medici-
nal chemistry in a precise and concise manner.
Cao et al. [19] Highlights a distinctive focus, the explo-
ration of quantum simulations for molecular system detec-
tion, which sets this approach apart from existing methods
in drug discovery and development. They also support the
idea that a hybrid quantum-classical approach should be
employed for quantum simulation and quantum machines
to develop a fault-tolerant system capable of overcoming
the limitations of quantum computers, which are still in the
development phase.
QC significantly enhances the development of genetic
algorithms through its evolutionary iterations, as discussed
by Duela et al. [14]. This paper delves into the synergistic
relationship between quantum theory and genetic program-
ming, highlighting how they mutually benefit each other’s ad-
vancement. On one hand, quantum computers offer increased
computational capabilities, and on the other, genetic pro-
gramming contributes an element of true randomness. This
combination opens up new frontiers in both fields, allowing
for more complex and efficient problem-solving strategies.
2VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
TABLE 1: Related works
Author Summary Advantages Disadvantages Quantum
Technologies
Wang et
al. [3]
This paper discusses the
integration of
bioinformatics with QC,
focusing on the potential for
improved data analysis and
processing.
- Discussion of QSAR
prediction model and
usage in drug discovery
- Improved accuracy
and speed of
bioinformatics
computations
The entanglement of
many qubits at once is
currently a significant
challenge, limiting
practical applications.
- Molecular Docking
- Quantum Simulation
- QML
Duela et
al. [14]
Provided an overview of
quantum-assisted genetic
algorithms and their
applications in various
fields.
- Complimenting of QC
and genetic algorithms
for optimized solutions
- Potential to solve
complex problems more
efficiently
- All currently used
encryption mechanisms
would be rendered
obsolete
- High computational
cost for implementation
- Quantum Genetic
Algorithm
- Quantum Gates
- Quantum Annealing
Lau et
al. [15]
Provides a brief overview of
HypaCADD and its
applications in QC.
- Provides an overview
of all basic topics and
applications in a
concise manner
It does not discuss the
limitations of current
quantum technologies.
- QML
- Qubit-Rotation Gates
- Quantum Fourier
Transform
Mustafa
et
al. [16]
Discuss various algorithms
used, such as VQE, in QC
for bioinformatics.
Discusses
- Protein folding
- Various quantum
algorithms and their
applications in
bioinformatics
It does not provide a
brief discussion of
quantum theory and its
principles.
- VQE
- Quantum Annealing
- Quantum Fourier
Transform
Our
work
[2024]
Our survey paper, provide
an in-depth analysis of the
state of QC in
bioinformatics.
- Comprehensive
overview of QC
- Analysis of current
research and future
trends
It does not provide
in-depth knowledge
about the physical
implementation of
quantum computers.
- Variation Quantum
Eigensolver (VQE)
- QML
- Quantum Simulation
Lau et al. [15] discuss the concept of hypaCADD, a hybrid
classical-quantum workflow method for determining ligand
binding to proteins, and also considers genetic mutations.
They discussed how hypaCADD helps combine classical
docking and molecular dynamics with Quantum Machine
Learning to get a report on the impact of mutation. This paper
outlines a neural network constructed using qubit-rotation
gates. It maps a classical machine learning module onto QC.
All of this is explained by taking a case study of the novel
coronavirus (SARS-CoV-2) protease and its mutants This
paper also states how QML performs on par with classical
computing, if not better. It summarises a successful strategy
for leveraging QC for CADD by hypaCADD.
Mustafa et al. [16] discuss using QC to understand the
concept of protein folding. Understanding the concept of
protein folding is relatively hard because of the difficulty
of understanding and finding a stable shape with increased
size. A moderate protein consists of around 100 amino acids,
and there is a certain point where a classical computer cannot
devise a solution for the protein’s structure or properties. This
paper also discusses how two different algorithms are used,
VQE and Quantum Approximate Optimisation Algorithm
(QAOA), using Qiskit Nature.
In conclusion, the above-mentioned research papers have
significantly contributed to the development of drug discov-
ery using quantum computers. They all have provided various
aspects towards the improvement at various steps in the pro-
cess. They also mentioned various techniques and algorithms
to ease the process and reduce the cost of production.
III. BRIEF ANALYSIS OF CORE CONCEPTS OF QC
TECHNOLOGY
The field of QC and all of its technology, in itself, is new
to the world, so it becomes very important to understand
its fundamentals. Even though we have supercomputers that
can perform any assigned task very quickly, the scenario has
changed in today’s world where data is vast, and time is
limited. To analyze it effectively, we require even more pow-
erful computers to reduce the time required [20]. Although
quantum computers are still in their early stages, they are
highly expected to solve this problem as they can leverage
principles like superposition and entanglement, presenting
exponential speedup and transformative potential [21]. This
section will help us grasp the core concepts to the fullest for
VOLUME X, 2020 3
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
a better understanding of this technology.
The first and foremost difference between classical and
quantum computers is that classical computers use bits (bi-
nary digits) in the form of 1’s and 0’s. In contrast, quantum
computers use quantum bits or qubits. These qubits represent
0, 1, or any superposition of these states [22].
A. SUPERPOSITION
In quantum computation, the principle of superposition is
foundational, permitting simultaneous operations across all
potential states of a problem space. This principle under-
pins algorithms such as Grover’s algorithm for unstructured
search problems and Shor’s algorithm for integer factor-
ization, both of which leverage the inherent parallelism of
quantum states to achieve a computational speedup unattain-
able by classical counterparts. For an illustrative analogy,
one might consider Schrödinger’s cat thought experiment,
wherein the feline subject is posited to exist in a coherent
superposition of orthogonal states - namely, "alive" |0⟩and
"dead" |1⟩- until an observation induces the collapse of the
wavefunction.
Mathematically, the state of a qubit in superposition is
expressed as a linear superposition of its basis states, rep-
resented by complex probability amplitudes. The probability
of observing the qubit in a given state post-measurement is
determined by the modulus squared of these amplitudes, as
formulated by:
|ψ⟩=α|0⟩+β|1⟩,(1)
where |ψ⟩denotes the quantum state of the qubit, and α
and βare complex numbers such that |α|2+|β|2= 1. Upon
measurement, the qubit’s wavefunction collapses to one of
the basis states |0⟩or |1⟩, with respective probabilities |α|2
and |β|2, as depicted by:
P(|0⟩) = |α|2, P (|1⟩) = |β|2.(2)
This non-classical correlation between the states, a charac-
teristic of quantum entanglement, is central to the computa-
tional advancements brought about by quantum processing.
B. QUANTUM ENTANGLEMENT
To signify the peculiar role in quantum particle correlation,
Erwin Schrödinger coined the idea of quantum entangle-
ment [23]. Quantum entanglement and teleportation plays
a major and vital role as the backbone of various quan-
tum technologies, such as quantum communications, quan-
tum networks, and quantum computations [24]. Quantum
entanglement is a phenomenon where two quantum parti-
cles become deeply interconnected in such a way that the
state of any one particle cannot be described independently
without considering the state of the other particles. |ψ⟩=
1
√2(| ↑⟩A⊗ | ↓⟩B− | ↓⟩A⊗ | ↑⟩B). The entangled state |Ψ⟩
signifies the joint quantum state of two particles, where |0⟩A
and |1⟩Arepresent possible states for particle A, and |0⟩B
and |1⟩Brepresent states for particle B. The tensor product ⊗
combines these states, and the coefficient 1
√2ensures proper
normalization, adhering to quantum probability principles.
C. QUANTUM GATES
The primary driver behind the development of quantum com-
puters is their superior computational capabilities, realized
through the manipulation of quantum bits, or qubits. [25].
To carry out complex computation tasks, manipulation by
quantum gates is performed. They are analogous to classi-
cal logic gates, which are responsible for the manipulation
of logic bits. What this means is that quantum gates play
the role of building blocks in QC circuits; they manipu-
late qubits, which are the fundamental units of quantum
information [26]. Quantum gates are represented as unitary
matrices and these matrices are reversible, which means that
if a quantum gate is applied to the qubits and its inverse is
applied, it will return to its original state [27].
Quantum gates also play a major role in the normalization
of all the possibilities to 1. When a quantum gate is applied,
all amplitude components may change, but the overall sum-
mation of all possibilities of all potential outcomes remains
constant. Some of the most famous examples of quantum
gates are Pauli Gate, CNOT Gate, Swap Gate [28], Hadamard
Gate [29], and Toffoli Gate [30].
Target
Idenification
CADD
Structure-based
CADD
Ligand-based
CADD
Target structure
MD
De novo design
Ligand docking
Pharmacophore
modeling
Ligand structure
information
QSAR
Pharmacophore
modeling
Ligand based virtual
screening
Lead
Optimization
Drug candidate
FIGURE 2: Types of Computer Aided Drug Designing.
D. QUANTUM INTERFERENCE
Quantum interference, an intrinsic phenomenon in quantum
mechanics, arises when the probability amplitudes of two
quantum states converge. This process is analogous to clas-
sical wave interference and is described by the principle
of superposition. Constructive interference occurs when the
phases of the amplitudes align, enhancing the probability
(Ψconstructive = Ψ1+ Ψ2), while destructive interference
occurs when the phases are opposed, diminishing the prob-
4VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
ability (Ψdestructive = Ψ1−Ψ2). In QC, qubits leverage
this principle; aligned states (|0⟩or |1⟩) result in constructive
interference, amplifying computational pathways, whereas
opposing states lead to destructive interference, effectively
pruning the computational landscape. Exploiting these inter-
ference patterns enables quantum algorithms to outperform
their classical counterparts in specific problem sets. Despite
its potential, mastering quantum interference for robust quan-
tum information processing remains a formidable challenge
in advancing quantum technologies.
IV. QUANTUM SIMULATIONS IN DRUG DISCOVERY
Quantum simulation is a technique that possesses the capa-
bility to revolutionize our understanding of drug design and
discovery. Quantum simulation is a computational technique
that uses various high-level, complex quantum algorithms
to simulate and model complex molecule and material de-
signs [31].
A Major part of drug discovery involves understanding
the interactions of molecules, such as proteins in the human
body, in various environmental contexts. Here is how quan-
tum simulation can impact drug discovery:
1) Accurate Modeling: Quantum simulation accounts for
the quantum behavior of molecules, enabling more ac-
curate predictions of their interaction with each other
and with biological systems [32].
2) Understanding Complex Reactions: Quantum simu-
lation can provide insights into chemical reactions and
processes vital for drug development, such as enzyme
interactions and protein folding [33].
3) Optimising Drug Candidates: Quantum simulations
can predict the properties of potential drug candidates,
helping researchers identify molecules that are likely to
have the desired therapeutic effects [34].
4) Reducing Experimental Efforts: Quantum simulation
can guide experimental efforts by providing insights
into which compounds are worth synthesizing and test-
ing in the lab [35].
5) Personalized Medicine: Quantum simulations can help
tailor drug treatments to individual patients by predict-
ing how specific molecules will interact with a person’s
unique biological makeup [36].
In summary, quantum simulation holds the promise of
transforming drug discovery by providing a more accurate
and efficient way to model and understand complex molecu-
lar interactions [37]. As QC technology matures, it could also
play a significant role in accelerating the development of new
drugs and treatments. The following subsections explain the
various steps involved in drug discovery simulation.
A. MOLECULAR DOCKING AND QC
In molecular biology, drug designing, and discovery, it is
very important to predict the interaction between ligands
(typical small molecules) and receptors (usually proteins) for
the formation of a stable complex. Molecular docking is a
tool widely used for the prediction of these complexes [3].
Ligands typically bind within the binding site of receptors,
and docking tools provide the best optimal orientation and
conformation from them. These tools offer insights into the
binding affinity and biological activity of the ligand.
FIGURE 3: Computational Steps in Molecular Docking
Molecular docking in drug discovery synergistically em-
ploys sophisticated computational methodologies, encom-
passing Computer-Aided Drug Design (CADD), Quantitative
Structure-Activity Relationship (QSAR), and advanced deep
docking techniques [38]. This integrative computational ap-
proach enables a nuanced analysis of the intricate interactions
between small molecules and protein targets. Through the
application of these techniques, molecular databases are sys-
tematically screened with heightened efficiency, culminating
in the identification of top-scoring candidates poised for opti-
mized drug development. The streamlined process, depicted
in Fig. 3, underscores the technical prowess of molecular
docking, showcasing its ability to expedite the selection of
promising drug candidates through a meticulously guided
computational exploration of molecular interactions.
First molecules are selected from a huge pool of databases
and after that they are employed against CADD. Here CADD
aims to expedite the identification of molecules with de-
sired pharmacological properties while minimizing the time
and cost associated with experimental testing [39]. It en-
compasses a range of computational techniques, including
molecular modeling, virtual screening, molecular dynamics
simulations, and more [40]. CADD is divided into structure-
based and ligand-based subtypes. In drug discovery, ligand-
based methods scrutinize small molecule-protein interactions
using quantum algorithms, optimizing drug design. Structure
prediction employs quantum models to simulate biomolecu-
lar structures, aiding target identification which is explained
in Fig. 2. QC promises a transformative era in intricate
molecular analyses.
Molecular docking follows various steps, which are listed
below :
1) Preparation of Ligand and Receptor Structures:
Experimental techniques like X-ray crystallography and
NMR spectroscopy provide accurate 3D structures of
molecules such as ligands and receptors. In cases where
VOLUME X, 2020 5
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
TABLE 2: Advantages of Quantum Simulations
Benefits of Quantum Simulation Description
1. Highly accurate predictions Enhances comprehension of molecular interactions.
2. Insights into chemical reactions Vital in drug development, including enzyme interactions and
protein folding
3. Property forecasting for drugs Identifies molecules with desired therapeutic effects
4. Streamlined Experimental Efforts Guided selection of compounds for synthesis and testing.
5. Personalized Medicine Tailors drug treatments to individual patients based on biological
profiles.
experimental data is unavailable, computational meth-
ods can be used to predict the structures of these
molecules [41].
2) Grid Generation and Scoring Function: Using man-
ual and automated methods, the binding site of the re-
ceptor is defined where ligands are expected to interact.
A grid is generated or created around the binding site
to sample different positions and orientations of the
ligand. To evaluate the relationship between a ligand
and a receptor, a successful function is established. The
separation energy, which measures the nature of ligand-
receptor interaction, is estimated using the established
function [42]. A stronger binding similarity is desired,
with lower energy values [43].
3) Search and Docking:
To investigate various conformations, the ligand is po-
sitioned into the binding site and repeatedly rotated and
translated. The scoring function is utilized throughout
this search procedure to evaluate the energy of the ligand
in various positions and orientations within the binding
site. The placement and orientation of the ligand in the
binding site are optimized using a variety of search al-
gorithms, including genetic algorithms and Monte Carlo
techniques [44].
4) Scoring and Ranking:
The computed binding energies of the created ligand
conformations are used to rank them. Conformations
with the lowest binding energy are considered to have
the highest binding affinity and are chosen as potential
binding sites [45].
5) Analysis and Interpretation:
To better understand how the ligand and receptor inter-
act, additional analysis is done on the top-ranked ligand
conformations [46]. Types of interactions, such as hy-
drogen bonding, van der Waals forces, and electrostatic
interactions, are identified. The binding postures and in-
teractions between ligands and receptors are visualized
using software and visualization tools [47].
6) Validation and Further Studies:
Experiments using X-ray crystallography or binding
tests can be used to verify the predicted binding pos-
tures. If the docking predictions are right, they can direct
additional research towards improving the binding affin-
ity and selectivity of ligands, such as structure-based
medication design [48].
Despite having various advantages molecular docking
faces several critical challenges, and the integration of quan-
tum computers holds promise in addressing these issues
[49]. One prominent challenge is the treatment of protein
flexibility. Classical molecular docking often assumes rigid
structures for both small molecule ligands and target proteins,
even though proteins can undergo conformational changes
and exhibit flexibility, influencing binding interactions [50].
Quantum computers offer the potential to model protein
flexibility more accurately by considering multiple protein
conformations and their energetic contributions, providing a
more realistic representation of binding events [51]
Preparation of
ligand and receptor
Grid Generation
and Scoring Search & Docking
Scoring & Ranking
Analysis and
Interpretation
Validation and
Further studies
FIGURE 4: Process of Molecular docking
Another significant challenge in classical molecular dock-
ing is the simplified treatment of solvation effects. The
solvent environment plays a crucial role in molecular in-
teractions, yet traditional docking simulations often employ
simplified solvation models that may not fully capture the
complexities of solvent influences on binding. As discussed
by Gioia et al. [52], classical docking methods suffer from
limitations related to the static or semi-flexible treatment
of ligands and targets, neglecting solvation and entropic
effects. This deficiency strongly limits the predictive power
of traditional docking approaches." Quantum computers can
conduct more sophisticated and precise simulations of solva-
tion effects, enhancing our understanding of the stability and
energetics of ligand-protein complexes [53].
Moreover, molecular docking demands substantial compu-
tational resources, particularly for larger and more intricate
biomolecular systems [54]. Quantum computers, with their
inherent parallelism [55]–[57] and efficiency in quantum
chemistry calculations, have the potential to significantly
accelerate these computations, reducing the time required for
molecular docking studies.
6VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Quantum mechanical accuracy is also crucial. While quan-
tum mechanics-based methods offer a more precise descrip-
tion of molecular interactions than classical force fields [58],
their computational demands have limited their application to
relatively small systems on classical computers [59]. Quan-
tum computers can expand the applicability of quantum me-
chanical calculations to larger and more biologically relevant
systems, thereby enhancing the precision of binding affinity
predictions [59].
Furthermore, quantum computers can revolutionize the
exploration [60] of chemical space by efficiently sampling
a broader range of chemical compounds for potential drug
candidates, potentially unveiling novel therapeutic molecules
that might be overlooked using classical approaches.
In summary, the integration of QC into molecular docking
offers promising solutions to these challenges, advancing the
field of drug discovery and development.
B. QML FOR VIRTUAL SCREENING
To develop potential drug compounds, we need to identify
and understand the interaction of a target biomolecule, which
can be achieved through virtual screening using QML [61].
QML is an interdisciplinary field that integrates and unites
QC and machine learning to address complex problems [62].
In traditional virtual screening methods, we usually stim-
ulate the biomolecule interaction using classical comput-
ers [63]. However, QC possesses various capabilities, and
QML leverages these unique properties to accelerate this
process. Here are the following steps that can be used to
understand the workings of QML for virtual screening in
drug discovery:
1) Quantum Simulations: Simulations of quantum sys-
tems have always been significantly faster on quantum
computers when compared with classical computers.
In drug discovery, accurate modeling of molecular in-
teractions is required, which allows the researchers to
study various complex biochemical processes. This can
be achieved by quantum systems, which are hard to
stimulate classically [64].
2) Quantum Feature Encoding: The more compact the
representation of molecules is, the more efficient and
faster the analysis of potential drug candidates. QML
can encode various molecular structures and properties
in the quantum state [65].
3) Quantum Neural Networks: Neural networks can un-
derstand and learn the patterns of quantum data that
might not be easily perceivable using classical methods.
To process quantum data and to perform quantum com-
putations, quantum Neural networks or quantum circuits
are used [66].
4) Quantum Kernels: Quantum kernels are equivalents
to classical kernels, which are used in Support Vector
Machines (SVMs) in classical machine learning [67].
To increase the accuracy of machine learning models
for drug discovery tasks, we can use quantum kernels
to capture quantum correlations.
5) Quantum Molecular Data: For determination of
molecular structures and properties of the complex,
we use various processes and data technologies such
as Nuclear Magnetic Resonance (NMR), X-ray and
crystallography, and QML can be used to improve and
increase its efficiency.
6) Quantum Search Algorithms: Various quantum al-
gorithms can be applied in this context. For instance,
Grover’s algorithm can search through a large database
of potential compounds [68]. These algorithms easily
speed up the process.
It is highly expected of QML techniques to save a huge
cost as well as increase time efficiency. A real-life example
of this approach was recently seen in research conducted
by McKinsey & Company in 2019, which examined and
calculated the potential cost and time efficiencies achievable
through the integration of QC and machine learning tech-
niques in drug research and development. These analyses
explored diverse scenarios to estimate the savings that could
be realized by adopting these advanced technologies in the
pharmaceutical industry [69].
C. QUANTUM ALGORITHMS FOR MOLECULAR
DYNAMICS SIMULATIONS
Quantum algorithms for molecular dynamics simulations
leverage various QC techniques to stimulate the behaviors
and interactions of molecules at the quantum level. Quantum
algorithms can provide more accurate and efficient simula-
tions by exploiting the inherent quantum properties of the
systems being modeled [70].
•Wavefunction Simulations: Quantum systems and
molecules are both described by wavefunctions that
capture the probability amplitudes of different quan-
tum states [71]. To enable more accurate calculation of
molecular properties and behaviors, quantum computers
directly simulate the time evolution of these wavefunc-
tions.
•Quantum Phase Estimation: To gain insights into
molecular dynamics and chemical reactions, we must
determine energy levels. This algorithm is used to esti-
mate the eigenvalues of the quantum system, which are
equivalent to the energy levels of the molecule [72].
•Quantum Walks and Quantum Monte Carlo Meth-
ods: This algorithm helps provide insights into the dy-
namics, conformational changes, and thermodynamics
properties by stimulating the behaviors of molecules and
their components [76].
•Excited State Calculations: Quantum computers can
accurately compute the excited state properties of
molecules, which are crucial for understanding pro-
cesses like electronic transitions and energy trans-
fer [77].
V. QUANTUM CHEMISTRY FOR DRUG DESIGN
To develop and discover drugs, we need insights into the
electronic structure, properties, and interactions of molecules
VOLUME X, 2020 7
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
TABLE 3: Real-life application of QCs
Study Molecule(s) Quantum Devices Variational Ansatz Optimizer Results
Liu et al. [73] Small molecule IBM Quantum UCCSD COBYLA Qubit-Frugal Low-Variational Quantum Eigensolver (L-VQE)
Pavel et al. [74] Drug candidate Rigetti Aspen-9 RYRZ L-BFGS-B Permutation Variational Quantum Eigensolver (PermVQE):
Connectivity-Optimized Quantum Chemistry
Rattew et al. [75] Protein-ligand Google Sycamore UCCSD SLSQP Evolutionary Variational Quantum
Eigensolver(EVQE) Advancements
at the quantum level, which can be achieved with the as-
sistance of quantum chemistry [78]. The main factors in
drug development are efficiency and reduction of side ef-
fects, quantum chemistry enables researchers to understand
the basic, fundamental behavior of molecules, predict their
properties, and design new drug candidates that meet the
above requirements [79].
1) Electronic Structure Calculation: Quantum chemistry
methods and techniques, such as Hartree-Fock, Density
Functional Theory (DFT), and correlated various wave-
function methods, are used to calculate the electronic
structure of molecules [80] accurately. This information
includes the distribution of electrons and their energy
levels, which are critical for understanding molecular
properties and reactivity.
2) Binding Energy and Affinity Prediction: Quantum
chemistry calculations can predict the binding energy
and affinity between a drug molecule and its target
protein or biomolecule [81]. This information is es-
sential for assessing the strength and quality of drug-
target interaction and designing molecules with optimal
binding affinities.
3) Transition State Analysis: Quantum chemistry enables
us to study reaction mechanisms and transition states,
which are crucial for understanding enzymatic reac-
tions, metabolic processes and chemical transformations
in drug metabolism [82].
4) Quantum Mechanics/Molecular Mechanics (QM/MM)
Simulations: In drug designing and discovery, QM/MM
simulation combines the accuracy of quantum chemistry
with the efficiency of classical molecular dynamics
simulations [83]. They are used to study reactions
occurring in complex environments, such as enzymatic
active sites.
5) Solvent Effects: It is very important to understand how
molecules behave in different solvents as it is important
for predicting drug solubility, stability, and bioavailabil-
ity [84]. Quantum chemistry can account for the effects
of solvents on molecular interactions.
6) Electrostatic Interactions and Charge Distribution:
For researchers, it is very important to understand how
electrostatic interactions contribute to binding and re-
activity, and quantum chemistry helps to reveal this
distribution of charges [85].
7) Prediction of Spectroscopic Properties: Quantum
chemistry methods can predict spectroscopic proper-
ties, including UV and visible absorption spectra, NMR
chemical shifts, and vibrational frequencies [86]. These
predictions aid in characterizing molecules and under-
standing their behaviors.
8) Design of Ligands and Inhibitors: Quantum chemistry
also guides the design of ligands and enzyme inhibitors
by optimizing their structures for maximum binding
affinity and selectivity [87].
9) High-Throughput Screening: Quantum chemistry cal-
culations can be used in high-throughput virtual screen-
ing to quickly assess large libraries of potential drug
candidates and prioritize molecules for experimental
testing [88].
A. QUANTUM ALGORITHMS FOR VQE IN QUANTUM
CHEMISTRY
Quantum chemistry involves studying molecular and mate-
rial behavior at a quantum level, which is a very challenging
and complex process. To solve the challenge of understand-
ing complex quantum mechanics we can use quantum algo-
rithms such as VQE, as they tend to approach the problem
more efficiently [89]. The following points describe the VQE
concisely:
•Objective: The VQE algorithm is used to approxi-
mate the lowest energy state (ground state) of a given
molecule [90]. The Hamiltonian ( ˆ
H) represents the total
energy of the molecule’s quantum states.
•Ansatz: VQE uses a parameterized quantum circuit
(ansatz) to prepare a trial quantum state [91]. This state
is prepared using quantum gates, each controlled by
specific parameters that can be adjusted.
•Quantum Measurements: Measurement of ansatz
state is done on a quantum computer to estimate its
energy concerning molecule’s Hamiltonian [92].
•Classical Optimisation: The estimated energy is then
used as a cost function in a classical optimization pro-
cess. The goal is to adjust the parameters of the ansatz so
that energy is minimized, thus finding an approximation
to the ground state energy [93].
•Iterative Process: The optimization is an iterative pro-
cess. After each iteration, the ansatz is updated based on
the classical optimization results. The process continues
until the energy converges to a minimum.
•Hybrid Nature: VQE is a hybrid algorithm because it
combines quantum and classical computing [94]. Quan-
tum computers perform the quantum measurements and
gate operations, while classical computers handle the
optimization and control of the quantum hardware.
•Application: VQE finds applications in quantum chem-
istry, optimizing molecular structures and electronic
8VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
configurations. It also addresses complex problems
in optimization, material science, and drug discovery,
showcasing its versatility across diverse scientific do-
mains.
One of the current and major examples of quantum al-
gorithms was seen and implemented by FRESNEL1 by
PASQAL [95]. A noteworthy example of this quantum al-
gorithm designed to expedite the drug discovery process
motivates more ventures in this field.
B. QUANTUM COMPUTATIONAL METHODS FOR
MOLECULAR STRUCTURE PREDICTION
Quantum computational methods for molecular structure
prediction are advanced techniques employed in research
for the accurate modeling and prediction of molecules’
three-dimensional structures [96]. These methods harness
the principles of quantum mechanics, a fundamental theory
describing the behavior of matter and energy at the quantum
level. In molecular structure prediction, quantum methods
offer several advantages over classical approaches, enabling
researchers to gain deeper insights into molecular properties,
interactions, and behavior. The following technologies are
currently being explored for molecular structure prediction:
1) Density Functional Theory
Density Functional Theory (DFT) is a powerful computa-
tional method used in quantum chemistry and condensed
matter physics to study the electronic structure and properties
of molecules, solids, and materials [97]. It is particularly
useful for systems with many electrons, which makes solving
the Schrödinger equation extremely challenging or even im-
possible due to its high computational cost. DFT provides a
more practical approach by focusing on the electronic density
rather than the wavefunction of the system [98]. There are
many benefits to DFT and some of them are mentioned in the
below paragraph.
2) Ab Initio Methods
Beyond DFT, abinitio methods, such as Hartee-Fock and
post-Hartee-Fock methods, offer higher levels of accuracy
by accounting for electron correlation effects [99]. These
methods are particularly useful for understanding complex
molecular systems and reaction mechanisms.
Incorporating quantum computational methods into
molecular structure prediction research requires a solid foun-
dation in quantum mechanics, access to quantum chemistry
software, and an understanding of the specific algorithms
and methods relevant to the research goals [100]. As the
field advances, researchers can leverage these methods to
achieve more accurate and detailed insights into the behavior
of molecules, opening up new avenues for discovery and
innovation.
Quantum computers have the potential to complement
Density Functional Theory (DFT) and ab initio methods in
various ways, similar to their potential benefits in molecular
structure prediction. DFT and ab initio methods are powerful
tools for simulating the electronic structure of molecules,
but they can be computationally intensive, especially for
large and complex systems. Quantum computers can per-
form certain quantum simulations much faster than classical
computers. This speedup can be particularly beneficial when
dealing with large molecules or complex chemical reactions.
Quantum computers can provide rapid solutions to elec-
tronic structure problems that would be impractical to solve
with classical ab initio methods. Quantum computers can
potentially provide higher levels of accuracy by simulating
quantum effects and electron correlation more precisely. This
can lead to more reliable predictions of molecular properties
and behaviors, including bond dissociation energies, reaction
mechanisms, and spectroscopic properties.
C. POTENTIAL ENERGY SURFACES AND REACTION
PATHWAYS
Potential energy surfaces (PES) are essential for under-
standing molecular behavior, chemical reactions, reaction
pathways, and equilibrium structures [101]. PES maps the
relationship between a molecule’s potential energy and its
atomic coordinates, aiding in studying molecular properties
and reaction pathways [102]. We can observe all of the major
chemical properties mapped by PES in Fig. 5. Recent ad-
vances in QC have significantly improved PES calculations,
providing a powerful tool for drug discovery.
Molecule
Molecular
Properties
Reaction Pathway
Equilibrium
Structures
Molecular
Interactions
Bond Strengths
Potential Energy
FIGURE 5: Potential Energy Surfaces
QC’s computational capabilities have the potential to en-
hance the accuracy of PES calculations, thereby improving
the precision of drug development [103]. Additionally, it
helps identify transition states on PES, which is crucial for
determining reaction rates and mechanisms. This quantum-
driven precision accelerates drug optimization, synthetic
route design, and complex chemical process comprehension,
with applications spanning materials science, catalysis, and
environmental chemistry. QC stands as a transformative force
in modern drug discovery.
For research purposes, studying potential energy surfaces
and reaction pathways requires the application of quantum
chemistry methods, computational algorithms, and visualiza-
tion tools [104]. Researchers delve into the intricate details
VOLUME X, 2020 9
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
of molecular energetics and dynamics to uncover the under-
lying mechanisms driving chemical transformations. These
insights have far-reaching implications for drug discovery,
materials science, catalysis, and environmental chemistry.
VI. COMPLETE PIPELINE FOR DRUG DEVELOPMENT
USING QUANTUM COMPUTING
The drug development pipeline, as illustrated in Fig. 6,
serves as a comprehensive roadmap for understanding the
intricacies of the entire process. Initiated by the pivotal phase
of target identification and characterization, this journey uses
quantum algorithms and simulations to unravel the com-
plexities of biomolecular systems˜
citefl1. This foundational
step provides a molecular-level comprehension of disease
mechanisms, setting the stage for subsequent stages in the
pipeline.
Advancing from target identification, the workflow tran-
sitions to hit search, where the integration of quantum-
enhanced algorithms expedites the virtual screening of chem-
ical libraries [105]. This accelerated process efficiently iden-
tifies potential drug candidates, establishing a solid founda-
tion for the subsequent stages.
Building on the identified hits, the next critical phase is
lead search and optimization. Here, quantum simulations and
algorithms play a central role, in predicting molecular prop-
erties and guiding an iterative optimization process [106].
This iterative refinement aims to enhance binding affinity and
reduce toxicity, laying the groundwork for the ensuing stages.
The pipeline further branches into the realm of Computer-
Aided Drug Design (CADD), where the contrast between
structure-based and ligand-based approaches becomes appar-
ent [107]. In the context of structure-based methodologies,
quantum algorithms come to the forefront, predicting three-
dimensional structures and interactions of molecules with
target proteins. Quantum technologies, exemplified by the
VQE, contribute significantly to refining the accuracy of
these predictions [108].
Simultaneously, ligand-based approaches within CADD
leverage quantum algorithms to analyze existing drugs and
predict the binding affinities of lead compounds [109]. The
integration of quantum machine learning into these ap-
proaches refines the comprehension of structure-activity re-
lationships, providing valuable insights for decision-making
in drug development.
Fig. 6serves as a visual guide, highlighting key
quantum technologies pivotal for advancing drug discov-
ery. NISQ (Noisy Intermediate-Scale Quantum) comput-
ing, Fault-Tolerant QC (FTQC), VQE in Quantum Me-
chanics/Molecular Mechanics (VQE in QM/MM), Quantum
Phase Estimation in Quantum Mechanics/Molecular Me-
chanics (PEA in QM/MM), and hybrid classical schemes
for both protein folding and machine learning are empha-
sized [110]. These technologies collectively represent a trans-
formative leap in drug discovery, ushering in a new era of
possibilities by harnessing the power of QC. The integration
of quantum machine learning techniques further augments
this transformative potential, offering enhanced insights into
complex relationships within large datasets and thereby
contributing to more informed decision-making [111]. In
essence, this holistic workflow, enriched by key quantum
technologies, underlines a paradigm shift in drug discovery,
showcasing the immense potential of QC to revolutionize
the field and expedite the development of novel therapeutic
agents.
A. REAL LIFE IMPLICATIONS OF QUANTUM
COMPUTERS FOR DRUG DISCOVERY PIPELINE
The discourse surrounding the "quantum revolution" in drug
discovery evokes visions of futuristic laboratories dominated
by enigmatic quantum computers. However, the present real-
ity unfolds in a more nuanced yet equally promising manner.
Although comprehensive drug discovery endeavors solely
propelled by the enigmatic powers of QC remain unrealized,
the burgeoning field is affecting tangible advancements. QC
is actively contributing to specific pivotal stages within the
conventional drug development pipeline [112].
Driving this progress are strategic collaborations between
industry leaders and avant-garde QC entities. A notable
instance is the collaboration between Boehringer Ingel-
heim [113] and Rigetti Computing [114], yielding a remark-
able 20-fold enhancement in the solubility of an existing
drug molecule—an impediment frequently encountered in
formulation and delivery. Similarly, the collaboration be-
tween Exscientia and Sumitomo Dainippon Pharma utilizes
quantum simulations to identify superior materials for drug
delivery systems, showcasing advancements beyond tradi-
tional materials.
QC’s impact extends beyond materials and delivery as-
pects. Merck’s [115] partnership with Zapata Comput-
ing [116] focuses on the intricate dynamics of protein-
ligand interactions—the cornerstone of drug action. Quan-
tum simulations achieved a noteworthy 2x acceleration in
simulating these interactions, potentially expediting drug dis-
covery pipelines substantially. Additionally, Vertex Pharma-
ceuticals [117] and QuantumScape [118] are pioneering the
utilization of quantum simulations to design novel antibiotics
targeting specific bacterial vulnerabilities. Although in its
nascent stages, this collaboration holds promise for discov-
ering antibiotics crucial in addressing the escalating threat of
antimicrobial resistance.
Moreover, Quantum technologies, including quantum
computers and simulators, are recognized for their potential
transformative impacts across various sectors, with a particu-
lar emphasis on applications in the life sciences. These tech-
nologies are already making significant progress in drug de-
velopment, the simulation of chemical processes, and genetic
and genomic sequencing [119]. Collaborations such as the
one between AstraZeneca and PsiQuantum concentrate on
leveraging quantum algorithms to augment the performance
of AI models employed in drug discovery. This collaboration
has resulted in a commendable 10% increase in the accuracy
10 VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
FIGURE 6: Pipeline for Drug Development using Quantum Techniques.
of predicting drug-protein interactions, potentially optimiz-
ing candidate selection and development pathways.
While these instances do not yet represent fully quantum-
driven drug discovery pipelines, they underscore the transfor-
mative potential of this nascent technology. From optimizing
existing drugs to identifying novel delivery materials, and
from deciphering protein-ligand interactions to designing
antibiotics, QC’s impact on specific yet pivotal drug devel-
opment stages is undeniable. As QC continues its evolution,
its role in revolutionizing drug discovery is poised to expand,
ultimately culminating in the development of more effective
and innovative medications, delivered with unprecedented
efficiency. This is not merely a futuristic aspiration; it is an
ongoing quantum leap, unfolding step by impactful step [19].
VII. QUANTUM INTEGRATED CLINICAL TRIAL
Clinical trials play a pivotal role in evaluating the safety and
efficacy of new medical interventions like drugs. However,
traditional clinical trial methodologies often face challenges
related to the complexity of data analysis, patient recruit-
ment, trial optimization, and time-to-market for new treat-
ments [120]. QC and quantum technologies offer a unique
perspective to address challenges in areas like drug discovery,
leveraging quantum phenomena such as superposition and
entanglement for accelerated computation and optimization.
These advancements can significantly impact the efficiency
and effectiveness of clinical trials in the pharmaceutical
industry [121].
However, over time, clinical trials have become more
efficient, but they still face various challenges. Designing an
effective clinical drug requires optimization at various and
multiple parameters such as sample size, treatment proto-
cols, and patient selection to ensure statistically meaningful
results [122]. The second major challenge is the analysis
of large, intricate clinical trial datasets, which can be time-
consuming and susceptible to errors. The third major problem
is biomarker identification, which means identifying relevant
biomarkers that predict a patient’s response to a drug, which
is crucial for personalized medicine and targeted therapies.
There are also many more problems like drug target interac-
tion modeling, clinical trial simulation, drug toxicity predic-
tion, drug formulation and delivery optimization challenge,
regulatory compliance and validation, patient recruitment
and stratification, predictive modeling for patient outcomes,
and much more.
With the help of quantum computers, we can easily stream-
line the process because it can easily accelerate complex
optimization tasks, enabling researchers to consider a larger
number of variables simultaneously and to find optimal trial
designs that lead to faster and more reliable outcomes [123].
Quantum computers also possess the ability to handle mas-
sive datasets and perform complex calculations that could
speed up data analysis, helping researchers uncover subtle
patterns and correlations that might be missed using classical
methods. Not only that, but QML algorithms could enhance
biomarker identification by analyzing intricate molecular in-
teractions and patient data, leading to the discovery of more
accurate and predictive biomarkers [124]. Quantum simula-
tions can provide a more detailed understanding of molec-
ular interactions, enabling researchers to design drugs with
higher binding affinity and specificity. Quantum-enhanced
simulations can also offer more precise predictions of drug
interactions, assisting in the refinement of trial designs and
reducing the need for a large number of physical trials.
Quantum computers can also model the quantum behavior
of molecules within various delivery systems, leading to opti-
mized drug formulations that improve efficacy and minimize
side effects. Quantum-enhanced methods would need to be
validated to meet regulatory standards. QC could provide
more accurate and efficient validation processes [125]. QML
VOLUME X, 2020 11
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
TABLE 4: Comparative Analysis between Classical and QC in Drug Discovery
Stage of Drug Development Classical Computing QC
Target Identification and Char-
acterization
Analyze known biological data for targets
based on existing knowledge.
Simulates complex molecular structures
for insights into protein interactions.
Hit and Lead Optimization Molecular dynamics, classical optimiza-
tion algorithms.
Quantum algorithms (e.g., VQE) for com-
plex optimization problems.
Molecular Docking Simulations with limited accuracy due to
computational cost.
Quantum algorithms enhance accuracy by
efficiently exploring multiple configura-
tions simultaneously.
Quantitative Structure-Activity
Relationship (QSAR)
Well-established classical QSAR models. Quantum-enhanced QSAR methods for
more accurate predictions.
Machine Learning in Drug Dis-
covery
Classical machine learning widely used. QML for potential speedup, particularly
with large datasets.
Quantum Chemistry Simula-
tions
Classically computationally intensive.
Struggles with large molecular systems.
Quantum algorithms excel in simulating
molecular structures and electronic config-
urations.
Computational Cost and Scala-
bility
Bottlenecks with high-dimensional param-
eter spaces and large datasets.
Potential exponential speedup in certain
calculations, but constrained by current
hardware and error issues.
algorithms have the potential to revolutionize data analysis
in healthcare by enabling the analysis of diverse patient
data sources to identify potential participants and stratify
them more effectively based on complex patterns [126].
Quantum-enhanced predictive models could incorporate in-
tricate molecular interactions and patient data, leading to
more accurate outcome predictions [110].
VIII. CHALLENGES IN QC FOR DRUG DISCOVERY
Despite holding great promise for development in the field
of drug discovery, there are still several challenges in QC
capabilities for this purpose [127]. The most critical and
significant challenge is scalability and error mitigation.
A. SCALABILITY AND ERROR MITIGATION
Scalability poses a significant challenge, given that quantum
computers demand a substantial number of qubits to model
complex molecular systems [128]. Current quantum devices
typically have limited qubit counts, necessitating substantial
advancements to meet the demands of drug discovery ap-
plications. Furthermore, scaling quantum gate operations is
crucial, as intricate algorithms for molecular simulations in-
volve numerous gate operations, potentially increasing error
rates [129].
Error mitigation is another critical concern in quantum
drug discovery. Quantum computers are susceptible to er-
rors due to decoherence and gate imperfections, impact-
ing simulation accuracy [130]. Developing error-correction
techniques and error-robust quantum algorithms is vital for
dependable quantum simulations. Lowering error rates in
quantum hardware is also a pressing issue, as existing devices
often exhibit error rates far above what is acceptable for
precise drug discovery simulations,
Quantum computers are highly sensitive to environmen-
tal factors, including temperature fluctuations and external
interference, which can introduce noise and errors [131].
Thus, creating controlled environments for QC is essential to
mitigate these influences. Adapting classical drug discovery
algorithms to the quantum paradigm poses a formidable chal-
lenge, demanding the development of quantum algorithms
tailored for real-world drug problems.
B. HARDWARE AND SOFTWARE CONSTRAINTS
Hardware and software constraints are pivotal factors in-
fluencing the integration of QC into drug discovery. On
the hardware front, the limitations regarding qubit count
and connectivity present formidable challenges [132]. Drug
discovery often involves the intricate modeling of complex
molecular systems, necessitating many qubits and intricate
qubit connections [133]. Unfortunately, contemporary quan-
tum devices generally feature a restricted number of qubits
and connectivity, constraining their capacity to simulate large
molecules accurately. Furthermore, it is crucial to acknowl-
edge that the error rates inherent in quantum hardware,
arising from challenges such as decoherence, gate imper-
fections, and readout errors, play a substantial role in the
context of drug discovery [134]. In drug discovery processes,
where precision and accuracy are paramount, addressing and
mitigating these error sources becomes a critical focus [135].
The reliability of quantum gates and the coherence times of
qubits represent further hardware constraints. Quantum gates
must exhibit high fidelity and stability, yet current hardware
often struggles to meet these stringent requirements [136].
Coherence times determine the duration of a qubit as it
can maintain its quantum state without errors, impacting
the feasibility of conducting complex drug discovery simu-
lations. Quantum volume, a comprehensive metric encom-
passing qubit count, gate fidelity, and connectivity, offers
12 VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
TABLE 5: Scope of quantum computers in clinical trials
Challenge Current Approach Potential Quantum Solution
Drug target interaction modeling Approximate mod-
els
Improved understanding of drug design
Clinical trial simulation Extensive physical
trials
Reduction in physical trials through quantum
insights
Drug toxicity prediction Inaccurate
predictions
Enhanced predictive modeling for better drug toxicity prediction
Drug formulation and delivery
optimisation Trial and error Optimised drug formulations
Regulatory compliance and validation Time-consuming More accurate and efficient validation processes
Patient recruitment and stratification Ineffective patient
selection
Effective participant identification and stratification
Predictive modelling for patient
outcomes Limited accuracy More accurate drug-human body interaction outcome prediction
insight into a quantum computer’s overall computational ca-
pability [137]. Shortcomings in quantum volume can restrict
the networks and complexity of drug discovery simulations
achievable through QC.
Current Challenges of quantum computing
Hybrid Approaches
Error mitigation Hardware Constraints
Software Constraints
Overall Expense
Trained Talent
Classical Computers
Error Correction
Quantum Computers
Quantum Properties
FIGURE 7: Hybrid Approaches
On the software front, developing quantum algorithms cus-
tomized for drug discovery represents a significant endeavor.
Adapting classical algorithms to the quantum realm requires
a profound grasp of quantum physics and a comprehensive
understanding of the unique challenges intrinsic to drug
discovery. Creating efficient quantum algorithms that harness
the strengths of QC while mitigating its limitations remains a
focal point of ongoing research [138].
Establishing a robust quantum software ecosystem is an-
other software constraint [139]. This ecosystem should en-
compass quantum compilers, programming languages, and
libraries specifically designed for drug discovery tasks. The
absence of mature and user-friendly quantum software tools
can impede the widespread adoption of QC in the field.
C. CRYPTOGRAPHY AND SECURITY
The advent of quantum computers, driven by algorithms like
Grover’s and Shor’s, introduces a formidable challenge to the
security landscape of drug discovery and development sys-
tems, with particular ramifications for safeguarding patient
data, privacy, and sensitive drug-related information [140].
The inherent potential of quantum computers to efficiently
compromise conventional encryption methods, analogous
to their threat to public key cryptographic systems, raises
substantial concerns regarding the vulnerability of critical
data within the pharmaceutical domain. Notably, patient
confidentiality and the integrity of drug data face potential
compromise.
While Quantum Key Distribution (QKD) has emerged
as a suggested quantum-safe alternative, it is not immune
to security issues. Implementation flaws and the looming
specter of advancements in quantum hacking methods pose
risks that could undermine patient confidentiality and the
integrity of drug-related data [141]. The transition towards
quantum-resistant cryptography in the pharmaceutical sec-
tor is a complex and resource-intensive endeavor, com-
pounded by the limited availability of thoroughly evaluated
quantum-resistant algorithms. Current efforts are diligently
directed towards the development and standardization of
robust quantum-resistant standards, necessitating continuous
vigilance to address unforeseen developments in quantum
security [142].
Moreover, the vast repository of drug and patient response
data in the pharmaceutical industry raises significant con-
cerns about data leakage, presenting substantial privacy is-
sues [143]. The sheer volume and sensitivity of this informa-
tion elevate the risk of inadvertent disclosures, underscoring
the need for stringent measures to protect patient privacy and
uphold the integrity of drug-related data. These challenges
prompt a critical examination of the security infrastructure
in drug discovery and development, urging a comprehensive
VOLUME X, 2020 13
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
reassessment of existing practices to mitigate vulnerabilities
effectively.
However, amidst these challenges, it is crucial to recognize
that the realm of quantum technologies also unveils opportu-
nities for innovative cryptographic techniques [144]. While
grappling with security concerns, the field offers prospects
for novel approaches that can enhance the resilience of drug
discovery systems. This emphasises the need for ongoing
research and adaptation to drug discovery and development
security issues’ changing landscape. Future cryptographic
developments must meet the special needs of the pharmaceu-
tical industry to protect sensitive data and promote medical
research.
D. HYBRID APPROACHES: COMBINING CLASSICAL
AND QC
Integrating QC with classical computing in a seamless,
effective manner—called hybrid quantum-classical integra-
tion—is a complex challenge [145]. Optimizing resource
utilization is paramount, given the constraints of qubit count
and gate operations on quantum hardware. In summary, hard-
ware and software constraints represent substantial hurdles in
harnessing QC’s potential to accelerate drug discovery [146].
Addressing these constraints is essential to fully unlock the
transformative power of QC in this critical field of research.
But, as quantum hardware advances, the hybrid approach
remains scalable, adapting to incorporate more quantum
processing as quantum devices become more capable [147].
The development of hybrid approaches becomes more pivotal
because it capitalizes on several advantages. It maximizes the
strengths of classical and QC, allowing each to excel in tasks
where they are most proficient. Classical computers pro-
vide robust error correction and can handle well-understood
computations, while quantum computers tackle complex,
quantum-specific aspects of drug discovery. Moreover, this
approach optimizes the utilization of quantum resources,
which are often constrained, by incorporating quantum pro-
cessing selectively within a larger classical workflow [148].
IX. FUTURE PROSPECTS AND IMPLICATIONS
As we stand on the edge of a rapidly changing future,
new technologies are reshaping the way we live and work.
This transformation brings both exciting possibilities and
significant challenges. Navigating this evolving landscape
requires a clear understanding of the forces that are shaping
our societies and economies.
A. REVOLUTIONIZING DRUG DEVELOPMENT
THROUGH QC
QC stands at the forefront of revolutionizing drug develop-
ment pipelines, presenting unprecedented opportunities for
innovation and efficiency [149]. This paradigm shift in com-
putational processes holds immense potential to accelerate
drug discovery by simulating intricate molecular interactions
and complex chemical reactions with unparalleled speed and
precision. The speed and power of QC offer the potential
to expedite drug discovery processes significantly. Through
rapid and precise molecular modeling, it enables a deeper
understanding of disease mechanisms and drug interactions
at the quantum level. Moreover, it streamlines drug re-
purposing efforts by efficiently analyzing existing databases,
potentially saving valuable time and resources [150].
B. HOLISTIC INTEGRATION APPROACH:
The integration of QC into pharmaceutical companies is
expected to follow a holistic approach, encompassing strate-
gic partnerships, collaborations, and workforce develop-
ment [151]. In order to seamlessly infuse QC capabilities
into drug development pipelines, companies are likely to
engage in strategic partnerships and collaborations with QC
firms or research institutions. Simultaneously, investments in
QC infrastructure or the utilization of cloud-based quantum
resources may be explored to enhance competitiveness. An
integral part of this integration strategy involves the ac-
quisition of QC experts and data scientists. These skilled
professionals will play a crucial role in bridging the gap
between quantum technologies and pharmaceutical research,
ensuring the effective utilization of these powerful tools. This
holistic approach aims to position pharmaceutical companies
at the forefront of QC advancements in the context of drug
development.
C. ETHICAL CONSIDERATIONS IN QC FOR DRUG
DISCOVERY:
As QC reshapes the pharmaceutical landscape, ethical con-
siderations and responsible use become paramount. Concerns
regarding data security and privacy are heightened, necessi-
tating a renewed focus to safeguard sensitive patient infor-
mation and intellectual property [152]. When QC converges
with artificial intelligence (AI) for drug discovery, additional
ethical concerns arise, including those related to AI bias,
transparency, and accountability [153]. Ensuring unbiased
and safe outcomes is essential for maintaining the integrity
of the drug development process. Ethical considerations also
extend to accessibility and equity, demanding that the bene-
fits of quantum-powered drug development reach undeserved
communities and diverse patient populations. Striking a bal-
ance between transformative potential and ethical responsi-
bility is key to realizing the full benefits of QC.
D. REGULATORY COMPLIANCE:
Finally, regulatory compliance remains essential [154]. Phar-
maceutical companies must navigate and adhere to evolving
regulations governing the ethical use of QC in drug devel-
opment. Adherence to these regulations ensures ethical stan-
dards are maintained throughout the transformative journey
of QC in drug discovery.
14 VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Future Applications Related To Drug Discovery
New Material With Specific
Characteristics
Understanding Chemical
Reaction
Studying Molecular
Properties
Understanding Reaction
Mechanism
FIGURE 8: Future applications of QC
X. CONCLUSION
This study investigated the disruptive potential of QC in the
field of drug development, as well as its applications and
future prospects. QC has improved pharmaceutical CADD,
chemical simulations, and clinical trial simulations. The tech-
nology’s capacity to accurately and rapidly replicate intricate
chemical reactions has brought about a transformative impact
on drug research. The system performs complex calculations
and analyses large datasets to enhance the efficiency of
clinical trials.
To effectively characterize complex chemical processes,
it is essential to have scalability, error mitigation, and a
sufficient number of qubits. Interdisciplinary collaboration
is necessary for the application of quantum computers in
pharmaceutical research, as it enables a comprehensive un-
derstanding of both quantum physics and pharmaceutical
processes.
Future research goals include the development of quantum
algorithms for drug discovery, quantum hardware for com-
plex simulations, and hybrid classical-quantum models for
resource optimization. Ethics, particularly concerning data
security and patient privacy, are also significant.
QC has the potential to enhance simulations and data
processing, leading to accelerated drug discovery and im-
proved treatment effectiveness. To harness this potential, it
is imperative to conduct research focused on technology and
its applications. The industry cannot overlook the significant
potential of QC, despite the obstacles it presents.
REFERENCES
[1] N.Berdigaliyev and M.Aljofan, “"an overview of drug discovery and
development",” vol. 12, no. 10, 2020, pp. 939–947.
[2] S. Magazine, “Why did it take 35 years to get a
malaria vaccine?” Smithsonian.com, Jun 2022. [Online]. Avail-
able: https://www.smithsonianmag.com/science-nature/why-did-it-take-
35-years-to-get-a-malaria- vaccine-180980151/
[3] P.-H. Wang, J.-H. Chen, Y.-Y. Yang, C. Lee, and Y. J. Tseng, “Recent
advances in quantum computing for drug discovery and development,”
IEEE Nanotechnology Magazine, vol. 17, no. 2, pp. 26–30, 2023.
[4] A. Blanco-Gonzalez, A. Cabezon, A. Seco-Gonzalez, D. Conde-Torres,
P. Antelo-Riveiro, A. Pineiro, and R. Garcia-Fandino, “The role of ai in
drug discovery: challenges, opportunities, and strategies,” Pharmaceuti-
cals, vol. 16, no. 6, p. 891, 2023.
[5] F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends,
R. Biswas, S. Boixo, F. G. S. L. Brandao, D. A. Buell et al., “Quantum
supremacy using a programmable superconducting processor,” Nature,
vol. 574, no. 7779, pp. 505–510, Oct. 2019.
[6] M. Zinner, F. Dahlhausen, P. Boehme, J. Ehlers, L. Bieske, and
L. Fehring, “Quantum computing’s potential for drug discovery: Early
stage industry dynamics,” Drug Discovery Today, vol. 26, no. 7, pp.
1680–1688, 2021. [Online]. Available: https://www.sciencedirect.com/
science/article/pii/S1359644621002750
[7] V. Mahesh and S. Shijo, “Accelerating drug discovery with quantum
computing,” Evolution and Applications of Quantum Computing, pp.
175–181, 2023.
[8] R. Sotelo, “Quantum in consumer technology,” IEEE Consumer Elec-
tronics Magazine, pp. 1–3, 2023.
[9] R. Biswas, A. Basu, A. Nandy, A. Deb, K. Haque, and D. Chanda, “Drug
discovery and drug identification using ai,” pp. 49–51, 2020.
[10] M. D. Parenti and G. Rastelli, “Advances and applications of binding
affinity prediction methods in drug discovery,” Biotechnology advances,
vol. 30, no. 1, pp. 244–250, 2012.
[11] J. Li, R. O. Topaloglu, and S. Ghosh, “Quantum generative models
for small molecule drug discovery,” IEEE Transactions on Quantum
Engineering, vol. 2, pp. 1–8, 2021.
[12] V. Chauhan, S. Negi, D. Jain, P. Singh, A. K. Sagar, and A. K. Sharma,
“Quantum computers: A review on how quantum computing can boom
ai,” in 2022 2nd International Conference on Advance Computing and
Innovative Technologies in Engineering (ICACITE), 2022, pp. 559–563.
[13] S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin, and X. Yuan,
“Quantum computational chemistry,” Rev. Mod. Phys., vol. 92, p.
015003, Mar 2020. [Online]. Available: https://link.aps.org/doi/10.1103/
RevModPhys.92.015003
[14] S. Duela, A. S.Rameshkumar, P. R, P. Umapathy, and K. Raja, “Quantum
assisted genetic algorithm for sequencing compatible amino acids in drug
design,” 01 2023, pp. 1–7.
[15] B. Lau, P. S. Emani, J. Chapman, L. Yao, T. Lam, P. Merrill, J. Warrell,
M. B. Gerstein, and H. Y. K. Lam, “Insights from incorporating quantum
computing into drug design workflows,” Bioinformatics, vol. 39, no. 1, p.
btac789, 12 2022.
[16] H. Mustafa, S. N. Morapakula, P. Jain, and S. Ganguly, “Variational
quantum algorithms for chemical simulation and drug discovery,” pp. 1–
8, 2022.
[17] E. National Academies of Sciences, Medicine et al., “Quantum comput-
ing: progress and prospects,” 2019.
[18] O. Dupouët, Y. Pitarch, M. Ferru, and B. Bernela, “Community dynamics
and knowledge production: forty years of research in quantum comput-
ing,” Journal of Knowledge Management, 2023.
[19] Y. Cao, J. Romero, and A. Aspuru-Guzik, “Potential of quantum com-
puting for drug discovery,” IBM Journal of Research and Development,
vol. 62, no. 6, pp. 6:1–6:20, 2018.
[20] J. Singh and M. Singh, “Evolution in quantum computing,” pp. 267–270,
2016.
[21] S. K. Sood and Pooja, “Quantum computing review: A decade of re-
search,” IEEE Transactions on Engineering Management, pp. 1–15, 2023.
[22] E. h. Shaik and N. Rangaswamy, “Implementation of quantum gates
based logic circuits using ibm qiskit,” pp. 1–6, 2020.
[23] J. Bub, “Quantum Entanglement and Information,” Aug. 2001, [Online;
accessed 17. Aug. 2023]. [Online]. Available: https://plato.stanford.edu/
entries/qt-entangle
[24] A. Zeilinger, “Quantum entanglement and information,” pp. 163–, 2000.
[25] S. Gamble, “Quantum computing: What it is, why we want it, and how
we’re trying to get it,” 2019.
[26] T. S. Humble, H. Thapliyal, E. Muñoz-Coreas, F. A. Mohiyaddin, and
R. S. Bennink, “Quantum computing circuits and devices,” IEEE Design
& Test, vol. 36, no. 3, pp. 69–94, 2019.
[27] M. Möttönen1and J. J. Vartiainen, “Decompositions of general quantum
gates,” Trends in Quantum Computing Research, p. 149, 2006.
[28] M. Swathi and B. Rudra, “Implementation of reversible logic gates with
quantum gates,” pp. 1557–1563, 2021.
[29] P. K. Roy, “Quantum logic gates,” 08 2020.
VOLUME X, 2020 15
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
[30] L. Banchi, N. Pancotti, and S. Bose, “Quantum gate learning in qubit
networks: Toffoli gate without time-dependent control,” npj Quantum
Inf., vol. 2, no. 16019, pp. 1–6, Jul. 2016.
[31] H. Ma, M. Govoni, and G. Galli, “Quantum simulations of materials on
near-term quantum computers,” npj Comput. Mater., vol. 6, no. 85, pp.
1–8, Jul. 2020.
[32] Y. Shi, S. T. Lam, and T. L. Beck, “Deep neural network based quantum
simulations and quasichemical theory for accurate modeling of molten
salt thermodynamics,” Chemical Science, vol. 13, no. 28, pp. 8265–8273,
2022.
[33] M. Reiher, N. Wiebe, K. M. Svore, D. Wecker, and M. Troyer, “Eluci-
dating reaction mechanisms on quantum computers,” Proceedings of the
national academy of sciences, vol. 114, no. 29, pp. 7555–7560, 2017.
[34] D. Prada-Gracia, S. Huerta-Yépez, and L. M. Moreno-Vargas, “Applica-
tion of computational methods for anticancer drug discovery, design, and
optimization,” Boletín Médico Del Hospital Infantil de México (English
Edition), vol. 73, no. 6, pp. 411–423, 2016.
[35] X. Lin, X. Li, and X. Lin, “A review on applications of computational
methods in drug screening and design,” Molecules, vol. 25, no. 6, p. 1375,
2020.
[36] D. Solenov, J. Brieler, and J. F. Scherrer, “The potential of quantum
computing and machine learning to advance clinical research and change
the practice of medicine,” Missouri medicine, vol. 115, no. 5, p. 463,
2018.
[37] W. L. Jorgensen, “Efficient drug lead discovery and optimization,” pp.
724–733, 2009.
[38] F. Gentile, J. C. Yaacoub, J. Gleave, M. Fernandez, A.-T. Ton, F. Ban,
A. Stern, and A. Cherkasov, “Artificial intelligence–enabled virtual
screening of ultra-large chemical libraries with deep docking,” Nat.
Protoc., vol. 17, pp. 672–697, Mar. 2022.
[39] A. Veselovsky and A. Ivanov, “Strategy of computer-aided drug design,”
Current Drug Targets-Infectious Disorders, vol. 3, no. 1, pp. 33–40, 2003.
[40] W. Yu and A. D. MacKerell, “Computer-aided drug design methods,”
Antibiotics: methods and protocols, pp. 85–106, 2017.
[41] G. Madhavi Sastry, M. Adzhigirey, T. Day, R. Annabhimoju, and
W. Sherman, “Protein and ligand preparation: parameters, protocols, and
influence on virtual screening enrichments,” pp. 221–234, 2013.
[42] K. K. Chaudhary and N. Mishra, “A review on molecular docking: novel
tool for drug discovery,” Databases, vol. 3, no. 4, p. 1029, 2016.
[43] A. Axenopoulos, P. Daras, G. Papadopoulos, and E. Houstis, “A shape
descriptor for fast complementarity matching in molecular docking,”
IEEE/ACM Transactions on Computational Biology and Bioinformatics,
vol. 8, no. 6, pp. 1441–1457, 2011.
[44] I. Halperin, B. Ma, H. Wolfson, and R. Nussinov, “Principles of docking:
An overview of search algorithms and a guide to scoring functions,”
Proteins: Structure, Function, and Bioinformatics, vol. 47, no. 4, pp. 409–
443, 2002.
[45] D. B. Kitchen, H. Decornez, J. R. Furr, and J. Bajorath, “Docking and
scoring in virtual screening for drug discovery: methods and applica-
tions,” Nature reviews Drug discovery, vol. 3, no. 11, pp. 935–949, 2004.
[46] Y. Fang, “Ligand–receptor interaction platforms and their applications
for drug discovery,” Expert opinion on drug discovery, vol. 7, no. 10, pp.
969–988, 2012.
[47] J. Vaidyanathan, T. Vaidyanathan, P. Yadav, and C. Linaras, “Collagen–
ligand interaction in dentinal adhesion: Computer visualization and anal-
ysis,” Biomaterials, vol. 22, no. 21, pp. 2911–2920, 2001.
[48] V. Rusinov, V. Baev, I. N. Minkov, and M. Tabler, “Microinspector: a
web tool for detection of mirna binding sites in an rna sequence,” Nucleic
acids research, vol. 33, no. suppl_2, pp. W696–W700, 2005.
[49] D. E. Bernal, A. Ajagekar, S. M. Harwood, S. T. Stober, D. Trenev, and
F. You, “Perspectives of quantum computing for chemical engineering,”
AIChE Journal, vol. 68, no. 6, p. e17651, 2022.
[50] S.-Y. Huang and X. Zou, “Advances and Challenges in Protein-Ligand
Docking,” Int. J. Mol. Sci., vol. 11, no. 8, p. 3016, 2010.
[51] A. Pyrkov, A. Aliper, D. Bezrukov, Y.-C. Lin, D. Polykovskiy,
P. Kamya, F. Ren, and A. Zhavoronkov, “Quantum computing for
near-term applications in generative chemistry and drug discovery,” Drug
Discovery Today, vol. 28, no. 8, p. 103675, 2023. [Online]. Available:
https://www.sciencedirect.com/science/article/pii/S1359644623001915
[52] D. Gioia, M. Bertazzo, M. Recanatini, M. Masetti, and A. Cavalli,
“Dynamic docking: a paradigm shift in computational drug discovery,”
Molecules, vol. 22, no. 11, p. 2029, 2017.
[53] I. Kassal, J. D. Whitfield, A. Perdomo-Ortiz, M.-H. Yung, and A. Aspuru-
Guzik, “Simulating chemistry using quantum computers,” Annual review
of physical chemistry, vol. 62, pp. 185–207, 2011.
[54] P. H. M. Torres, A. C. R. Sodero, P. Jofily, and F. P. Silva-Jr, “Key Topics
in Molecular Docking for Drug Design,” Int. J. Mol. Sci., vol. 20, no. 18,
Sep. 2019.
[55] V. Kulkarni, “Quantum Parallelism - Where Quantum Computers Get
Their Mojo From |Towards Data Science,” Medium, Dec. 2021. [On-
line]. Available: https://towardsdatascience.com/quantum-parallelism-
where-quantum-computers-get-their-mojo-from-66c93bd09855
[56] M. Nabil, “Quantum Parallelism - Madali Nabil - Medium,” Feb. 2023.
[Online]. Available: https://medium.com/@madali.nabil97/quantum-
parallelism-e4b0d2dfa744
[57] “The power of quantum computing: Parallelism,” ScIU, Jan. 2024,
[Online; accessed 26. Jan. 2024]. [Online]. Available: https://blogs.iu.
edu/sciu/2019/07/13/quantum-computing-parallelism
[58] P. H. M. Torres, A. C. R. Sodero, P. Jofily, and F. P. Silva-Jr, “Key Topics
in Molecular Docking for Drug Design,” Int. J. Mol. Sci., vol. 20, no. 18,
Sep. 2019.
[59] O. A. Arodola and M. E. S. Soliman, “Quantum mechanics implemen-
tation in drug-design workflows: does it really help?” Drug Design,
Development and Therapy, vol. 11, p. 2551, 2017.
[60] “Towards using quantum computing to speed up drug
development |Imperial News |Imperial College London,”
Imperial News, Oct. 2023, [Online; accessed 26. Jan. 2024].
[Online]. Available: https://www.imperial.ac.uk/news/248638/towards-
using-quantum-computing-speed-drug
[61] S. Mensa, E. Sahin, F. Tacchino, P. Kl Barkoutsos, and I. Tavernelli,
“Quantum machine learning framework for virtual screening in drug dis-
covery: a prospective quantum advantage,” Machine Learning: Science
and Technology, vol. 4, no. 1, p. 015023, 2023.
[62] C. Gorgulla, A. Jayaraj, K. Fackeldey, and H. Arthanari, “Emerging
frontiers in virtual drug discovery: From quantum mechanical methods
to deep learning approaches,” Current Opinion in Chemical Biology,
vol. 69, p. 102156, 2022.
[63] J. Pérez-Sianes, H. Pérez-Sánchez, and F. Díaz, “Virtual screening: a
challenge for deep learning,” pp. 13–22, 2016.
[64] I. M. Georgescu, S. Ashhab, and F. Nori, “Quantum simulation,” Reviews
of Modern Physics, vol. 86, no. 1, p. 153, 2014.
[65] H. Yano, Y. Suzuki, K. M. Itoh, R. Raymond, and N. Yamamoto,
“Efficient discrete feature encoding for variational quantum classifier,”
IEEE Transactions on Quantum Engineering, vol. 2, pp. 1–14, 2021.
[66] S. Jeswal and S. Chakraverty, “Recent developments and applications in
quantum neural network: A review,” Archives of Computational Methods
in Engineering, vol. 26, pp. 793–807, 2019.
[67] S. Mensa, E. Sahin, F. Tacchino, P. Kl Barkoutsos, and I. Tavernelli,
“Quantum machine learning framework for virtual screening in drug dis-
covery: a prospective quantum advantage,” Machine Learning: Science
and Technology, vol. 4, no. 1, p. 015023, 2023.
[68] J. A. Jones, M. Mosca, and R. H. Hansen, “Implementation of a quantum
search algorithm on a quantum computer,” Nature, vol. 393, no. 6683, pp.
344–346, 1998.
[69] M. Evers, A. Heid, and I. Ostojic, “Pharma’s digital Rx:
Quantum computing in drug research and development,”
McKinsey & Company, Jun. 2021. [Online]. Available: https:
//www.mckinsey.com/industries/life- sciences/our-insights/pharmas-
digital-rx-quantum-computing-in-drug- research-and- development
[70] Y. Fan, “A quantum algorithm for molecular dynamics simulation,” arXiv
preprint ArXiv:0901.4163, 2009.
[71] X.-s. Ma, B. Dakic, W. Naylor, A. Zeilinger, and P. Walther, “Quantum
simulation of the wavefunction to probe frustrated heisenberg spin sys-
tems,” Nature Physics, vol. 7, no. 5, pp. 399–405, 2011.
[72] U. Dorner, R. Demkowicz-Dobrzanski, B. J. Smith, J. S. Lundeen,
W. Wasilewski, K. Banaszek, and I. A. Walmsley, “Optimal quantum
phase estimation,” Physical review letters, vol. 102, no. 4, p. 040403,
2009.
[73] Q.-X. Xie, W.-g. Zhang, X.-S. Xu, S. Liu, and Y. Zhao, “Qubit unitary
coupled cluster with generalized single and paired double excitations
ansatz for variational quantum eigensolver,” Int. J. Quantum Chem., vol.
122, no. 24, p. e27001, Dec. 2022.
[74] N. Tkachenko, J. Sud, Y. Zhang, S. Tretiak, P. Anisimov, A. Arrasmith,
P. Coles, L. Cincio, and P. Dub, “Correlation-informed permutation of
qubits for reducing ansatz depth in vqe,” 09 2020.
16 VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
[75] A. G. Rattew, S. Hu, M. Pistoia, R. Chen, and S. Wood, “A Domain-
agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational
Quantum Eigensolver,” arXiv, Oct. 2019.
[76] D. M. Ceperley, “An overview of quantum monte carlo methods,” The-
oretical and computational methods in mineral physics, pp. 129–136,
2018.
[77] Q.-X. Xie, S. Liu, and Y. Zhao, “Orthogonal state reduction variational
eigensolver for the excited-state calculations on quantum computers,”
Journal of Chemical Theory and Computation, vol. 18, no. 6, pp. 3737–
3746, 2022.
[78] T. Zhou, D. Huang, and A. Caflisch, “Quantum mechanical methods for
drug design,” Current topics in medicinal chemistry, vol. 10, no. 1, pp.
33–45, 2010.
[79] M. Manathunga, A. W. Götz, and K. M. Merz Jr, “Computer-aided drug
design, quantum-mechanical methods for biological problems,” Current
Opinion in Structural Biology, vol. 75, p. 102417, 2022.
[80] D. R. Bowler and T. Miyazaki, “Methods in electronic structure calcula-
tions,” Reports on Progress in Physics, vol. 75, no. 3, p. 036503, 2012.
[81] R. Guareschi, I. Lukac, I. H. Gilbert, and F. Zuccotto, “Sophosqm:
Accurate binding affinity prediction in compound optimization,” ACS
omega, vol. 8, no. 17, pp. 15083–15 098, 2023.
[82] V. L. Schramm, “Transition states, analogues, and drug development,”
ACS chemical biology, vol. 8, no. 1, pp. 71–81, 2013.
[83] M. W. van der Kamp and A. J. Mulholland, “Combined quantum me-
chanics/molecular mechanics (qm/mm) methods in computational enzy-
mology,” Biochemistry, vol. 52, no. 16, pp. 2708–2728, 2013.
[84] C. Reichardt, “Solvents and solvent effects: an introduction,” Organic
process research & development, vol. 11, no. 1, pp. 105–113, 2007.
[85] C. T. Sebens, “Electron charge density: a clue from quantum chemistry
for quantum foundations,” Foundations of Physics, vol. 51, no. 4, p. 75,
2021.
[86] V. Barone, S. Alessandrini, M. Biczysko, J. R. Cheeseman, D. C.
Clary, A. B. McCoy, R. J. DiRisio, F. Neese, M. Melosso, and C. Puz-
zarini, “Computational molecular spectroscopy,” Nature Reviews Meth-
ods Primers, vol. 1, no. 1, p. 38, 2021.
[87] L. M. Mihalovits, G. G. Ferenczy, and G. M. Keser˝u, “The role of
quantum chemistry in covalent inhibitor design,” International Journal
of Quantum Chemistry, vol. 122, no. 8, p. e26768, 2022.
[88] A. Irfan, M. Hussien, M. Y. Mehboob, A. Ahmad, and M. R. S. A. Janjua,
“Learning from fullerenes and predicting for y6: Machine learning and
high-throughput screening of small molecule donors for organic solar
cells,” Energy Technology, vol. 10, no. 6, p. 2101096, 2022.
[89] A. Montanaro, “Quantum algorithms: an overview,” npj Quantum Infor-
mation, vol. 2, no. 1, pp. 1–8, 2016.
[90] A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow,
and J. M. Gambetta, “Hardware-efficient variational quantum eigensolver
for small molecules and quantum magnets,” nature, vol. 549, no. 7671,
pp. 242–246, 2017.
[91] J.-G. Liu, Y.-H. Zhang, Y. Wan, and L. Wang, “Variational quantum
eigensolver with fewer qubits,” Physical Review Research, vol. 1, no. 2,
p. 023025, 2019.
[92] S. McArdle, T. Jones, S. Endo, Y. Li, S. C. Benjamin, and X. Yuan, “Vari-
ational ansatz-based quantum simulation of imaginary time evolution,”
npj Quantum Information, vol. 5, no. 1, p. 75, 2019.
[93] J. Romero, R. Babbush, J. R. McClean, C. Hempel, P. J. Love, and
A. Aspuru-Guzik, “Strategies for quantum computing molecular energies
using the unitary coupled cluster ansatz,” Quantum Science and Technol-
ogy, vol. 4, no. 1, p. 014008, 2018.
[94] G. Nannicini, “Performance of hybrid quantum-classical variational
heuristics for combinatorial optimization,” Physical Review E, vol. 99,
no. 1, p. 013304, 2019.
[95] “GENCI/CEA, FZJ, PASQAL Achieve Major Hybrid Computing
Milestone,” Jan. 2024, [Online; accessed 20. Jan. 2024]. [Online].
Available: https://www.miragenews.com/gencicea-fzj-pasqal-achieve-
major-hybrid-1120388
[96] J. J. McDouall, Computational quantum chemistry: molecular structure
and properties in silico. Royal Society of Chemistry, 2013, no. 5.
[97] M. Orio, D. A. Pantazis, and F. Neese, “Density functional theory,”
Photosynthesis research, vol. 102, pp. 443–453, 2009.
[98] E. Engel, Density functional theory. Springer, 2011.
[99] I. N. Levine, D. H. Busch, and H. Shull, “Quantum chemistry,” vol. 6,
2009.
[100] L. Ahmed, B. Rasulev, M. Turabekova, D. Leszczynska, and J. Leszczyn-
ski, “Receptor-and ligand-based study of fullerene analogues: compre-
hensive computational approach including quantum-chemical, qsar and
molecular docking simulations,” Organic & biomolecular chemistry,
vol. 11, no. 35, pp. 5798–5808, 2013.
[101] A. Teplukhin and D. Babikov, “Visualization of potential energy function
using an isoenergy approach and 3d prototyping,” Journal of Chemical
Education, vol. 92, no. 2, pp. 305–309, 2015.
[102] G. C. Schatz, “The analytical representation of electronic potential-
energy surfaces,” Reviews of Modern Physics, vol. 61, no. 3, p. 669,
1989.
[103] A. Baiardi, M. Christandl, and M. Reiher, “Quantum computing for
molecular biology,” ChemBioChem, vol. 24, no. 13, p. e202300120,
2023.
[104] A. Baiardi, S. A. Grimmel, M. Steiner, P. L. Türtscher, J. P.
Unsleber, T. Weymuth, and M. Reiher, “Expansive quantum mechanical
exploration of chemical reaction paths,” Accounts of Chemical
Research, vol. 55, no. 1, p. 35–43, Dec. 2021. [Online]. Available:
http://dx.doi.org/10.1021/acs.accounts.1c00472
[105] B. Bonde, P. Patil, and B. Choubey, “The future of drug development with
quantum computing,” pp. 153–179, 2023.
[106] G. W. Richings and S. Habershon, “Predicting molecular photochemistry
using machine-learning-enhanced quantum dynamics simulations,” Ac-
counts of Chemical Research, vol. 55, no. 2, pp. 209–220, 2022.
[107] N. Schaduangrat, S. Lampa, S. Simeon, M. P. Gleeson, O. Spjuth, and
C. Nantasenamat, “Towards reproducible computational drug discovery,”
Journal of cheminformatics, vol. 12, pp. 1–30, 2020.
[108] Y. Liu, J. Liu, J. R. Raney, and P. Wang, “Quantum computing for solid
mechanics and structural engineering–a demonstration with variational
quantum eigensolver,” Extreme Mechanics Letters, p. 102117, 2023.
[109] S. K. Niazi and Z. Mariam, “Computer-aided drug design and drug
discovery: A prospective analysis,” Pharmaceuticals, vol. 17, no. 1, p. 22,
2023.
[110] P. Hassanzadeh, “Towards the quantum-enabled technologies for devel-
opment of drugs or delivery systems,” Journal of Controlled Release, vol.
324, pp. 260–279, 2020.
[111] M. Sajjan, J. Li, R. Selvarajan, S. H. Sureshbabu, S. S. Kale, R. Gupta,
V. Singh, and S. Kais, “Quantum machine learning for chemistry and
physics,” Chemical Society Reviews, 2022.
[112] C. Outeiral, M. Strahm, J. Shi, G. M. Morris, S. C. Benjamin, and C. M.
Deane, “The prospects of quantum computing in computational molecu-
lar biology,” Wiley Interdisciplinary Reviews: Computational Molecular
Science, vol. 11, no. 1, p. e1481, 2021.
[113] “Boehringer Ingelheim - Life Forward,” Jan. 2024, [Online; accessed 30.
Jan. 2024]. [Online]. Available: https://www.boehringer-ingelheim.com
[114] “Quantum Computing,” Jan. 2024, [Online; accessed 30. Jan. 2024].
[Online]. Available: https://www.rigetti.com
[115] “Merck |Home,” Jan. 2024, [Online; accessed 31. Jan. 2024]. [Online].
Available: https://www.merck.com
[116] “Home,” Jan. 2024, [Online; accessed 30. Jan. 2024]. [Online].
Available: https://zapata.ai
[117] “Vertex Pharmaceuticals |Home,” Jan. 2024, [Online; accessed 30. Jan.
2024]. [Online]. Available: https://www.vrtx.com
[118] “Building the Best Solid State Battery |QuantumScape,” Oct.
2023, [Online; accessed 30. Jan. 2024]. [Online]. Available: https:
//www.quantumscape.com
[119] S. Gunashekar, “Using quantum computers and simulators in the life
sciences,” 2022.
[120] P. Kouvelis, J. Milner, and Z. Tian, “Clinical trials for new drug devel-
opment: Optimal investment and application,” Manufacturing & Service
Operations Management, vol. 19, no. 3, pp. 437–452, 2017.
[121] F. Bova, A. Goldfarb, and R. G. Melko, “Commercial applications of
quantum computing,” EPJ quantum technology, vol. 8, no. 1, p. 2, 2021.
[122] A. D. Roses, “Pharmacogenetics and drug development: the path to safer
and more effective drugs,” Nature Reviews Genetics, vol. 5, no. 9, pp.
645–656, 2004.
[123] M. Langione, J.-F. Bobier, C. Meier, S. Hasenfuss, and U. Schulze, “Will
quantum computing transform biopharma r&d,” 2021.
[124] K. Dwivedi and A. Nigam, “Quantum deep learning for phoniatrics
biomarker based disease detection deep learning model for disease de-
tection by phoniatrics biomarkers using quantum computation,” pp. 1–8,
2020.
VOLUME X, 2020 17
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
[125] A. S. Bhatia, M. K. Saggi, and S. Kais, “Quantum machine learning
predicting adme-tox properties in drug discovery,” Journal of Chemical
Information and Modeling, 2023.
[126] Y. Zhang and Q. Ni, “Recent advances in quantum machine learning,”
Quantum Engineering, vol. 2, no. 1, p. e34, 2020.
[127] A. Fedorov and M. Gelfand, “Towards practical applications in quantum
computational biology,” Nature Computational Science, vol. 1, no. 2, pp.
114–119, 2021.
[128] P. Lolur, M. Skogh, W. Dobrautz, C. Warren, J. Biznárová, A. Osman,
G. Tancredi, G. Wendin, J. Bylander, and M. Rahm, “Reference-state
error mitigation: A strategy for high accuracy quantum computation of
chemistry,” Journal of Chemical Theory and Computation, vol. 19, no. 3,
pp. 783–789, 2023.
[129] S. Carretta, D. Zueco, A. Chiesa, Á. Gómez-León, and F. Luis, “A
perspective on scaling up quantum computation with molecular spins,”
Applied Physics Letters, vol. 118, no. 24, 2021.
[130] S. Endo, Z. Cai, S. C. Benjamin, and X. Yuan, “Hybrid quantum-classical
algorithms and quantum error mitigation,” Journal of the Physical Society
of Japan, vol. 90, no. 3, p. 032001, 2021.
[131] L.-M. Duan and G.-C. Guo, “Suppressing environmental noise in quan-
tum computation through pulse control,” Physics Letters A, vol. 261, no.
3-4, pp. 139–144, 1999.
[132] G. Li, A. Wu, Y. Shi, A. Javadi-Abhari, Y. Ding, and Y. Xie, “On the
co-design of quantum software and hardware,” pp. 1–7, 2021.
[133] B. Lau, P. S. Emani, J. Chapman, L. Yao, T. Lam, P. Merrill, J. Warrell,
M. B. Gerstein, and H. Y. Lam, “Insights from incorporating quantum
computing into drug design workflows,” Bioinformatics, vol. 39, no. 1, p.
btac789, 2023.
[134] J. M. Baker and F. T. Chong, “Emerging technologies for quantum
computing,” IEEE Micro, vol. 41, no. 5, pp. 41–47, 2021.
[135] J. Singh and K. S. Bhangu, “Contemporary quantum computing use
cases: Taxonomy, review and challenges,” Archives of Computational
Methods in Engineering, vol. 30, no. 1, pp. 615–638, 2023.
[136] N. P. De Leon, K. M. Itoh, D. Kim, K. K. Mehta, T. E. Northup, H. Paik,
B. Palmer, N. Samarth, S. Sangtawesin, and D. W. Steuerman, “Materials
challenges and opportunities for quantum computing hardware,” Science,
vol. 372, no. 6539, p. eabb2823, 2021.
[137] P. Jurcevic, A. Javadi-Abhari, L. S. Bishop, I. Lauer, D. F. Bogorin,
M. Brink, L. Capelluto, O. Günlük, T. Itoko, N. Kanazawa et al.,
“Demonstration of quantum volume 64 on a superconducting quantum
computing system,” Quantum Science and Technology, vol. 6, no. 2, p.
025020, 2021.
[138] E. Knill, R. Laflamme, and G. J. Milburn, “A scheme for efficient
quantum computation with linear optics,” nature, vol. 409, no. 6816, pp.
46–52, 2001.
[139] V. Stirbu, M. Haghparast, M. Waseem, N. Dayama, and T. Mikkonen,
“Full-stack quantum software in practice: Ecosystem, stakeholders and
challenges,” arXiv preprint arXiv:2307.16345, 2023.
[140] C. Ugwuishiwu, U. Orji, C. Ugwu, and C. Asogwa, “An overview of
quantum cryptography and shor’s algorithm,” Int. J. Adv. Trends Comput.
Sci. Eng, vol. 9, no. 5, 2020.
[141] E. Diamanti, H.-K. Lo, B. Qi, and Z. Yuan, “Practical challenges in
quantum key distribution,” npj Quantum Inf., vol. 2, no. 16025, pp. 1–
12, Nov. 2016.
[142] D. Garisto, “What Is the Future of Quantum-Proof Encryption?” IEEE
Spectr., Nov. 2022. [Online]. Available: https://spectrum.ieee.org/post-
quantum-cryptography-nist
[143] U. Tariq, I. Ahmed, A. K. Bashir, and K. Shaukat, “A critical
cybersecurity analysis and future research directions for the internet of
things: A comprehensive review,” Sensors, vol. 23, no. 8, 2023. [Online].
Available: https://www.mdpi.com/1424-8220/23/8/4117
[144] G. Trifirò and S. Crisafulli, “A new era of pharmacovigilance: Future
challenges and opportunities,” Frontiers in Drug Safety and Regulation,
vol. 2, 2022. [Online]. Available: https://www.frontiersin.org/articles/10.
3389/fdsfr.2022.866898
[145] S. Fadli, B. S. Rawal, and A. Mentges, “Hybrid quantum machine
learning using quantum integrated cloud architecture (qica),” pp. 419–
425, 2023.
[146] S. P. Leelananda and S. Lindert, “Computational methods in drug discov-
ery,” Beilstein journal of organic chemistry, vol. 12, no. 1, pp. 2694–2718,
2016.
[147] L. Wang, F. Tang, and H. Wu, “Hybrid genetic algorithm based on quan-
tum computing for numerical optimization and parameter estimation,”
Applied Mathematics and Computation, vol. 171, no. 2, pp. 1141–1156,
2005.
[148] M. Perelshtein, A. Sagingalieva, K. Pinto, V. Shete, A. Pakhomchik,
A. Melnikov, F. Neukart, G. Gesek, A. Melnikov, and V. Vinokur, “Prac-
tical application-specific advantage through hybrid quantum computing,”
arXiv preprint arXiv:2205.04858, 2022.
[149] N. Jyothi Ahuja and S. Dutt, “Implications of quantum science on
industry 4.0: Challenges and opportunities,” Quantum and Blockchain
for Modern Computing Systems: Vision and Advancements: Quantum
and Blockchain Technologies: Current Trends and Challenges, pp. 183–
204, 2022.
[150] J. Kietzmann, D. S. Demetis, T. Eriksson, and A. Dabirian, “Hello quan-
tum! how quantum computing will change the world,” IT Professional,
vol. 23, no. 4, pp. 106–111, 2021.
[151] R. De Wolf, “The potential impact of quantum computers on society,”
Ethics and Information Technology, vol. 19, pp. 271–276, 2017.
[152] P. B. Upama, M. J. H. Faruk, M. Nazim, M. Masum, H. Shahriar,
G. Uddin, S. Barzanjeh, S. I. Ahamed, and A. Rahman, “Evolution
of quantum computing: A systematic survey on the use of quantum
computing tools,” pp. 520–529, 2022.
[153] J. Davids, N. Lidströmer, and H. Ashrafian, “Artificial intelligence in
medicine using quantum computing in the future of healthcare,” pp. 423–
446, 2022.
[154] S. Sinha and D. Vohora, “Drug discovery and development: An
overview,” Pharmaceutical medicine and translational clinical research,
pp. 19–32, 2018.
GAUTAM KUMAR is currently pursuing B.Tech.
degree from Kalinga Institute of Industrial Tech-
nology (KIIT), Bhubaneshwar. He is currently (the
summer of 2023) pursuing his research internship
at the Birla Institute of Technology and Science
(BITS), Pilani under Dr. Vikas Hassija. His re-
search interests include machine learning, com-
puter vision, and deep learning.
SAHIL YADAV is currently pursuing B.Tech. de-
gree from Kalinga Institute of Industrial Technol-
ogy (KIIT), Bhubaneshwar. He is currently (the
summer of 2023) pursuing his research internship
at the Birla Institute of Technology and Science
(BITS), Pilani under Dr. Vikas Hassija. His re-
search interests include machine learning, rein-
forcement learning and deep learning.
ANIRUDDHA MUKHERJEE is currently pursu-
ing his B.Tech. degree from Kalinga Institute of
Industrial Technology (KIIT), Bhubaneshwar. He
was at Birla Institute of Technology and Science
(BITS), Pilani during the summer of 2023. On
campus, he was pursuing his research internship
under Dr Vinay Chamola. He has two published
patents to his name. His research interests include
machine learning, natural language processing,
computer vision, and deep learning for privacy &
security.
18 VOLUME X, 2020
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
VIKAS HASSIJA is an Associate Professor in the
School of Computer Engineering, Kalinga Insti-
tute of Industrial Technology, Bhubaneshwar. He
was a Postdoctoral Research Fellow at National
University of Singapore (NUS). He received his
M.E. degree in 2014 from BITS Pilani, India. His
current research is in Blockchain, Non fungible
tokens, IoT, privacy & security, and distributed
networks.
MOHSEN GUIZANI [S’85, M’89, SM’99, F’09]
received his B.S. (with distinction) and M.S. de-
grees in electrical engineering, and M.S. and Ph.D.
degrees in computer engineering from Syracuse
University, New York, in 1984, 1986, 1987, and
1990, respectively. He is a fellow of IEEE and a
senior member of ACM. He is currently a Pro-
fessor with the Machine Learning Department,
Mohamed Bin Zayed University of Artificial In-
telligence (MBZUAI), Abu Dhabi, UAE.
VOLUME X, 2020 19
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3376408
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
