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Design Automation Challenges and Benefits of Dynamic Quantum Circuit in Present NISQ Era and Beyond: (Invited Paper)
... Optimization tasks such as floorplanning, routing, and process variation handling are critical in minimizing the time and cost of VLSI manufacturing while maintaining optimal circuit performance. Despite considerable progress, classical optimization techniques can lead to nonoptimal solutions, particularly in high-dimensional, combinatorial problems that are inherent in VLSI design [1][2][3]. ...
The growing complexity of Very Large Scale Integration (VLSI) circuit designs has significantly increased the demand for innovative approaches to enhance design efficiency and fabrication accuracy. Traditional computational methods face limitations in terms of scalability and optimization for complex VLSI systems. The advent of quantum computing presents a promising paradigm shift, offering exponential speedup in solving computationally intensive problems such as optimization, simulation, and data analysis in VLSI design and fabrication. This research investigates the potential of quantum computing algorithms to optimize VLSI circuit design and fabrication processes, addressing key challenges such as layout optimization, routing, and process variation. The problem lies in the increasing complexity and size of VLSI circuits, which often lead to inefficient designs, longer manufacturing times, and increased cost. Current methods such as classical algorithms and heuristic techniques struggle to achieve optimal solutions within reasonable time frames. Quantum algorithms, particularly those based on quantum annealing and variational quantum eigensolvers (VQE), have shown promise in solving combinatorial optimization problems with much higher efficiency. By leveraging quantum computing, the goal is to improve the optimization of circuit layouts, minimize power consumption, and reduce production costs in VLSI systems. The proposed method utilizes quantum computing techniques to model and solve optimization problems related to VLSI circuit design and fabrication. Simulations and comparisons with classical methods show significant improvements in design efficiency, reduced error rates, and faster optimization cycles. The outcomes of this study could transform the future of VLSI design and fabrication, leading to more efficient, cost-effective, and scalable solutions for semiconductor manufacturing.
Quantum simulation traditionally relies on unitary dynamics, inherently imposing efficiency constraints on the generation of intricate entangled states. In principle, these limitations can be superseded by nonunitary, dynamic circuits. These circuits exploit measurements alongside conditional feed-forward operations, providing a promising approach for long-range entangling gates, higher effective connectivity of near-term hardware, and more efficient state preparations. Here, we explore the utility of shallow dynamic circuits for creating long-range entanglement on large-scale quantum devices. Specifically, we study two tasks: controlled- gate teleportation between up to 101 qubits by feeding forward 99 midcircuit measurement outcomes, and the preparation of Greenberger–Horne–Zeilinger states with genuine entanglement. In the former, we observe that dynamic circuits can outperform their unitary counterparts. In the latter, by tallying instructions of compiled quantum circuits, we provide an error budget detailing the obstacles that must be addressed to unlock the full potential of dynamic circuits. Looking forward, we expect dynamic circuits to be useful for generating long-range entanglement in the near term on large-scale quantum devices.
Published by the American Physical Society 2024
In noisy intermediate-scale quantum computing, the limited scalability of a single quantum processing unit (QPU) can be extended through distributed quantum computing (DQC), in which one can implement global operations over two QPUs by entanglement-assisted local operations and classical communication. To facilitate this type of DQC in experiments, we need an entanglement-efficient protocol. To this end, we extend the protocol in [Eisert et. al., PRA, 62:052317(2000)] implementing each nonlocal controlled-unitary gate locally with one maximally entangled pair to a packing protocol, which can pack multiple nonlocal controlled-unitary gates locally using one maximally entangled pair. In particular, two types of packing processes are introduced as the building blocks, namely the distributing processes and embedding processes. Each distributing process distributes corresponding gates locally with one entangled pair. The efficiency of entanglement is then enhanced by embedding processes, which merge two non-sequential distributing processes and hence save the entanglement cost. We show that the structure of distributability and embeddability of a quantum circuit can be fully represented by the corresponding packing graphs and conflict graphs. Based on these graphs, we derive heuristic algorithms for finding an entanglement-efficient packing of distributing processes for a given quantum circuit to be implemented by two parties. These algorithms can determine the required number of local auxiliary qubits in the DQC. We apply these algorithms for bipartite DQC of unitary coupled-cluster circuits and find a significant reduction of entanglement cost through embeddings. This method can determine a constructive upper bound on the entanglement cost for the DQC of quantum circuits.
The searching efficiency of the quantum approximate optimization algorithm is dependent on both the classical and quantum sides of the algorithm. Recently a quantum approximate Bayesian optimization algorithm (QABOA) that includes two mixers was developed, where surrogate-based Bayesian optimization is applied to improve the sampling efficiency of the classical optimizer. A continuous-time quantum walk mixer is used to enhance exploration, and the generalized Grover mixer is also applied to improve exploitation. In this paper, an extension of the QABOA is proposed to further improve its searching efficiency. The searching efficiency is enhanced through two aspects. First, two mixers, including one for exploration and the other for exploitation, are applied in an alternating fashion. Second, uncertainty of the quantum circuit is quantified with a new quantum Matérn kernel based on the kurtosis of the basis state distribution, which increases the chance of obtaining the optimum. The proposed new two-mixer QABOA's with and without uncertainty quantification are compared with three single-mixer QABOA's on five discrete and four mixed-integer problems. The results show that the proposed two-mixer QABOA with uncertainty quantification has the best performance in efficiency and consistency for five out of the nine tested problems. The results also show that QABOA with the generalized Grover mixer performs the best among the single-mixer algorithms, thereby demonstrating the benefit of exploitation and the importance of dynamic exploration-exploitation balance in improving searching efficiency.
B. Nachman et al. [Phys. Rev. Lett. 126, 062001 (2021)] recently introduced an algorithm (QPS) for simulating parton showers with intermediate flavor states using polynomial resources on a digital quantum computer. We make use of a new quantum hardware capability called dynamical quantum computing to improve the scaling of this algorithm, which significantly improves the method precision. In particular, we modify the quantum parton shower circuit to incorporate midcircuit qubit measurements, resets, and quantum operations conditioned on classical information. This reduces the computational depth from O(N5log2(N)2) to O(N3log2(N)2) and the qubit requirements from O(Nlog2(N)) to O(N). Using “matrix product state” state vector simulators, we demonstrate that the improved algorithm yields expected results for 2, 3, 4, and 5-steps of the algorithm. We compare absolute costs with the original QPS algorithm, and show that dynamical quantum computing can significantly reduce costs in the class of digital quantum algorithms representing quantum walks (which includes the QPS). Python code that implements QPS, both with and without dynamic gates, is publicly available on Github.
We present t|ket, a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t|ket is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has been extensively benchmarked and outperforms most competitors in terms of circuit optimisation and qubit routing.
The Quantum Internet, a network interconnecting remote quantum devices through quantum links in synergy with classical ones, is envisioned as the final stage of the quantum revolution, opening fundamentally new communications and computing capabilities. But the Quantum Internet is governed by the laws of quantum mechanics. Phenomena with no counterpart in classical networks, such as no-cloning, quantum measurement, entanglement and quantum teleportation, impose new challenging constraints for network design. Specifically, classical network functionalities are based on the assumption that classical information can be safely read and copied. However, this assumption does not hold in the Quantum Internet. As a consequence, its design requires a major network-paradigm shift to harness the quantum mechanics specificities. The goal of this work is to shed light on the challenges and open problems of Quantum Internet design. We first introduce some basic knowledge of quantum mechanics, needed to understand the differences between a classical and a quantum network. Then, we introduce quantum teleportation as the key strategy for transmitting quantum information without physically transferring the particle that stores the quantum information or violating the principles of quantum mechanics. Finally, the key research challenges to design quantum communication networks are discussed.
The aim of this review is to provide quantum engineers with an introductory guide to the central concepts and challenges in the rapidly accelerating field of superconducting quantum circuits. Over the past twenty years, the field has matured from a predominantly basic research endeavor to a one that increasingly explores the engineering of larger-scale superconducting quantum systems. Here, we review several foundational elements—qubit design, noise properties, qubit control, and readout techniques—developed during this period, bridging fundamental concepts in circuit quantum electrodynamics and contemporary, state-of-the-art applications in gate-model quantum computation.
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away --- we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.
The technological world is in the midst of a quantum computing and quantum
information revolution. Since Richard Feynman's famous "plenty of room at the
bottom" lecture, hinting at the notion of novel devices employing quantum
mechanics, the quantum information community has taken gigantic strides in
understanding the potential applications of a quantum computer and laid the
foundational requirements for building one. We believe that the next
significant step will be to demonstrate a quantum memory, in which a system of
interacting qubits stores an encoded logical qubit state longer than the
incorporated parts. Here, we describe the important route towards a logical
memory with superconducting qubits, employing a rotated version of the surface
code. The current status of technology with regards to interconnected
superconducting-qubit networks will be described and near-term areas of focus
to improve devices will be identified. Overall, the progress in this exciting
field has been astounding, but we are at an important turning point where it
will be critical to incorporate engineering solutions with quantum
architectural considerations, laying the foundation towards scalable
fault-tolerant quantum computers in the near future.
We investigate the minimal resources that are required in the local implementation of nonlocal quantum gates in a distributed quantum computer. Both classical communication requirements and entanglement consumption are investigated. We present general statements on the minimal resource requirements and present optimal procedures for a number of important gates, including controlled-NOT (CNOT) and Toffoli gates. We show that one bit of classical communication in each direction is both necessary and sufficient for the nonlocal implementation of the quantum CNOT, while in general two bits in each direction is required for the implementation of a general two-bit quantum gate. In particular, the state swapper requires this maximum classical communication overhead. Extensions of these ideas to multiparty gates are presented.
A scalable quantum computer could be built by networking together many simple
processor cells, thus avoiding the need to create a single complex structure.
The difficulty is that realistic quantum links are very error prone. A solution
is for cells to repeatedly communicate with each other and so 'purify' any
imperfections; however prior studies suggest that the cells themselves must
then have prohibitively low internal error rates. Here we describe a method by
which even error-prone cells can perform purification: groups of cells generate
shared resource states, which then enable stabilization of topologically
encoded data. Given a realistically noisy network (>=10% error rate) we find
that our protocol can succeed provided that intra-cell error rates for
initialisation, state manipulation and measurement are below 0.82%. This level
of fidelity is already achievable in several laboratory systems.
A new method for determining elementary quantum gate realizations for multiple-control Toffoli (MCT) gates is presented. The realization for each MCT gate is formed as a composition of realizations of smaller MCT gates. A marking algorithm which is more effective than the traditional moving rule is used to optimize the final circuit. The main improvement is that the resulting circuits make significantly better use of ancillary lines than has been achieved in earlier approaches. Initial results are also presented for circuits with nearest-neighbour communication. These results show that the overall approach is not as effective for that problem indicating that research on direct synthesis of nearest-neighbour quantum circuits should be considered. While, the results presented are for the NCV quantum gate library (i.e. for quantum circuits composed of NOT gates, controlled-NOT gates, and controlled-V /V + gates), the approach can be applied to other libraries of elementary quantum gates.
We present an algorithm for computing depth-optimal decompositions of logical
operations, leveraging a meet-in-the-middle technique to provide a significant
speed-up over simple brute force algorithms. As an illustration of our method
we implemented this algorithm and found factorizations of the commonly used
quantum logical operations into elementary gates in the Clifford+T set. In
particular, we report a decomposition of the Toffoli gate over the set of
Clifford and T gates. Our decomposition achieves a total T-depth of 3, thereby
providing a 40% reduction over the previously best known decomposition for the
Toffoli gate. Due to the size of the search space the algorithm is only
practical for small parameters, such as the number of qubits, and the number of
gates in an optimal implementation.
Quantum computational algorithms exploit quantum mechanics to solve problems
exponentially faster than the best classical algorithms. Shor's quantum
algorithm for fast number factoring is a key example and the prime motivator in
the international effort to realise a quantum computer. However, due to the
substantial resource requirement, to date, there have been only four
small-scale demonstrations. Here we address this resource demand and
demonstrate a scalable version of Shor's algorithm in which the n qubit control
register is replaced by a single qubit that is recycled n times: the total
number of qubits is one third of that required in the standard protocol.
Encoding the work register in higher-dimensional states, we implement a
two-photon compiled algorithm to factor N=21. The algorithmic output is
distinguishable from noise, in contrast to previous demonstrations. These
results point to larger-scale implementations of Shor's algorithm by harnessing
scalable resource reductions applicable to all physical architectures.
An unknown quantum state ‖φ〉 can be disassembled into, then later reconstructed from, purely classical information and purely nonclassical Einstein-Podolsky-Rosen (EPR) correlations. To do so the sender, ‘‘Alice,’’ and the receiver, ‘‘Bob,’’ must prearrange the sharing of an EPR-correlated pair of particles. Alice makes a joint measurement on her EPR particle and the unknown quantum system, and sends Bob the classical result of this measurement. Knowing this, Bob can convert the state of his EPR particle into an exact replica of the unknown state ‖φ〉 which Alice destroyed.
We show that a set of gates that consists of all one-bit quantum gates (U(2)) and the two-bit exclusive-or gate (that maps Boolean values (x,y) to ) is universal in the sense that all unitary operations on arbitrarily many bits n (U()) can be expressed as compositions of these gates. We investigate the number of the above gates required to implement other gates, such as generalized Deutsch-Toffoli gates, that apply a specific U(2) transformation to one input bit if and only if the logical AND of all remaining input bits is satisfied. These gates play a central role in many proposed constructions of quantum computational networks. We derive upper and lower bounds on the exact number of elementary gates required to build up a variety of two-and three-bit quantum gates, the asymptotic number required for n-bit Deutsch-Toffoli gates, and make some observations about the number required for arbitrary n-bit unitary operations. Comment: 31 pages, plain latex, no separate figures, submitted to Phys. Rev. A. Related information on http://vesta.physics.ucla.edu:7777/
With the availability of moderate sized noisy quantum computers, researchers have been exploring various methods to execute quantum circuits using these devices. In some of these platforms (e.g. those using superconducting qubits), the qubit coupling architecture imposes constraints on 2-qubit gate operations. This is referred to as Nearest Neighbor (NN) constraint, which requires the interacting qubits to be adjacent. To make a quantum circuit NN-complaint, additional gates are required to be added that may compromise on the computational reliability. Quantum algorithms that involve Toffoli gate operations, must be first re-described using 1- and 2-qubit elementary quantum gates. Broadly two major approaches exist for decomposing larger Toffoli gates into elementary gates: (i) using dirty ancilla, (ii) using clean ancilla. The dirty ancilla based approach introduces more 2-qubit gates to describe a Toffoli netlist, while reducing the requirement of additional qubits during decomposition. On the other hand, clean ancilla based approach requires less number of 2-qubit gates at the cost of additional clean ancilla qubits. None of the methods available in the literature consider architectural information during decomposition. In this work to the best of our knowledge, for the first time we propose an architecture-aware decomposition approach, which exploits the Qubit Interaction Graph structure of Toffoli gates. The Clifford+T gate library is used for the final decomposed netlist. From the conducted experiments on various Toffoli netlists, it is observed that the architecture-aware approach generates netlists with comparable number of 2-qubit gates as in clean ancilla based approach when more physical qubits are available. Moreover, mapping the resulting netlists on physical architectures (viz. Hex20 and IBM27) reveals that the architectural information during decomposition is beneficial in reducing gate overhead.
The scarcity of qubits is a major obstacle to the practical usage of quantum computers in the near future. To circumvent this problem, various circuit knitting techniques have been developed to partition large quantum circuits into subcircuits that fit on smaller devices, at the cost of a simulation overhead. In this work, we study a particular method of circuit knitting based on quasiprobability simulation of nonlocal gates with operations that act locally on the subcircuits. We investigate whether classical communication between these local quantum computers can help. We provide a positive answer by showing that for circuits containing
n
nonlocal CNOT gates connecting two circuit parts, the simulation overhead can be reduced from
O
(9
n
) to
O
(4
n
) if one allows for classical information exchange. Similar improvements can be obtained for general Clifford gates and, at least in a restricted form, for other gates such as controlled rotation gates.
Quantum computing offers substantial speedup over conventional computing in solving certain computationally hard problems. The emergence of quantum computers in recent years has motivated researchers to develop design automation tools to map quantum circuits to such platforms. One major challenge is to limit the noise or computational error during gate operations; in particular, errors are higher when gates operate on non-neighbor qubits. A common approach to tackle this problem is to make the circuits Nearest-Neighbor (NN) compliant by inserting either Swap gates or CNOT templates. Reduction of gate overhead also becomes important as it serves to limit the overall noise and error. In some recent works, mapping of quantum circuits to hexagonal qubit architecture have been investigated. Hexagonal layout of qubits offers extended neighborhood that helps to reduce the number of Swap or additional CNOT gates required for NN-compliance. Existing approaches incur high gate overheads that can be reduced by improved gate mapping strategies with better cost metrics. The present work proposes one such approach using a priority-based cost metric. The proposed cost-metric is general and can be applied to any architectures; however, in this work we show its benefit for hexagonal architecture. Experiments on benchmark circuits confirm that the proposed method reduces gate overhead by over a very recent work based on greedy mapping.KeywordsQuantum circuitsArchitecture-aware decompositionQubit mappingClean and dirty ancilla
Elementary gate decomposition of larger Toffoli operations is often carried out using additional qubits (ancilla). The number of gates and the circuit depth vary in such transformation depending on the type of ancilla used (clean or dirty). The present Noisy Intermediate Scale Quantum (NISQ) devices have limited number of coherent qubits with faulty native operation support. Superconducting devices also have coupling restrictions or Nearest-Neighbor (NN) constraints, which require additional gates to map the transformed netlist for execution. While the mapping overhead is correlated with the number of 2-qubit gates and involved qubits, the fidelity of execution is inversely proportional to the number of gates and circuit depth. There is a tradeoff in devising the transpilation (i.e. low-level transformation and mapping) approach — dirty ancilla demands less qubits and overhead at the expense of more gates and depth as compared to clean ancilla, which involves less gates and depth at the expense of more qubits and overhead. This paper analyzes the disparity in gates, depth and qubits between: (i) the low-level transformation approaches without considering device coupling information, and (ii) the mapping schemes based on netlist transformation using a specific type of ancilla. We analyze the benefits of using NN-constraints at the transformation stage, and the impact of distributing clean ancilla across architectures. We have carried out experiments on IBM Q20 and Hexagonal Q20 architectures, which show improvements of 17% and 13% respectively in terms of number of gates.KeywordsQuantum circuitsArchitecture-aware decompositionQubit mappingClean and dirty ancilla
Nonequilibrium physics including many-body localization (MBL) has attracted increasing attentions, but theoretical approaches of reliably studying nonequilibrium properties remain quite limited. In this Letter, we propose a systematic approach to probe MBL phases via the excited-state variational quantum eigensolver (VQE) and demonstrate convincing results of MBL on a quantum hardware, which we believe paves a promising way for future simulations of nonequilibrium systems beyond the reach of classical computations in the noisy intermediate-scale quantum (NISQ) era. Moreover, the MBL probing protocol based on excited-state VQE is NISQ-friendly, as it can successfully differentiate the MBL phase from thermal phases with relatively shallow quantum circuits, and it is also robust against the effect of quantum noises.
To date, quantum computation on real, physical devices has largely been limited to simple, time-ordered sequences of unitary operations followed by a final projective measurement. As hardware platforms for quantum computing continue to mature in size and capability, it is imperative to enable quantum circuits beyond their conventional construction. Here we break into the realm of dynamic quantum circuits on a superconducting-based quantum system. Dynamic quantum circuits not only involve the evolution of the quantum state throughout the computation but also periodic measurements of qubits midcircuit and concurrent processing of the resulting classical information on timescales shorter than the execution times of the circuits. Using noisy quantum hardware, we explore one of the most fundamental quantum algorithms, quantum phase estimation, in its adaptive version, which exploits dynamic circuits, and compare the results to a nonadaptive implementation of the same algorithm. We demonstrate that the version of real-time quantum computing with dynamic circuits can yield results comparable to an approach involving classical asynchronous postprocessing, thus opening the door to a new realm of available algorithms on real quantum systems.
Limited quantum memory is one of the most important constraints for near-term quantum devices. Understanding whether a small quantum computer can simulate a larger quantum system, or execute an algorithm requiring more qubits than available, is both of theoretical and practical importance. In this Letter, we introduce cluster parameters K and d of a quantum circuit. The tensor network of such a circuit can be decomposed into clusters of size at most d with at most K qubits of inter-cluster quantum communication. We propose a cluster simulation scheme that can simulate any (K,d)-clustered quantum circuit on a d-qubit machine in time roughly 2O(K), with further speedups possible when taking more fine-grained circuit structure into account. We show how our scheme can be used to simulate clustered quantum systems—such as large molecules—that can be partitioned into multiple significantly smaller clusters with weak interactions among them. By using a suitable clustered ansatz, we also experimentally demonstrate that a quantum variational eigensolver can still achieve the desired performance for estimating the energy of the BeH2 molecule while running on a physical quantum device with half the number of required qubits.
Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom and arbitrarily long coherence time. In Noisy Intermediate-Scale Quantum (NISQ) devices, however, the number of qubits is highly limited and quantum operation error and qubit coherence are not negligible. Besides, the connectivity of physical qubits in a quantum processing unit (QPU) is also strictly constrained. Thereby, additional operations like SWAP gates have to be inserted to satisfy this constraint while preserving the functionality of the original circuit. This process is known as quantum circuit transformation. Adding additional gates will increase both the size and depth of a quantum circuit and therefore cause further decay of the performance of a quantum circuit. Thus it is crucial to minimize the number of added gates. In this paper, we propose an efficient method to solve this problem. We first choose by using simulated annealing an initial mapping which fits well with the input circuit and then, with the help of a heuristic cost function, stepwise apply the best selected SWAP gates until all quantum gates in the circuit can be executed. Our algorithm runs in time polynomial in all parameters including the size and the qubit number of the input circuit, and the qubit number in the QPU. Its space complexity is quadratic to the number of edges in the QPU. Experimental results on extensive realistic circuits confirm that the proposed method is efficient and the number of added gates of our algorithm is, on average, only 57% of that of state-of-the-art algorithms on IBM Q20 (Tokyo), the most recent IBM quantum device.
Quantum computers are becoming a reality today due to the rapid progress made by researchers in the last years. In the process of building quantum computers, IBM has developed several versions—starting from 5-qubit architectures like IBM QX2 and IBM QX4 to larger 16-or 20-qubit architectures. These architectures support arbitrary rotations of a single qubit and a controlled negation (CNOT) involving two qubits. The two qubit operations come with added coupling-map restrictions that only allow specific physical qubits to be the control and target qubits of the operation. In order to execute a quantum circuit on the IBM QX architecture, CNOT gates must satisfy the so-called coupling constraints of the architecture. Previous works addressed this issue with the objective of reducing the number of gates and the circuit depth. However, in this work we show that further improvements are possible. To this end, we present a general approach for further improving the number of gate operations and depth of the mapped circuit. The proposed approach encompasses the selection of physical qubits, determining initial and local permutations efficiently to obtain the final circuit mapped to the given IBM QX architecture. Through experiments improvements are observed over existing methods in terms of the number of gates and circuit depth.
Due to little considerations in the hardware constraints, e.g., limited connections between physical qubits to enable two-qubit gates, most quantum algorithms cannot be directly executed on the Noisy Intermediate-Scale Quantum (NISQ) devices. Dynamically remapping logical qubits to physical qubits in the compiler is needed to enable the two-qubit gates in the algorithm, which introduces additional operations and inevitably reduces the fidelity of the algorithm. Previous solutions in finding such remapping suffer from high complexity, poor initial mapping quality, and limited flexibility and control. To address these drawbacks mentioned above, this paper proposes a SWAP-based Bidirectional heuristic search algorithm (SABRE), which is applicable to NISQ devices with arbitrary connections between qubits. By optimizing every search attempt, globally optimizing the initial mapping using a novel reverse traversal technique, introducing the decay effect to enable the trade-off between the depth and the number of gates of the entire algorithm, SABRE outperforms the best known algorithm with exponential speedup and comparable or better results on various benchmarks.
The Noisy Intermediate-Scale Quantum (NISQ) technology is currently investigated by major players in the field to build the first practically useful quantum computer. IBM QX architectures are the first ones which are already publicly available today. However, in order to use them, the respective quantum circuits have to be compiled for the respectively used target architecture. While first approaches have been proposed for this purpose, they are infeasible for a certain set of SU(4) quantum circuits which have recently been introduced to benchmark corresponding compilers. In this work, we analyze the bottlenecks of existing compilers and provide a dedicated method for compiling this kind of circuits to IBM QX architectures. Our experimental evaluation (using tools provided by IBM) shows that the proposed approach significantly outperforms IBM's own solution regarding fidelity of the compiled circuit as well as runtime. Moreover, the solution proposed in this work has been declared winner of the IBM QISKit Developer Challenge. An implementation of the proposed methodology is publicly available at http://iic.jku.at/eda/research/ibm_qx_mapping.
In March 2017, IBM launched the project IBM Q with the goal to provide access to quantum computers for a broad audience. This allowed users to conduct quantum experiments on a 5-qubit and, since June 2017, also on a 16-qubit quantum computer (called IBM QX2 and IBM QX3, respectively). In order to use these, the desired quantum functionality (e.g. provided in terms of a quantum circuit) has to properly be mapped so that the underlying physical constraints are satisfied - a complex task. This demands for solutions to automatically and efficiently conduct this mapping process. In this paper, we propose such an approach which satisfies all constraints given by the architecture and, at the same time, aims to keep the overhead in terms of additionally required quantum gates minimal. The proposed approach is generic and can easily be configured for future architectures. Experimental evaluations show that the proposed approach clearly outperforms IBM's own mapping solution. In fact, for many quantum circuits, the proposed approach determines a mapping to the IBM architecture within less than five minutes (and within a fraction of a second in most cases), while IBM's solution suffers from long runtimes and runs into a timeout of 1 hour in several cases. As an additional benefit, the proposed approach yields mapped circuits with smaller costs (i.e. fewer additional gates are required).
We present parity measurements on a five-qubit lattice with connectivity amenable to the surface code quantum error correction architecture. Using all-microwave controls of superconducting qubits coupled via resonators, we encode the parities of four data qubit states in either the X- or the Z-basis. Given the connectivity of the lattice, we perform full characterization of the static Z-interactions within the set of five qubits, as well as dynamical Z-interactions brought along by single- and two-qubit microwave drives. The parity measurements are significantly improved by modifying the microwave two-qubit gates to dynamically remove non-ideal Z errors.
Machines are possible to have some artificial intelligence like human beings
owing to particular algorithms or software. Such machines could learn knowledge
from what people taught them and do works according to the knowledge. In
practical learning cases, the data is often extremely complicated and large,
thus classical learning machines often need huge computational resources.
Quantum machine learning algorithm, on the other hand, could be exponentially
faster than classical machines using quantum parallelism. Here, we demonstrate
a quantum machine learning algorithm on a four-qubit NMR test bench to solve an
optical character recognition problem, also known as the handwriting
recognition. The quantum machine learns standard character fonts and then
recognize handwritten characters from a set with two candidates. To our best
knowledge, this is the first artificial intelligence realized on a quantum
processor. Due to the widespreading importance of artificial intelligence and
its tremendous consuming of computational resources, quantum speedup would be
extremely attractive against the challenges from the Big Data.
One of the most cited books in physics of all time, Quantum Computation and Quantum Information remains the best textbook in this exciting field of science. This 10th anniversary edition includes an introduction from the authors setting the work in context. This comprehensive textbook describes such remarkable effects as fast quantum algorithms, quantum teleportation, quantum cryptography and quantum error-correction. Quantum mechanics and computer science are introduced before moving on to describe what a quantum computer is, how it can be used to solve problems faster than 'classical' computers and its real-world implementation. It concludes with an in-depth treatment of quantum information. Containing a wealth of figures and exercises, this well-known textbook is ideal for courses on the subject, and will interest beginning graduate students and researchers in physics, computer science, mathematics, and electrical engineering.
Quantum computers hold the promise of solving certain computational tasks much more efficiently than classical computers. We review recent experimental advances towards a quantum computer with trapped ions. in particular, various implementations of qubits, quantum gates and some key experiments are discussed. Furthermore, we review some implementations of quantum algorithms such as a deterministic teleportation of quantum information and an error correction scheme. (c) 2008 Elsevier B.V. All rights reserved.
Imagine a phone directory containing N names arranged in completely random order. In order to find someone's phone number with a 50% probability, any classical algorithm (whether deterministic or probabilistic) will need to look at a minimum of N/2 names. Quantum mechanical systems can be in a superposition of states and simultaneously examine multiple names. By properly adjusting the phases of various operations, successful computations reinforce each other while others interfere randomly. As a result, the desired phone number can be obtained in only O(sqrt(N)) steps. The algorithm is within a small constant factor of the fastest possible quantum mechanical algorithm.