R. Tate, M. Farhadi, C. Herold, G. Mohler, Swati Gupta
We study the Quantum Approximate Optimization Algorithm (QAOA) in the context of the Max-Cut problem. Noisy quantum devices are only able to accurately execute QAOA at low circuit depths, while classically-challenging problem instances may call for a relatively high circuit-depth. This is due to the need to build correlations between reachable pairs of vertices in potentially large graphs [16]. To enhance the solving power of low-depth QAOA, we introduce a classical pre-processing step that initializes QAOA with a biased superposition of possible cuts in the graph, referred to as a warm-start. In particular, we initialize QAOA with a solution to a low-rank semidefinite programming relaxation of the Max-Cut problem. Our experimental results show that this variant of QAOA, called QAOA-warm, is able to outperform standard QAOA on lower circuit depths in solution quality and training time. While this improvement is partly due to the classical warm-start, we find strong evidence of further improvement using QAOA circuit at small depth. We provide experimental evidence of improved performance as well as theoretical properties of the proposed framework.
{"title":"Bridging Classical and Quantum with SDP initialized warm-starts for QAOA","authors":"R. Tate, M. Farhadi, C. Herold, G. Mohler, Swati Gupta","doi":"10.1145/3549554","DOIUrl":"https://doi.org/10.1145/3549554","url":null,"abstract":"We study the Quantum Approximate Optimization Algorithm (QAOA) in the context of the Max-Cut problem. Noisy quantum devices are only able to accurately execute QAOA at low circuit depths, while classically-challenging problem instances may call for a relatively high circuit-depth. This is due to the need to build correlations between reachable pairs of vertices in potentially large graphs [16]. To enhance the solving power of low-depth QAOA, we introduce a classical pre-processing step that initializes QAOA with a biased superposition of possible cuts in the graph, referred to as a warm-start. In particular, we initialize QAOA with a solution to a low-rank semidefinite programming relaxation of the Max-Cut problem. Our experimental results show that this variant of QAOA, called QAOA-warm, is able to outperform standard QAOA on lower circuit depths in solution quality and training time. While this improvement is partly due to the classical warm-start, we find strong evidence of further improvement using QAOA circuit at small depth. We provide experimental evidence of improved performance as well as theoretical properties of the proposed framework.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115618243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise that is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates is critical to these models. We use a Bayesian inference approach to identify posterior distributions of these parameters such that they can be characterized more elaborately. By characterizing the device errors in this way, we can further improve the accuracy of quantum error mitigation. Experiments conducted on IBM’s quantum computing devices suggest that our approach provides better error mitigation performance than existing techniques used by the vendor. Also, our approach outperforms the standard Bayesian inference method in some scenarios.
{"title":"A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errors","authors":"Muqing Zheng, Ang Li, T. Terlaky, Xiu Yang","doi":"10.1145/3563397","DOIUrl":"https://doi.org/10.1145/3563397","url":null,"abstract":"Various noise models have been developed in quantum computing study to describe the propagation and effect of the noise that is caused by imperfect implementation of hardware. Identifying parameters such as gate and readout error rates is critical to these models. We use a Bayesian inference approach to identify posterior distributions of these parameters such that they can be characterized more elaborately. By characterizing the device errors in this way, we can further improve the accuracy of quantum error mitigation. Experiments conducted on IBM’s quantum computing devices suggest that our approach provides better error mitigation performance than existing techniques used by the vendor. Also, our approach outperforms the standard Bayesian inference method in some scenarios.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124603250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thien Nguyen, Anthony Santana, Tyler Kharazi, D. Claudino, H. Finkel, A. McCaskey
We present qcor—a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language agnostic manner, as well as a hardware-agnostic, retargetable compiler workflow targeting a number of physical and virtual quantum computing backends. qcor leverages novel Clang plugin interfaces and builds upon the XACC system-level quantum programming framework to provide a state-of-the-art integration mechanism for quantum-classical compilation that leverages the best from the community at-large. qcor translates quantum kernels ultimately to the XACC intermediate representation, and provides user-extensible hooks for quantum compilation routines like circuit optimization, analysis, and placement. This work details the overall architecture and compiler workflow for qcor, and provides a number of illuminating programming examples demonstrating its utility for near-term variational tasks, quantum algorithm expression, and feed-forward error correction schemes.
{"title":"Extending C++ for Heterogeneous Quantum-Classical Computing","authors":"Thien Nguyen, Anthony Santana, Tyler Kharazi, D. Claudino, H. Finkel, A. McCaskey","doi":"10.1145/3462670","DOIUrl":"https://doi.org/10.1145/3462670","url":null,"abstract":"We present qcor—a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context. Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language agnostic manner, as well as a hardware-agnostic, retargetable compiler workflow targeting a number of physical and virtual quantum computing backends. qcor leverages novel Clang plugin interfaces and builds upon the XACC system-level quantum programming framework to provide a state-of-the-art integration mechanism for quantum-classical compilation that leverages the best from the community at-large. qcor translates quantum kernels ultimately to the XACC intermediate representation, and provides user-extensible hooks for quantum compilation routines like circuit optimization, analysis, and placement. This work details the overall architecture and compiler workflow for qcor, and provides a number of illuminating programming examples demonstrating its utility for near-term variational tasks, quantum algorithm expression, and feed-forward error correction schemes.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"47 30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122160410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hardy’s paradox (equivalently, Hardy’s non-locality or Hardy’s test) [Phys. Rev. Lett. 68, 2981 (1992)] is used to show non-locality without inequalities, and it has been tested several times using optical circuits. We, for the first time, experimentally test Hardy’s paradox of non-locality in superconducting qubits. For practical verification of Hardy’s paradox, we argue that the error-modeling used in optical circuits is not useful for superconducting qubits. So, we propose a new error-modeling for Hardy’s paradox and a new method to estimate the lower bound on Hardy’s probability (i.e., the probability of a specific event in Hardy’s test) for superconducting qubits. Our results confirmed the theory that any non-maximally entangled state of two qubits violates Hardy’s equations; whereas, any maximally entangled state and product state of two qubits do not exhibit Hardy’s non-locality. Further, we point out the difficulties associated with the practical implementation of quantum protocols based on Hardy’s paradox and propose possible remedies. We also propose two performance measures for any two qubits of any quantum computer based on superconducting qubits.
哈代悖论(也就是哈代的非定域性或哈代的检验)[物理学]。Rev. Lett. 68, 2981(1992)]用于显示无不等式的非局部性，并且已经使用光学电路进行了多次测试。我们首次在超导量子比特中实验测试了哈迪的非定域性悖论。为了实际验证Hardy悖论，我们认为光学电路中使用的误差建模对于超导量子比特是无用的。因此，我们提出了一种新的Hardy悖论误差模型和一种估计超导量子比特Hardy概率下界(即Hardy检验中特定事件的概率)的新方法。我们的结果证实了两个量子位的任何非最大纠缠态违反哈代方程的理论;然而，两个量子位元的任何最大纠缠态和乘积态都不表现出Hardy的非局域性。此外，我们指出了与基于哈代悖论的量子协议的实际实施相关的困难，并提出了可能的补救措施。我们还提出了基于超导量子比特的任意量子计算机的任意两个量子比特的两个性能度量。
{"title":"A New Error-Modeling of Hardy’s Paradox for Superconducting Qubits and Its Experimental Verification","authors":"S. Das, G. Paul","doi":"10.1145/3396239","DOIUrl":"https://doi.org/10.1145/3396239","url":null,"abstract":"Hardy’s paradox (equivalently, Hardy’s non-locality or Hardy’s test) [Phys. Rev. Lett. 68, 2981 (1992)] is used to show non-locality without inequalities, and it has been tested several times using optical circuits. We, for the first time, experimentally test Hardy’s paradox of non-locality in superconducting qubits. For practical verification of Hardy’s paradox, we argue that the error-modeling used in optical circuits is not useful for superconducting qubits. So, we propose a new error-modeling for Hardy’s paradox and a new method to estimate the lower bound on Hardy’s probability (i.e., the probability of a specific event in Hardy’s test) for superconducting qubits. Our results confirmed the theory that any non-maximally entangled state of two qubits violates Hardy’s equations; whereas, any maximally entangled state and product state of two qubits do not exhibit Hardy’s non-locality. Further, we point out the difficulties associated with the practical implementation of quantum protocols based on Hardy’s paradox and propose possible remedies. We also propose two performance measures for any two qubits of any quantum computer based on superconducting qubits.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127252002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Almudena Carrera Vazquez, R. Hiptmair, Stefan Woerner
We present a quantum algorithm to solve systems of linear equations of the form Ax=b, where A is a tridiagonal Toeplitz matrix and b results from discretizing an analytic function, with a circuit complexity of O(1/√ε, poly (log κ, log N)), where N denotes the number of equations, ε is the accuracy, and κ the condition number. The repeat-until-success algorithm has to be run O(κ/(1-ε)) times to succeed, leveraging amplitude amplification, and needs to be sampled O(1/ε2) times. Thus, the algorithm achieves an exponential improvement with respect to N over classical methods. In particular, we present efficient oracles for state preparation, Hamiltonian simulation, and a set of observables together with the corresponding error and complexity analyses. As the main result of this work, we show how to use Richardson extrapolation to enhance Hamiltonian simulation, resulting in an implementation of Quantum Phase Estimation (QPE) within the algorithm with 1/√ε circuits that can be run in parallel each with circuit complexity 1/√ ε instead of 1/ε. Furthermore, we analyze necessary conditions for the overall algorithm to achieve an exponential speedup compared to classical methods. Our approach is not limited to the considered setting and can be applied to more general problems where Hamiltonian simulation is approximated via product formulae, although our theoretical results would need to be extended accordingly. All the procedures presented are implemented with Qiskit and tested for small systems using classical simulation as well as using real quantum devices available through the IBM Quantum Experience.
{"title":"Enhancing the Quantum Linear Systems Algorithm Using Richardson Extrapolation","authors":"Almudena Carrera Vazquez, R. Hiptmair, Stefan Woerner","doi":"10.1145/3490631","DOIUrl":"https://doi.org/10.1145/3490631","url":null,"abstract":"We present a quantum algorithm to solve systems of linear equations of the form Ax=b, where A is a tridiagonal Toeplitz matrix and b results from discretizing an analytic function, with a circuit complexity of O(1/√ε, poly (log κ, log N)), where N denotes the number of equations, ε is the accuracy, and κ the condition number. The repeat-until-success algorithm has to be run O(κ/(1-ε)) times to succeed, leveraging amplitude amplification, and needs to be sampled O(1/ε2) times. Thus, the algorithm achieves an exponential improvement with respect to N over classical methods. In particular, we present efficient oracles for state preparation, Hamiltonian simulation, and a set of observables together with the corresponding error and complexity analyses. As the main result of this work, we show how to use Richardson extrapolation to enhance Hamiltonian simulation, resulting in an implementation of Quantum Phase Estimation (QPE) within the algorithm with 1/√ε circuits that can be run in parallel each with circuit complexity 1/√ ε instead of 1/ε. Furthermore, we analyze necessary conditions for the overall algorithm to achieve an exponential speedup compared to classical methods. Our approach is not limited to the considered setting and can be applied to more general problems where Hamiltonian simulation is approximated via product formulae, although our theoretical results would need to be extended accordingly. All the procedures presented are implemented with Qiskit and tested for small systems using classical simulation as well as using real quantum devices available through the IBM Quantum Experience.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133673732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matteo G. Pozzi, Steven Herbert, A. Sengupta, Robert D. Mullins University of Cambridge Computer Laboratory, Cambridge Quantum Computing, Department of Engineering, U. Cambridge
‘‘Qubit routing” refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates. In this article, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available (Qiskit and t ( | ) ket ( rangle ) ), on both random and realistic circuits, across a range of near-term architecture sizes (with up to 50 qubits).
“量子比特路由”是指修改量子电路，使其满足目标量子计算机的连接约束的任务。这涉及到在电路中插入SWAP门，以便逻辑门只发生在相邻的物理量子位之间。目标是最小化SWAP门所增加的电路深度。在本文中，我们提出了一个量子比特路由过程，该过程使用了深度q学习范式的修改版本。该系统能够在随机和现实电路中，在一系列近期架构尺寸(最多50个量子位)上，超越目前可用的两个最先进的量子编译器(Qiskit和t ( | ) ket ( rangle ))的量子位路由程序。
{"title":"Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers","authors":"Matteo G. Pozzi, Steven Herbert, A. Sengupta, Robert D. Mullins University of Cambridge Computer Laboratory, Cambridge Quantum Computing, Department of Engineering, U. Cambridge","doi":"10.1145/3520434","DOIUrl":"https://doi.org/10.1145/3520434","url":null,"abstract":"‘‘Qubit routing” refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates. In this article, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available (Qiskit and t ( | ) ket ( rangle ) ), on both random and realistic circuits, across a range of near-term architecture sizes (with up to 50 qubits).","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133988006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The quantum circuit layout (QCL) problem involves mapping out a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter automatically infers the optimal QXX parameter values such that the laid out circuit has a reduced depth. In order to speed up circuit compilation, before laying the circuits out, we use a Gaussian function to estimate the depth of the compiled circuits. This Gaussian also informs the compiler about the circuit region that influences most the resulting circuit’s depth. We present empiric evidence for the feasibility of learning the layout method using approximation. QXX and QXX-MLP open the path to feasible large-scale QCL methods.
{"title":"Machine Learning Optimization of Quantum Circuit Layouts","authors":"A. Paler, L. Sasu, A. Florea, Razvan Andonie","doi":"10.1145/3565271","DOIUrl":"https://doi.org/10.1145/3565271","url":null,"abstract":"The quantum circuit layout (QCL) problem involves mapping out a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter automatically infers the optimal QXX parameter values such that the laid out circuit has a reduced depth. In order to speed up circuit compilation, before laying the circuits out, we use a Gaussian function to estimate the depth of the compiled circuits. This Gaussian also informs the compiler about the circuit region that influences most the resulting circuit’s depth. We present empiric evidence for the feasibility of learning the layout method using approximation. QXX and QXX-MLP open the path to feasible large-scale QCL methods.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126354890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, major progress has been made towards the realisation of quantum internet to enable a broad range of classically intractable applications. These applications such as delegated quantum computation require running a secure identification protocol between a low-resource and a high-resource party to provide secure communication. In this work, we propose two identification protocols based on the emerging hardware-secure solutions, the quantum Physical Unclonable Functions (qPUFs). The first protocol allows a low-resource party to prove its identity to a high-resource party and in the second protocol, it is vice versa. Unlike existing identification protocols based on Quantum Read-out PUFs that rely on the security against a specific family of attacks, our protocols provide provable exponential security against any Quantum Polynomial-Time adversary with resource-efficient parties. We provide a comprehensive comparison between the two proposed protocols in terms of resources such as quantum memory and computing ability required in both parties as well as the communication overhead between them.
{"title":"Client-server Identification Protocols with Quantum PUF","authors":"Mina Doosti, N. Kumar, M. Delavar, E. Kashefi","doi":"10.1145/3484197","DOIUrl":"https://doi.org/10.1145/3484197","url":null,"abstract":"Recently, major progress has been made towards the realisation of quantum internet to enable a broad range of classically intractable applications. These applications such as delegated quantum computation require running a secure identification protocol between a low-resource and a high-resource party to provide secure communication. In this work, we propose two identification protocols based on the emerging hardware-secure solutions, the quantum Physical Unclonable Functions (qPUFs). The first protocol allows a low-resource party to prove its identity to a high-resource party and in the second protocol, it is vice versa. Unlike existing identification protocols based on Quantum Read-out PUFs that rely on the security against a specific family of attacks, our protocols provide provable exponential security against any Quantum Polynomial-Time adversary with resource-efficient parties. We provide a comprehensive comparison between the two proposed protocols in terms of resources such as quantum memory and computing ability required in both parties as well as the communication overhead between them.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122294781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-level benchmark suite and insightful evaluation metrics for characterizing the properties of prototype NISQ devices, the efficiency of QC programming compilers, schedulers and assemblers, and the capability of quantum system simulators in a classical computer. In this work, we fill this gap by proposing a low-level, easy-to-use benchmark suite called QASMBench based on the OpenQASM assembly representation. It consolidates commonly used quantum routines and kernels from a variety of domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, and so on, trading-off between generality and usability. To analyze these kernels in terms of NISQ device execution, in addition to circuit width and depth, we propose four circuit metrics including gate density, retention lifespan, measurement density, and entanglement variance, to extract more insights about the execution efficiency, the susceptibility to NISQ error, and the potential gain from machine-specific optimizations. Applications in QASMBench can be launched and verified on several NISQ platforms, including IBM-Q, Rigetti, IonQ and Quantinuum. For evaluation, we measure the execution fidelity of a subset of QASMBench applications on 12 IBM-Q machines through density matrix state tomography, comprising 25K circuit evaluations. We also compare the fidelity of executions among the IBM-Q machines, the IonQ QPU and the Rigetti Aspen M-1 system. QASMBench is released at: http://github.com/pnnl/QASMBench.
{"title":"QASMBench: A Low-Level Quantum Benchmark Suite for NISQ Evaluation and Simulation","authors":"Ang Li, S. Stein, S. Krishnamoorthy, James Ang","doi":"10.1145/3550488","DOIUrl":"https://doi.org/10.1145/3550488","url":null,"abstract":"The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-level benchmark suite and insightful evaluation metrics for characterizing the properties of prototype NISQ devices, the efficiency of QC programming compilers, schedulers and assemblers, and the capability of quantum system simulators in a classical computer. In this work, we fill this gap by proposing a low-level, easy-to-use benchmark suite called QASMBench based on the OpenQASM assembly representation. It consolidates commonly used quantum routines and kernels from a variety of domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, and so on, trading-off between generality and usability. To analyze these kernels in terms of NISQ device execution, in addition to circuit width and depth, we propose four circuit metrics including gate density, retention lifespan, measurement density, and entanglement variance, to extract more insights about the execution efficiency, the susceptibility to NISQ error, and the potential gain from machine-specific optimizations. Applications in QASMBench can be launched and verified on several NISQ platforms, including IBM-Q, Rigetti, IonQ and Quantinuum. For evaluation, we measure the execution fidelity of a subset of QASMBench applications on 12 IBM-Q machines through density matrix state tomography, comprising 25K circuit evaluations. We also compare the fidelity of executions among the IBM-Q machines, the IonQ QPU and the Rigetti Aspen M-1 system. QASMBench is released at: http://github.com/pnnl/QASMBench.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114356150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the last few years, several quantum algorithms that try to address the problem of partial differential equation solving have been devised: on the one hand, “direct” quantum algorithms that aim at encoding the solution of the PDE by executing one large quantum circuit; on the other hand, variational algorithms that approximate the solution of the PDE by executing several small quantum circuits and making profit of classical optimisers. In this work, we propose an experimental study of the costs (in terms of gate number and execution time on a idealised hardware created from realistic gate data) associated with one of the “direct” quantum algorithm: the wave equation solver devised in [32]. We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm. We also explain in great detail the implementation steps and discuss some possibilities of improvements. Finally, our implementation proves experimentally that some PDE can be solved on a quantum computer, even if the direct quantum algorithm chosen will require error-corrected quantum chips, which are not believed to be available in the short-term.
{"title":"Practical Quantum Computing","authors":"Adrien Suau, G. Staffelbach, H. Calandra","doi":"10.1145/3430030","DOIUrl":"https://doi.org/10.1145/3430030","url":null,"abstract":"In the last few years, several quantum algorithms that try to address the problem of partial differential equation solving have been devised: on the one hand, “direct” quantum algorithms that aim at encoding the solution of the PDE by executing one large quantum circuit; on the other hand, variational algorithms that approximate the solution of the PDE by executing several small quantum circuits and making profit of classical optimisers. In this work, we propose an experimental study of the costs (in terms of gate number and execution time on a idealised hardware created from realistic gate data) associated with one of the “direct” quantum algorithm: the wave equation solver devised in [32]. We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm. We also explain in great detail the implementation steps and discuss some possibilities of improvements. Finally, our implementation proves experimentally that some PDE can be solved on a quantum computer, even if the direct quantum algorithm chosen will require error-corrected quantum chips, which are not believed to be available in the short-term.","PeriodicalId":365166,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130018186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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