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Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms (2211.15631v2)

Published 28 Nov 2022 in quant-ph, cs.LG, and cs.PF

Abstract: Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms. A robust large-scale study of how the performance of these platforms scales with the number of qubits is key to providing quantum solutions to challenging industry problems. This work benchmarks the runtime and accuracy for a representative sample of specialized high-performance simulated and physical quantum processing units. Results show the QMware simulator can reduce the runtime for executing a quantum circuit by up to 78% compared to the next fastest option for algorithms with fewer than 27 qubits. The AWS SV1 simulator offers a runtime advantage for larger circuits, up to the maximum 34 qubits available with SV1. Beyond this limit, QMware can execute circuits as large as 40 qubits. Physical quantum devices, such as Rigetti's Aspen-M2, can provide an exponential runtime advantage for circuits with more than 30 qubits. However, the high financial cost of physical quantum processing units presents a serious barrier to practical use. Moreover, only IonQ's Harmony quantum device achieves high fidelity with more than four qubits. This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.

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References (71)
  1. Quantum machine learning. Nature, 549(7671):195, 2017.
  2. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Reports on Progress in Physics, 81(7):074001, 2018.
  3. Lucas Lamata. Quantum machine learning and quantum biomimetics: A perspective. Machine Learning: Science and Technology, 1(3):033002, 2020.
  4. Quantum machine learning: from physics to software engineering. Advances in Physics: X, 8(1):2165452, 2023.
  5. Classical versus quantum models in machine learning: Insights from a finance application. Machine Learning: Science and Technology, 1(3):035003, 2020.
  6. Hyperparameter optimization of hybrid quantum neural networks for car classification. arXiv preprint arXiv:2205.04878, 2022.
  7. Quantum Machine Learning for Finance. arXiv preprint arXiv:2109.04298, 2021.
  8. Quantum machine learning for image classification. arXiv preprint arXiv:2304.09224, 2023.
  9. Generation of high-resolution handwritten digits with an ion-trap quantum computer. Physical Review X, 12(3):031010, 2022.
  10. Hybrid quantum neural network for drug response prediction. Cancers, 15(10):2705, 2023.
  11. Quantum algorithms applied to satellite mission planning for Earth observation. arXiv preprint arXiv:2302.07181, 2023.
  12. Quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes. arXiv preprint arXiv:2304.11247, 2023.
  13. Freely scalable and reconfigurable optical hardware for deep learning. Scientific Reports, 11(1):3144, 2021.
  14. Measuring the Algorithmic Efficiency of Neural Networks. arXiv preprint arXiv:2005.04305, 2020.
  15. Practical application-specific advantage through hybrid quantum computing. arXiv preprint arXiv:2205.04858, 2022.
  16. Generalization in quantum machine learning from few training data. Nature Communications, 13(1):4919, 2022.
  17. The power of quantum neural networks. Nature Computational Science, 1(6):403–409, 2021.
  18. Characterizing quantum supremacy in near-term devices. Nature Physics, 14(6):595–600, 2018.
  19. Equivalences and Separations Between Quantum and Classical Learnability. SIAM Journal on Computing, 33(5):1067–1092, 2004.
  20. Demonstration of quantum advantage in machine learning. npj Quantum Information, 3(1):16, 2017.
  21. Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4):043001, 2019.
  22. Learning with Quantum Models, pages 247–272. Springer International Publishing, Cham, 2018.
  23. Parallel hybrid networks: an interplay between quantum and classical neural networks. arXiv preprint arXiv:2303.03227, 2023.
  24. Quantum Machine Learning in Finance: Time Series Forecasting. arXiv preprint arXiv:2202.00599, 2022.
  25. Quantum versus classical generative modelling in finance. Quantum Science and Technology, 6(2):024013, 2021.
  26. Foundations for Near-Term Quantum Natural Language Processing. arXiv preprint arXiv:2012.03755, 2020.
  27. Grammar-Aware Question-Answering on Quantum Computers. arXiv preprint arXiv:2012.03756, 2020.
  28. Scaling for edge inference of deep neural networks. Nature Electronics, 1(4):216–222, 2018.
  29. Efficient Processing of Deep Neural Networks: A Tutorial and Survey. arXiv preprint arXiv:1703.09039, 2017.
  30. Mark Horowitz. 1.1 Computing’s energy problem (and what we can do about it). In IEEE International Solid-State Circuits Conference, pages 10–14, 2014.
  31. Evaluating analytic gradients on quantum hardware. Physical Review A, 99(3):032331, 2019.
  32. Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms. https://github.com/terra-quantum-public/benchmarking, 2023.
  33. Atos. Quantum Learning Machine. https://atos.net/en/solutions/quantum-learning-machine, 2022.
  34. Gadi Aleksandrowicz et al. Qiskit: An open-source framework for quantum computing. IBM Quantum, 2019.
  35. Koki Aoyama. Qulacs. https://github.com/qulacs/qulacs, 2022.
  36. IBM quantum breaks the 100‑qubit processor barrier. https://research.ibm.com/blog/127-qubit-quantum-processor-eagle, 2021.
  37. Demonstration of the trapped-ion quantum CCD computer architecture. Nature, 592:209–213, 2021.
  38. Quantum supremacy using a programmable superconducting processor. Nature, 574:505–510, 2019.
  39. Benchmarking an 11-qubit quantum computer. Nature Communications, 10, 11 2019.
  40. Quantum chemistry as a benchmark for near-term quantum computers. npj Quantum Information, 5, 2019.
  41. Benchmarking quantum computers and the impact of quantum noise, 2019.
  42. Application-oriented performance benchmarks for quantum computing. https://github.com/SRI-International/QC-App-Oriented-Benchmarks, 2021.
  43. Pacific Northwest National Laboratory. QASMBench benchmark suite. https://github.com/pnnl/QASMBench, 2022.
  44. Application-motivated, holistic benchmarking of a full quantum computing stack. Quantum, 5:415, 2021.
  45. Q-score. https://github.com/myQLM/qscore, 06 2022.
  46. An application benchmark for fermionic quantum simulations, 2020.
  47. Qpack: Quantum approximate optimization algorithms as universal benchmark for quantum computers, 2021.
  48. PennyLane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968, 2022.
  49. Amazon Web Services. Amazon braket. https://aws.amazon.com/, 2020.
  50. QMware. QMware — The first global quantum cloud. https://qm-ware.com/, 2022.
  51. Quality, Speed, and Scale: Three key attributes to measure the performance of near-term quantum computers. arXiv preprint arXiv:2110.14108, 2021.
  52. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.
  53. George Marsaglia. Choosing a Point from the Surface of a Sphere. The Annals of Mathematical Statistics, 43(2):645–646, 1972.
  54. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980, 2017.
  55. Learning representations by back-propagating errors. Nature, 323(6088):533–536, 1986.
  56. Efficient calculation of gradients in classical simulations of variational quantum algorithms. arXiv preprint arXiv:2009.02823, 2020.
  57. PyTorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019.
  58. William Chauvenet. A Manual of Spherical and Practical Astronomy: Spherical astronomy. J. B. Lippincott & Company, 1863.
  59. Barren plateaus in quantum neural network training landscapes. Nature Communications, 9(1):4812, 2018.
  60. Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus. Quantum, 5:466, 2021.
  61. Large gradients via correlation in random parameterized quantum circuits. Quantum Science and Technology, 6(2):025008, 2021.
  62. An initialization strategy for addressing barren plateaus in parametrized quantum circuits. Quantum, 3:214, 2019.
  63. An exponentially-growing family of universal quantum circuits. arXiv preprint arXiv:2212.00736, 2022.
  64. The Race to Quantum Advantage Depends on Benchmarking. Boston Consulting Group, Tech. Rep, 2022.
  65. SoK: Benchmarking the Performance of a Quantum Computer. Entropy, 24(10):1467, 2022.
  66. Benchmarking gate-based quantum computers. Computer Physics Communications, 220:44–55, 2017.
  67. Demonstrating NISQ era challenges in algorithm design on IBM’s 20 qubit quantum computer. AIP Advances, 10(9):095101, 2020.
  68. Validating quantum computers using randomized model circuits. Physical Review A, 100(3):032328, 2019.
  69. Peter Chapman. Scaling IonQ’s Quantum Computers: The Roadmap. https://ionq.com/, 2020.
  70. Scalable benchmarks for gate-based quantum computers. arXiv preprint arXiv:2104.10698, 2021.
  71. On the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization. arXiv preprint arXiv:2210.04291, 2022.
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