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Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intract...
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... Quantum computing has been considered one of the most emerging fields in recent years, with the potential to significantly impact different industries by solving complex problems challenging for classical computers [1]. Recent advances in quantum computing have shown progress in various areas, such as quantum error correction [2], quantum supremacy [3], and the development of quantum algorithms integration with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) models. ...
Quantum Cloud Data Centers (QCDCs) are one of the emerging fields that considerably enhance data computing by integrating quantum processing capabilities with conventional cloud infrastructure, which leads to high computational power for tackling complex challenges in comparison to the classic Cloud Data Center (CDC). Although QCDCs hold paramount importance, a few researches have been developed directly focusing on them. To this end, this paper presents a Machine Learning (ML)-assisted modeling and simulation of a QCDC and its electrical power prediction. The system includes a Quantum Processing Unit (QPU), cryogenic dynamics, error correction, and power prediction. Key classes include Qubit for quantum bits with noise and crosstalk, Quantum Processor for multi-Qubit gates, Cryogenic System for thermal management, and Surface Code for error correction. The developed ML model estimates power using simulation data and Key Performance Indicators (KPIs). A central class unifies these, simulating circuits over 24,000 shots (24 hours) on a QCDC with two QPUs (200 Qubits each), testing a sample circuit with Hadamard, rotation, Control-Z (CZ), and measurement gates. Based on the conducted simulation results, the proposed model achieves KPIs well below 1.2, verifying its accuracy and performance in the predictive modeling of the proposed QCDC.
... In the future, QC will play a crucial role in safeguarding IoT communication. According to Gill et al. [66], quantum advantage can be utilized across domains such as medicine, cybersecurity, IoT, weather forecasting, national laboratories, and even complex challenges like autonomous cars. While the current IoT communication infrastructure relies on classical cryptography, such as Public and Private structures, it remains vulnerable to attacks from quantum computers. ...
The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining Quantum Computing with Machine Learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. Quantum Computing, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, quantum computing, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating quantum computing approaches with ML. It also addresses the challenges and limitations of integrating ML and quantum computing in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.
... Moreover, in long-distance quantum communications, secure data sharing methods using quantum key distribution utilizing satellites offer substantial leaps forward. In addition, the development of next-generation materials, such as topological quantum materials, and the integration of machine learning with quantum algorithms hint at overcoming current hardware limitations (Gill, Kumar, et al., 2022). These technologies, combined with progress in various fields, show promise to shape the future of the industry, leading to stronger and more sustainable technology. ...
Modern computing systems are undergoing rapid transformation through theoretical advances and technical innovations. This article explores the technological and application trends in quantum-driven artificial intelligence (AI) innovations. The authors explore the mathematical frameworks underlying AI and quantum computing systems, with a particular focus on the role of algebraic topology in quantum circuit optimization and error correction. From neural networks to transformers, they investigate how AI architectures are reshaping computational capabilities, such as in healthcare, autonomous systems, and real-time computing. They highlight key hardware advances such as 3D stacked memory, neuromorphic chips, and quantum computing integration. They identify key challenges and limitations by focusing on ethical considerations, computation constraints, and scaling issues. This article looks ahead at research in quantum and AI, highlighting emerging technologies, potential breakthroughs and emerging trends, and spotlighting technological convergence and research trajectories.
... In quantum computing, entangled qubits governed by probabilistic rules can collectively yield reliable computational outcomes. Each qubit exists in a superposition of states, and only collapses into a definite state upon measurement, in accordance with quantum mechanical probability laws [31], [39]- [41]. Unlike classical computing, which operates through deterministic binary logic, quantum computing leverages uncertainty, entanglement, and interference to perform calculations [12]. ...
The quantification of social science remains a longstanding challenge, largely due to the philosophical nature of its foundational theories. Although quantum computing has advanced rapidly in recent years, its relevance to social theory remains underexplored. Most existing research focuses on micro-cognitive models or philosophical analogies, leaving a gap in system-level applications of quantum principles to the analysis of social systems. This study addresses that gap by proposing a theoretical and computational framework that combines quantum mechanics with Generative AI to simulate the emergence and evolution of social norms. Drawing on core quantum concepts--such as superposition, entanglement, and probabilistic measurement--this research models society as a dynamic, uncertain system and sets up five ideal-type experiments. These scenarios are simulated using 25 generative agents, each assigned evolving roles as compliers, resistors, or enforcers. Within a simulated environment monitored by a central observer (the Watcher), agents interact, respond to surveillance, and adapt to periodic normative disruptions. These interactions allow the system to self-organize under external stress and reveal emergent patterns. Key findings show that quantum principles, when integrated with generative AI, enable the modeling of uncertainty, emergence, and interdependence in complex social systems. Simulations reveal patterns including convergence toward normative order, the spread of resistance, and the spontaneous emergence of new equilibria in social rules. In conclusion, this study introduces a novel computational lens that lays the groundwork for a quantum-informed social theory. It offers interdisciplinary insights into how society can be understood not just as a structure to observe but as a dynamic system to simulate and redesign through quantum technologies.
... Quantum qubit [15], [16] is the fundamental unit of information in quantum computing, represented as a superposition of two orthogonal quantum states | 0⟩ and | 1⟩. each associated with complex probability amplitudes and [13], [17], [18] respectively. Mathematically, a general qubit state | ⟩ can be expressed as (1), where and are complex numbers satisfying the normalization condition | | 2 +| β | 2 = 1. ...
... The image, denoted as , is transformed into a quantum state using the tensor product of pixel coordinates and their respective gray values. The quantum state based on plain image can be seen in (15). Where, ...
... The initial step involves permuting the pixel positions in the image to obscure its spatial structure. Based (15), to scramble this process can be seen in (16): ...
Digital image security is increasingly vulnerable to sophisticated attacks, underscoring the urgent need for robust encryption techniques. Traditional encryption methods often fall short in defending against advanced threats, highlighting the importance of innovative solutions to protect digital images. This study tackles these challenges by incorporating quantum computing into image encryption, employing techniques such as bit-plane scrambling, pixel permutation, and bit permutation. These strategies enhance security by introducing complex, non-linear transformations that make decryption attempts significantly more difficult without the correct cryptographic keys. A key configuration based on r=44, μ=2024 is employed to achieve this. The integration of quantum bit-plane scrambling and quantum pixel permutation results in a highly secure encryption method. Experimental results show substantial improvements in entropy levels, along with strong unified average changing intensity (UACI) and number of pixels change rate(NPCR) values across various images. Notably, the "Peppers" image achieved the best performance, with UACI values of 33.5572 and NPCR values of 99.8301. The method proves highly effective, as repeated tests with incorrect keys failed to decrypt the plain image accurately. Future research could explore the addition of a discrete quantum wavelet transform to further enhance the security and efficiency of quantum-based image encryption methods.
... Quantum simulation is an emerging field that aims at studying quantum systems with quantum hardware to reach a level of accuracy unattainable with classical computers. Quantum computers [1][2][3][4][5][6][7] are proving increasingly effective to provide such hardware [8], even leveraging intrinsic noise [9]. Recently, quantum algorithms have also been applied to nuclear physics in order to determine nuclear structures and simulate elementary processes [10][11][12][13] including our work [14]. ...
Quantum computers have shown promise in simulating complex quantum systems, including nuclear processes that challenge even supercomputers. We summarize the key ingredients to demonstrate the feasibility of a full nuclear transition simulation, covering also ground and excited state preparation. The tritium nucleus has been used to model strong interactions between nucleons, with quantum circuits used to represent the initial and final states. Variational quantum algorithms aid in preparing such states, requiring four qubits to describe spin-isospin states. Our results show low relative errors in the energy estimation for the obtained eigenstates. Additionally, the transition probability between these states has been estimated as a function of dipole polarization angle. We also study the transition for the neutron capture d(n, γ)t and compare the result with the tritium de-excitation.
... Garhwal et al. [28] Quantum programming languages. Gill et al. [13] Quantum computing. Gyongyosi et al. [12] Quantum computing. ...
... Quantum computing utilizes the fundamental concepts of quantum mechanics for computing, such as superposition, entanglement, and interference [80,[98][99][100][101][102]. As the development of quantum computers evolve [13,80,98,99,101,[103][104][105][106][107][108][109][110][111][112][113][114][115][116][117][118][119], the relevance of quantum computations has become more interpretable for problem-solving tasks [80,[120][121][122][123]. Quantum computers could definitely help solving difficult computational problems [12,98,[124][125][126][127][128][129][130]. ...
... In the NISQ era [98,130], gate-model quantum computers have particular relevance since these architectures can be implemented on near-term settings [13,80,[98][99][100][101][103][104][105][106][107][108][109][110][111][113][114][115][116][117][118][119][134][135][136][137]. In a gate-model quantum computer, the computational steps are realized via unitary gates. ...
The intense growth of quantum computation and communication allows the development of advanced solutions and services. Networked quantum services are provided for the users via quantum computers and quantum networking. Here, we review the fundamental concepts and recent achievements of networked quantum services. We present a comprehensive study of the state of the art, the different technologies, platforms and applications. We analyze the implementation basis and identify key challenges.
... In the quest of fault tolerance, many technologies are being explored as candidates to host the error corrected quantum computers of the future. Superconducting junctions, neutral atoms, ion traps or silicon spin quantum dots are some examples of these technologies [3]. ...
... Note, however, that in realistic hardware, single qubit gates present error rates around an order of magnitude lower than entangling gates. Thus, the performance will be better when considering such non-identically distributed noise scenarios [1][2][3][4][5]. ...
Tailoring quantum error correction codes (QECC) to biased noise has demonstrated significant benefits. However, most of the prior research on this topic has focused on code capacity noise models. Furthermore, a no-go theorem prevents the construction of CNOT gates for two-level qubits in a bias preserving manner which may, in principle, imply that noise bias cannot be leveraged in such systems. In this work, we show that a residual bias up to 5 can be maintained in CNOT gates under certain conditions. Moreover, we employ controlled-phase (CZ) gates in syndrome extraction circuits and show how to natively implement these in a bias-preserving manner for a broad class of qubit platforms. This motivates the introduction of what we call a hybrid biased-depolarizing (HBD) circuit-level noise model which captures these features. We numerically study the performance of the XZZX surface code and observe that bias-preserving CZ gates are critical for leveraging biased noise. Accounting for the residual bias present in the CNOT gates, we observe an increase in the code threshold up to a physical error rate, representing a improvement. Additionally, we find that the required qubit footprint can be reduced by up to a at relevant physical error rates.
... Recent research has focused on improving quantum algorithms and hardware [ 2,44,45]. Quantum computers use quantum bits (qubits) that can exist in multiple states simultaneously due to superposition and entanglement [ 3]. García et al. (2023) conducted a systematic literature review identifying various quantum implementations of classical machine learning algorithms [ 4]. ...
... These algorithms demonstrate the potential for exponential speedup in solving certain computational problems [ 5]. Additionally, recent advancements in quantum algorithms have expanded their applicability to areas such as cryptography, and optimization [ 3]. Sood and Chauhan (2023) emphasize the need for advancements in quantum hardware and error correction techniques to achieve practical quantum advantage [ 5]. ...
... Sood and Chauhan (2023) emphasize the need for advancements in quantum hardware and error correction techniques to achieve practical quantum advantage [ 5]. Research is ongoing to improve new quantum hardware architectures [ 3]. A review by Gill et al. (2020) discusses the potential impact of quantum computing on these fields and highlights ongoing research efforts to develop quantum algorithms tailored to specific applications [ 3]. ...
This chapter discusses quantum computing and fuzzy logic. The quantum bits (qubits) inherently contains uncertainty. This motivates the study to define quantum fuzzy logic. Besides few imporatnt properties of quantum fuzzy logic has been discussed.
... Even with the steady advancements in Quantum Computing technology in terms of both, qubit counts and fault tolerance [7,8], and the increasing number of hardware vendors, the current NISQ computers still remain out of reach for constant use for many developers. This is due to hardware scarcity, vendor dependent development infrastructure and high operational costs of QPUs. ...
Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, these new capabilities need software solutions that are able to effectively harness its power. However, developers may face significant challenges when developing and executing quantum software due to the limited availability of quantum computer hardware, high computational demands of simulating quantum computers on classical systems, and complicated technology stack to enable currently available accelerators into development environments. These limitations make it difficult for the developer to create an efficient workflow for quantum software development. In this paper, we investigate the potential of using remote computational capabilities in an efficient manner to improve the workflow of quantum software developers, by lowering the barrier of moving between local execution and computationally more efficient remote hardware and offering speedup in execution with simulator surroundings. The goal is to allow the development of more complex circuits and to support an iterative software development approach. In our experiment, with the solution presented in this paper, we have obtained up to 5 times faster circuit execution runtime, and enabled qubit ranges from 21 to 29 qubits with a simple plug-and-play kernel for the Jupyter notebook.
