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The QUATRO Application Suite: Quantum Computing for Models of Human Cognition

Published 1 Sep 2023 in cs.CE, cs.DC, cs.ET, q-bio.NC, and quant-ph | (2309.00597v2)

Abstract: Research progress in quantum computing has, thus far, focused on a narrow set of application domains. Expanding the suite of quantum application domains is vital for the discovery of new software toolchains and architectural abstractions. In this work, we unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling. Cognitive models are critical to understanding and replicating human intelligence. Our work connects computational cognitive models to quantum computer architectures for the first time. We release QUATRO, a collection of quantum computing applications from cognitive models. The development and execution of QUATRO shed light on gaps in the quantum computing stack that need to be closed to ease programming and drive performance. Among several contributions, we propose and study ideas pertaining to quantum cloud scheduling (using data from gate- and annealing-based quantum computers), parallelization, and more. In the long run, we expect our research to lay the groundwork for more versatile quantum computer systems in the future.

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References (139)
  1. “Amazon Braket.” [Online]. Available: https://aws.amazon.com/braket/
  2. “Azure Quantum cloud service.” [Online]. Available: https://azure.microsoft.com/en-us/products/quantum
  3. “D-Wave Leap.” [Online]. Available: https://www.dwavesys.com/solutions-and-products/cloud-platform/
  4. “IBM Quantum.” [Online]. Available: https://quantum-computing.ibm.com/
  5. “Xanadu Cloud.” [Online]. Available: https://platform.xanadu.ai/
  6. Y. Alexeev, D. Bacon, K. R. Brown, R. Calderbank, L. D. Carr, F. T. Chong, B. DeMarco, D. Englund, E. Farhi, B. Fefferman, A. V. Gorshkov, A. Houck, J. Kim, S. Kimmel, M. Lange, S. Lloyd, M. D. Lukin, D. Maslov, P. Maunz, C. Monroe, J. Preskill, M. Roetteler, M. J. Savage, and J. Thompson, “Quantum computer systems for scientific discovery,” PRX Quantum, vol. 2, p. 017001, Feb 2021. [Online]. Available: https://link.aps.org/doi/10.1103/PRXQuantum.2.017001
  7. D. Kumaran, D. Hassabis, and J. L. McClelland, “What learning systems do intelligent agents need? complementary learning systems theory updated,” Trends in cognitive sciences, vol. 20, no. 7, pp. 512–534, 2016.
  8. E. M. Pothos and J. R. Busemeyer, “Quantum cognition,” Annual review of psychology, vol. 73, pp. 749–778, 2022.
  9. D. Kahneman, “Maps of bounded rationality: Psychology for behavioral economics,” American economic review, vol. 93, no. 5, pp. 1449–1475, 2003.
  10. J. Veselỳ, R. P. Pothukuchi, K. Joshi, S. Gupta, J. D. Cohen, and A. Bhattacharjee, “Distill: Domain-specific compilation for cognitive models,” in 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO).   IEEE, 2022, pp. 301–312.
  11. K. J. Holyoak, “Analogy and relational reasoning,” in The Oxford handbook of thinking and reasoning, K. J. Holyoak and R. G. Morrison, Eds.   Oxford University Press, 2012, pp. 234–259.
  12. M. K. Ho, D. Abel, J. D. Cohen, M. L. Littman, and T. L. Griffiths, “The efficiency of human cognition reflects planned information processing,” in AAAI Conference on Artificial Intelligence, Apr. 2020.
  13. L. Rosendahl, A. S. Bizyaeva, and J. Cohen, “A novel quantum approach to the dynamics of decision making.” in CogSci, 2020.
  14. M. L. Rosendahl, “The multi-particle multi-well (mpmw) framework: A quantum framework incorporating attentional capture, representational generality, and arousal to perceptual choice problems,” Ph.D. dissertation, Princeton University, 2022.
  15. J. R. Busemeyer, Z. Wang, and R. M. Shiffrin, “Bayesian model comparison favors quantum over standard decision theory account of dynamic inconsistency.” Decision, vol. 2, no. 1, p. 1, 2015.
  16. E. M. Pothos and J. R. Busemeyer, “Quantum principles in psychology: The debate, the evidence, and the future,” Behavioral and Brain Sciences, vol. 36, no. 3, pp. 310–327, 2013.
  17. P. D. Bruza, Z. Wang, and J. R. Busemeyer, “Quantum cognition: a new theoretical approach to psychology,” Trends in cognitive sciences, vol. 19, no. 7, pp. 383–393, 2015.
  18. G. P. Epping and J. R. Busemeyer, “Using diverging predictions from classical and quantum models to dissociate between categorization systems,” Journal of Mathematical Psychology, vol. 112, p. 102738, 2023.
  19. D. Widdows, J. Rani, and E. M. Pothos, “Quantum circuit components for cognitive decision-making,” Entropy, vol. 25, no. 4, 2023. [Online]. Available: https://www.mdpi.com/1099-4300/25/4/548
  20. J. Liu, G. T. Byrd, and H. Zhou, “Quantum circuits for dynamic runtime assertions in quantum computation,” in Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, ser. ASPLOS ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 1017–1030. [Online]. Available: https://doi.org/10.1145/3373376.3378488
  21. J. Liu and H. Zhou, “Systematic approaches for precise and approximate quantum state runtime assertion,” in 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2021, pp. 179–193.
  22. K. Temme, S. Bravyi, and J. M. Gambetta, “Error Mitigation for Short-Depth Quantum Circuits,” Phys. Rev. Lett., vol. 119, no. 18, p. 180509, Nov. 2017.
  23. S. Endo, S. C. Benjamin, and Y. Li, “Practical Quantum Error Mitigation for Near-Future Applications,” Phys. Rev. X, vol. 8, no. 3, p. 031027, Jul. 2018.
  24. Z. Cai, R. Babbush, S. C. Benjamin, S. Endo, W. J. Huggins, Y. Li, J. R. McClean, and T. E. O’Brien, “Quantum Error Mitigation,” arXiv, Oct. 2022.
  25. T. Patel and D. Tiwari, “Veritas: Accurately estimating the correct output on noisy intermediate-scale quantum computers,” in SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, 2020, pp. 1–16.
  26. ——, “Qraft: Reverse your quantum circuit and know the correct program output,” in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ser. ASPLOS ’21.   New York, NY, USA: Association for Computing Machinery, 2021, p. 443–455. [Online]. Available: https://doi.org/10.1145/3445814.3446743
  27. S. S. Tannu and M. K. Qureshi, “Mitigating measurement errors in quantum computers by exploiting state-dependent bias,” in Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, ser. MICRO ’52.   New York, NY, USA: Association for Computing Machinery, 2019, p. 279–290. [Online]. Available: https://doi.org/10.1145/3352460.3358265
  28. P. Murali, D. C. Mckay, M. Martonosi, and A. Javadi-Abhari, “Software mitigation of crosstalk on noisy intermediate-scale quantum computers,” in Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, ser. ASPLOS ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 1001–1016. [Online]. Available: https://doi.org/10.1145/3373376.3378477
  29. P. Das, C. A. Pattison, S. Manne, D. M. Carmean, K. M. Svore, M. Qureshi, and N. Delfosse, “Afs: Accurate, fast, and scalable error-decoding for fault-tolerant quantum computers,” in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2022, pp. 259–273.
  30. Y. Suzuki, T. Sugiyama, T. Arai, W. Liao, K. Inoue, and T. Tanimoto, “Q3de: A fault-tolerant quantum computer architecture for multi-bit burst errors by cosmic rays,” in 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2022, pp. 1110–1125.
  31. A. JavadiAbhari, S. Patil, D. Kudrow, J. Heckey, A. Lvov, F. T. Chong, and M. Martonosi, “Scaffcc: A framework for compilation and analysis of quantum computing programs,” in Proceedings of the 11th ACM Conference on Computing Frontiers, ser. CF ’14.   New York, NY, USA: Association for Computing Machinery, 2014. [Online]. Available: https://doi.org/10.1145/2597917.2597939
  32. W. Tang, T. Tomesh, M. Suchara, J. Larson, and M. Martonosi, “Cutqc: using small quantum computers for large quantum circuit evaluations,” in Proceedings of the 26th ACM International conference on architectural support for programming languages and operating systems, 2021, pp. 473–486.
  33. P. Murali, N. M. Linke, M. Martonosi, A. J. Abhari, N. H. Nguyen, and C. H. Alderete, “Full-stack, real-system quantum computer studies: Architectural comparisons and design insights,” in Proceedings of the 46th International Symposium on Computer Architecture, ser. ISCA ’19.   New York, NY, USA: Association for Computing Machinery, 2019, p. 527–540. [Online]. Available: https://doi.org/10.1145/3307650.3322273
  34. S. Mittal and S. Vaishay, “A survey of techniques for optimizing deep learning on gpus,” Journal of Systems Architecture, vol. 99, p. 101635, 2019.
  35. I. M. Georgescu, S. Ashhab, and F. Nori, “Quantum simulation,” Rev. Mod. Phys., vol. 86, no. 1, pp. 153–185, Mar. 2014.
  36. A. Aspuru-Guzik, A. D. Dutoi, P. J. Love, and M. Head-Gordon, “Simulated Quantum Computation of Molecular Energies,” Science, vol. 309, no. 5741, pp. 1704–1707, Sep. 2005.
  37. R. Orús, S. Mugel, and E. Lizaso, “Quantum computing for finance: Overview and prospects,” Rev. Phys., vol. 4, p. 100028, Nov. 2019.
  38. P. Rebentrost, B. Gupt, and T. R. Bromley, “Quantum computational finance: Monte carlo pricing of financial derivatives,” Physical Review A, vol. 98, no. 2, p. 022321, 2018.
  39. J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, vol. 549, no. 7671, pp. 195–202, 2017.
  40. N. Moll, P. Barkoutsos, L. S. Bishop, J. M. Chow, A. Cross, D. J. Egger, S. Filipp, A. Fuhrer, J. M. Gambetta, M. Ganzhorn, A. Kandala, A. Mezzacapo, P. Müller, W. Riess, G. Salis, J. Smolin, I. Tavernelli, and K. Temme, “Quantum optimization using variational algorithms on near-term quantum devices,” Quantum Science and Technology, vol. 3, no. 3, p. 030503, 2018.
  41. “Quantum computing: Progress and prospects,” National Academies of Sciences, Engineering, and Medicine, 2019, National Academies Press.
  42. R. Ratcliff, “A theory of memory retrieval,” Psychological Review, vol. 85, no. 2, pp. 59–108, 1978.
  43. M. Usher and J. L. McClelland, “The time course of perceptual choice: the leaky, competing accumulator model.” Psychological review, vol. 108, no. 3, p. 550, 2001.
  44. S. Musslick and J. D. Cohen, “Rationalizing constraints on the capacity for cognitive control,” Trends in Cognitive Sciences, vol. 25, no. 9, pp. 757–775, 2021.
  45. A. Graves, G. Wayne, and I. Danihelka, “Neural turing machines,” arXiv preprint arXiv:1410.5401, 2014.
  46. D. Rolnick, A. Ahuja, J. Schwarz, T. Lillicrap, and G. Wayne, “Experience replay for continual learning,” Advances in Neural Information Processing Systems, vol. 32, 2019.
  47. T. W. Webb, I. Sinha, and J. Cohen, “Emergent symbols through binding in external memory,” in International Conference on Learning Representations, 2020.
  48. B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman, “Building machines that learn and think like people,” Behavioral and brain sciences, vol. 40, p. e253, 2017.
  49. A. Tversky and E. Shafir, “The disjunction effect in choice under uncertainty,” Psychological science, vol. 3, no. 5, pp. 305–310, 1992.
  50. E. M. Pothos and J. R. Busemeyer, “Can quantum probability provide a new direction for cognitive modeling?” Behavioral and brain sciences, vol. 36, no. 3, pp. 255–274, 2013.
  51. P. D. Kvam, T. J. Pleskac, S. Yu, and J. R. Busemeyer, “Interference effects of choice on confidence: Quantum characteristics of evidence accumulation,” Proceedings of the National Academy of Sciences, vol. 112, no. 34, pp. 10 645–10 650, 2015.
  52. J. R. Busemeyer, Z. Wang, and J. T. Townsend, “Quantum dynamics of human decision-making,” Journal of Mathematical Psychology, vol. 50, no. 3, pp. 220–241, 2006.
  53. J. R. Busemeyer, P. D. Kvam, and T. J. Pleskac, “Comparison of markov versus quantum dynamical models of human decision making,” Wiley Interdisciplinary Reviews: Cognitive Science, vol. 11, no. 4, p. e1526, 2020.
  54. T. Tomesh, P. Gokhale, V. Omole, G. S. Ravi, K. N. Smith, J. Viszlai, X.-C. Wu, N. Hardavellas, M. R. Martonosi, and F. T. Chong, “Supermarq: A scalable quantum benchmark suite,” in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA).   IEEE, 2022, pp. 587–603.
  55. A. Mallick, S. Mandal, A. Karan, and C. M. Chandrashekar, “Simulating dirac hamiltonian in curved space-time by split-step quantum walk,” Journal of Physics Communications, vol. 3, no. 1, p. 015012, 2019.
  56. T. Oka, N. Konno, R. Arita, and H. Aoki, “Breakdown of an electric-field driven system: a mapping to a quantum walk,” Physical review letters, vol. 94, no. 10, p. 100602, 2005.
  57. M. Ferdman, A. Adileh, O. Kocberber, S. Volos, M. Alisafaee, D. Jevdjic, C. Kaynak, A. D. Popescu, A. Ailamaki, and B. Falsafi, “Clearing the clouds: A study of emerging scale-out workloads on modern hardware,” SIGPLAN Not., vol. 47, no. 4, p. 37–48, mar 2012. [Online]. Available: https://doi.org/10.1145/2248487.2150982
  58. R. Ratcliff, P. L. Smith, S. D. Brown, and G. McKoon, “Diffusion decision model: Current issues and history,” Trends in cognitive sciences, vol. 20, no. 4, pp. 260–281, 2016.
  59. J. R. Busemeyer, S. Gluth, J. Rieskamp, and B. M. Turner, “Cognitive and neural bases of multi-attribute, multi-alternative, value-based decisions,” Trends in cognitive sciences, vol. 23, no. 3, pp. 251–263, 2019.
  60. T. L. Willke, S. B. M. Yoo, M. Capotă, S. Musslick, B. Y. Hayden, and J. D. Cohen, “A comparison of non-human primate and deep reinforcement learning agent performance in a virtual pursuit-avoidance task,” in Reinforcement Learning and Decision Making Conference, 2019.
  61. R. Bogacz, M. Usher, J. Zhang, and J. L. McClelland, “Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 362, no. 1485, pp. 1655–1670, 2007.
  62. Y. Ding and F. T. Chong, “Circuit synthesis and compilation,” in Quantum Computer Systems: Research for Noisy Intermediate-Scale Quantum Computers.   Springer, 2020, pp. 91–125.
  63. A. Matuschak and M. A. Nielsen, “Quantum Computing for the Very Curious,” 2019. [Online]. Available: https://quantum.country/qcvc
  64. E. Farhi and S. Gutmann, “Quantum computation and decision trees,” Physical Review A, vol. 58, no. 2, p. 915, 1998.
  65. S. E. Venegas-Andraca, “Quantum walks: a comprehensive review,” Quantum Information Processing, vol. 11, no. 5, pp. 1015–1106, 2012.
  66. R. Wille, R. Van Meter, and Y. Naveh, “Ibm’s qiskit tool chain: Working with and developing for real quantum computers,” in 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE).   IEEE, 2019, pp. 1234–1240.
  67. A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien, “A variational eigenvalue solver on a photonic quantum processor,” Nat. Commun., vol. 5, no. 4213, pp. 1–7, Jul. 2014.
  68. J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics, vol. 18, no. 2, p. 023023, 2016.
  69. E. Farhi, J. Goldstone, and S. Gutmann, “A quantum approximate optimization algorithm,” arXiv preprint arXiv:1411.4028, 2014.
  70. K. M. Nakanishi, K. Mitarai, and K. Fujii, “Subspace-search variational quantum eigensolver for excited states,” Physical Review Research, vol. 1, no. 3, p. 033062, 2019.
  71. S. Sim, P. D. Johnson, and A. Aspuru-Guzik, “Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms,” Advanced Quantum Technologies, vol. 2, no. 12, p. 1900070, 2019.
  72. M. Motta, C. Sun, A. T. Tan, M. J. O’Rourke, E. Ye, A. J. Minnich, F. G. Brandao, and G. K.-L. Chan, “Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution,” Nature Physics, vol. 16, no. 2, pp. 205–210, 2020.
  73. S. McArdle, T. Jones, S. Endo, Y. Li, S. C. Benjamin, and X. Yuan, “Variational ansatz-based quantum simulation of imaginary time evolution,” npj Quantum Inf., vol. 5, no. 75, pp. 1–6, Sep. 2019.
  74. T. H. Kyaw, M. B. Soley, B. Allen, P. Bergold, C. Sun, V. S. Batista, and A. Aspuru-Guzik, “Variational quantum iterative power algorithms for global optimization,” arXiv, Aug. 2022.
  75. G. Saxena, A. Shalabi, and T. H. Kyaw, “Practical limitations of quantum data propagation on noisy quantum processors,” arXiv, Jun. 2023.
  76. K. Kurowski, M. Slysz, M. Subocz, and R. Różycki, “Applying a quantum annealing based restricted boltzmann machine for mnist handwritten digit classification.” Computational Methods in Science & Technology, vol. 27, no. 3, 2021.
  77. P. Hauke, H. G. Katzgraber, W. Lechner, H. Nishimori, and W. D. Oliver, “Perspectives of quantum annealing: Methods and implementations,” Reports on Progress in Physics, vol. 83, no. 5, p. 054401, 2020.
  78. E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, “Quantum computation by adiabatic evolution,” arXiv preprint quant-ph/0001106, 2000.
  79. T. H. Kyaw, Y. Li, and L.-C. Kwek, “Measurement-Based Quantum Computation on Two-Body Interacting Qubits with Adiabatic Evolution,” Phys. Rev. Lett., vol. 113, no. 18, p. 180501, Oct. 2014.
  80. T. H. Kyaw and L.-C. Kwek, “Cluster state generation in one-dimensional Kitaev honeycomb model via shortcut to adiabaticity,” New J. Phys., vol. 20, no. 4, p. 045007, Apr. 2018.
  81. T. H. Kyaw, S. Allende, L.-C. Kwek, and G. Romero, “Parity-preserving light-matter system mediates effective two-body interactions,” Quantum Sci. Technol., vol. 2, no. 2, p. 025007, May 2017.
  82. W. van Dam, M. Mosca, and U. Vazirani, “How powerful is adiabatic quantum computation?” in Proceedings 42nd IEEE Symposium on Foundations of Computer Science.   IEEE, Oct. 2001, pp. 279–287.
  83. “QuEra.” [Online]. Available: https://www.quera.com/
  84. Y. Peng, J. Young, P. Liu, and X. Wu, “Simuq: A domain-specific language for quantum simulation with analog compilation,” arXiv preprint arXiv:2303.02775, 2023.
  85. R. Xia, T. Bian, and S. Kais, “Electronic structure calculations and the ising hamiltonian,” The Journal of Physical Chemistry B, vol. 122, no. 13, pp. 3384–3395, 2017.
  86. G. Li, L. Zhou, N. Yu, Y. Ding, M. Ying, and Y. Xie, “Projection-based runtime assertions for testing and debugging quantum programs,” Proceedings of the ACM on Programming Languages, vol. 4, no. OOPSLA, pp. 1–29, 2020.
  87. E. Boros and P. L. Hammer, “Pseudo-boolean optimization,” Discrete applied mathematics, vol. 123, no. 1-3, pp. 155–225, 2002.
  88. P. Gokhale, O. Angiuli, Y. Ding, K. Gui, T. Tomesh, M. Suchara, M. Martonosi, and F. T. Chong, “Optimization of simultaneous measurement for variational quantum eigensolver applications,” in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), 2020, pp. 379–390.
  89. J. M. Clary, E. B. Jones, D. Vigil-Fowler, C. Chang, and P. Graf, “Exploring the scaling limitations of the variational quantum eigensolver with the bond dissociation of hydride diatomic molecules,” International Journal of Quantum Chemistry, p. e27097, 2023.
  90. L. Mineh and A. Montanaro, “Accelerating the variational quantum eigensolver using parallelism,” Quantum Science and Technology, vol. 8, no. 3, p. 035012, 2023.
  91. J. R. McClean, M. E. Kimchi-Schwartz, J. Carter, and W. A. de Jong, “Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states,” Phys. Rev. A, vol. 95, no. 4, p. 042308, Apr. 2017.
  92. P. Smolensky, “Information processing in dynamical systems: Foundations of harmony theory,” 1986, department of Computer Science, University of Colorado, Boulder.
  93. G. E. Hinton, “Learning multiple layers of representation,” Trends in cognitive sciences, vol. 11, no. 10, pp. 428–434, 2007.
  94. B. Recht, C. Re, S. Wright, and F. Niu, “Hogwild!: A lock-free approach to parallelizing stochastic gradient descent,” Advances in neural information processing systems, vol. 24, 2011.
  95. S. Jiang, K. A. Britt, A. J. McCaskey, T. S. Humble, and S. Kais, “Quantum annealing for prime factorization,” Scientific reports, vol. 8, no. 1, p. 17667, 2018.
  96. S. A. Rahman, R. Lewis, E. Mendicelli, and S. Powell, “Su (2) lattice gauge theory on a quantum annealer,” Physical Review D, vol. 104, no. 3, p. 034501, 2021.
  97. “QPU-Specific Characteristics.” [Online]. Available: https://docs.dwavesys.com/docs/latest/_downloads/114d18bf1a9d2fd6fea1a16c89c30799/09-1262A-C_QPU_Properties_Advantage_system4_1.pdf
  98. J. Marshall, D. Venturelli, I. Hen, and E. G. Rieffel, “Power of pausing: Advancing understanding of thermalization in experimental quantum annealers,” Phys. Rev. Appl., vol. 11, p. 044083, Apr 2019. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevApplied.11.044083
  99. M. A. Carreira-Perpinan and G. Hinton, “On contrastive divergence learning,” in International workshop on artificial intelligence and statistics.   PMLR, 2005, pp. 33–40.
  100. E. T. Campbell, B. M. Terhal, and C. Vuillot, “Roads towards fault-tolerant universal quantum computation,” Nature, vol. 549, no. 7671, pp. 172–179, 2017.
  101. A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,” nature, vol. 549, no. 7671, pp. 242–246, 2017.
  102. H. Wang, Y. Ding, J. Gu, Y. Lin, D. Z. Pan, F. T. Chong, and S. Han, “Quantumnas: Noise-adaptive search for robust quantum circuits,” in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA).   IEEE, 2022, pp. 692–708.
  103. “IBM Quantum.” [Online]. Available: https://quantum-computing.ibm.com/lab/docs/iql/manage/systems/queue
  104. “IBM Quantum.” [Online]. Available: https://qiskit.org/ecosystem/ibm-runtime/sessions.html
  105. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The case for vm-based cloudlets in mobile computing,” IEEE pervasive Computing, vol. 8, no. 4, pp. 14–23, 2009.
  106. S. Stein, N. Wiebe, Y. Ding, P. Bo, K. Kowalski, N. Baker, J. Ang, and A. Li, “Eqc: ensembled quantum computing for variational quantum algorithms,” in Proceedings of the 49th Annual International Symposium on Computer Architecture, 2022, pp. 59–71.
  107. N. H. Stair and F. A. Evangelista, “Qforte: an efficient state simulator and quantum algorithms library for molecular electronic structure,” arXiv preprint arXiv:2108.04413, 2021.
  108. J. R. McClean, N. C. Rubin, K. J. Sung, I. D. Kivlichan, X. Bonet-Monroig, Y. Cao, C. Dai, E. S. Fried, C. Gidney, B. Gimby, P. Gokhale, T. Häner, T. Hardikar, V. Havlíček, O. Higgott, C. Huang, J. Izaac, Z. Jiang, X. Liu, S. McArdle, M. Neeley, T. O’Brien, B. O’Gorman, I. Ozfidan, M. D. Radin, J. Romero, N. P. D. Sawaya, B. Senjean, K. Setia, S. Sim, D. S. Steiger, M. Steudtner, Q. Sun, W. Sun, D. Wang, F. Zhang, and R. Babbush, “Openfermion: the electronic structure package for quantum computers,” Quantum Science and Technology, vol. 5, no. 3, p. 034014, 2020.
  109. J. S. Kottmann, S. Alperin-Lea, T. Tamayo-Mendoza, A. Cervera-Lierta, C. Lavigne, T.-C. Yen, V. Verteletskyi, P. Schleich, A. Anand, M. Degroote, S. Chaney, M. Kesibi, N. G. Curnow, B. Solo, G. Tsilimigkounakis, C. Zendejas-Morales, A. F. Izmaylov, and A. Aspuru-Guzik, “TEQUILA: a platform for rapid development of quantum algorithms,” Quantum Sci. Technol., vol. 6, no. 2, p. 024009, Mar. 2021.
  110. X.-Z. Luo, J.-G. Liu, P. Zhang, and L. Wang, “Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design,” Quantum, vol. 4, p. 341, Oct. 2020.
  111. T. Alexander, N. Kanazawa, D. J. Egger, L. Capelluto, C. J. Wood, A. Javadi-Abhari, and D. C. McKay, “Qiskit pulse: programming quantum computers through the cloud with pulses,” Quantum Science and Technology, vol. 5, no. 4, p. 044006, 2020.
  112. P. Gokhale, A. Javadi-Abhari, N. Earnest, Y. Shi, and F. T. Chong, “Optimized quantum compilation for near-term algorithms with openpulse,” in 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).   IEEE, 2020, pp. 186–200.
  113. T. Imoto, Y. Seki, Y. Matsuzaki, and S. Kawabata, “Guaranteed-accuracy quantum annealing,” Physical Review A, vol. 106, no. 4, p. 042615, 2022.
  114. T. Kopyciuk, M. Lewandowski, and P. Kurzyński, “Pre-and post-selection paradoxes in quantum walks,” New Journal of Physics, vol. 21, no. 10, p. 103054, 2019.
  115. J. T. Monroe, “Partial measurements of quantum systems,” Ph.D. dissertation, Washington University in St. Louis, 2021.
  116. F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. S. L. Brandao, D. A. Buell, B. Burkett, Y. Chen, Z. Chen, B. Chiaro, R. Collins, W. Courtney, A. Dunsworth, E. Farhi, B. Foxen, A. Fowler, C. Gidney, M. Giustina, R. Graff, K. Guerin, S. Habegger, M. P. Harrigan, M. J. Hartmann, A. Ho, M. Hoffmann, T. Huang, T. S. Humble, S. V. Isakov, E. Jeffrey, Z. Jiang, D. Kafri, K. Kechedzhi, J. Kelly, P. V. Klimov, S. Knysh, A. Korotkov, F. Kostritsa, D. Landhuis, M. Lindmark, E. Lucero, D. Lyakh, S. Mandrà, J. R. McClean, M. McEwen, A. Megrant, X. Mi, K. Michielsen, M. Mohseni, J. Mutus, O. Naaman, M. Neeley, C. Neill, M. Y. Niu, E. Ostby, A. Petukhov, J. C. Platt, C. Quintana, E. G. Rieffel, P. Roushan, N. C. Rubin, D. Sank, K. J. Satzinger, V. Smelyanskiy, K. J. Sung, M. D. Trevithick, A. Vainsencher, B. Villalonga, T. White, Z. J. Yao, P. Yeh, A. Zalcman, H. Neven, and J. M. Martinis, “Quantum supremacy using a programmable superconducting processor,” Nature, vol. 574, no. 7779, pp. 505–510, Oct 2019. [Online]. Available: https://doi.org/10.1038/s41586-019-1666-5
  117. H.-S. Zhong, H. Wang, Y.-H. Deng, M.-C. Chen, L.-C. Peng, Y.-H. Luo, J. Qin, D. Wu, X. Ding, Y. Hu, P. Hu, X.-Y. Yang, W.-J. Zhang, H. Li, Y. Li, X. Jiang, L. Gan, G. Yang, L. You, Z. Wang, L. Li, N.-L. Liu, C.-Y. Lu, and J.-W. Pan, “Quantum computational advantage using photons,” Science, vol. 370, no. 6523, pp. 1460–1463, Dec. 2020.
  118. Y. Wu, W.-S. Bao, S. Cao, F. Chen, M.-C. Chen, X. Chen, T.-H. Chung, H. Deng, Y. Du, D. Fan, M. Gong, C. Guo, C. Guo, S. Guo, L. Han, L. Hong, H.-L. Huang, Y.-H. Huo, L. Li, N. Li, S. Li, Y. Li, F. Liang, C. Lin, J. Lin, H. Qian, D. Qiao, H. Rong, H. Su, L. Sun, L. Wang, S. Wang, D. Wu, Y. Xu, K. Yan, W. Yang, Y. Yang, Y. Ye, J. Yin, C. Ying, J. Yu, C. Zha, C. Zhang, H. Zhang, K. Zhang, Y. Zhang, H. Zhao, Y. Zhao, L. Zhou, Q. Zhu, C.-Y. Lu, C.-Z. Peng, X. Zhu, and J.-W. Pan, “Strong Quantum Computational Advantage Using a Superconducting Quantum Processor,” Phys. Rev. Lett., vol. 127, no. 18, p. 180501, Oct. 2021.
  119. L. S. Madsen, F. Laudenbach, M. F. Askarani, F. Rortais, T. Vincent, J. F. F. Bulmer, F. M. Miatto, L. Neuhaus, L. G. Helt, M. J. Collins, A. E. Lita, T. Gerrits, S. W. Nam, V. D. Vaidya, M. Menotti, I. Dhand, Z. Vernon, N. Quesada, and J. Lavoie, “Quantum computational advantage with a programmable photonic processor,” Nature, vol. 606, no. 7912, pp. 75–81, Jun 2022. [Online]. Available: https://doi.org/10.1038/s41586-022-04725-x
  120. K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, W.-K. Mok, S. Sim, L.-C. Kwek, and A. Aspuru-Guzik, “Noisy intermediate-scale quantum algorithms,” Rev. Mod. Phys., vol. 94, no. 1, p. 015004, Feb. 2022.
  121. J. Zhang, T. H. Kyaw, S. Filipp, L.-C. Kwek, E. Sjöqvist, and D. Tong, “Geometric and holonomic quantum computation,” Phys. Rep., vol. 1027, pp. 1–53, Jul. 2023.
  122. A. Fedorov and M. Gelfand, “Towards practical applications in quantum computational biology,” Nature Computational Science, vol. 1, no. 2, pp. 114–119, 2021.
  123. R. P. Feynman, “Simulating physics with computers,” International Journal of Theoretical Physics, vol. 21, no. 6/7, 1982.
  124. T. Lubinski, S. Johri, P. Varosy, J. Coleman, L. Zhao, J. Necaise, C. H. Baldwin, K. Mayer, and T. Proctor, “Application-oriented performance benchmarks for quantum computing,” IEEE Transactions on Quantum Engineering, 2023.
  125. J. R. Finžgar, P. Ross, L. Hölscher, J. Klepsch, and A. Luckow, “Quark: A framework for quantum computing application benchmarking,” in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE).   IEEE, 2022, pp. 226–237.
  126. V. Shende, S. Bullock, and I. Markov, “Synthesis of quantum-logic circuits,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 25, no. 6, pp. 1000–1010, 2006.
  127. M. Amy, D. Maslov, M. Mosca, and M. Roetteler, “A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 32, no. 6, pp. 818–830, 2013.
  128. P. Das, S. Tannu, and M. Qureshi, “Jigsaw: Boosting fidelity of nisq programs via measurement subsetting,” in MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, 2021, pp. 937–949.
  129. G. S. Ravi, P. Gokhale, Y. Ding, W. Kirby, K. Smith, J. M. Baker, P. J. Love, H. Hoffmann, K. R. Brown, and F. T. Chong, “Cafqa: A classical simulation bootstrap for variational quantum algorithms,” in Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1, 2022, pp. 15–29.
  130. G. S. Ravi, K. Smith, J. M. Baker, T. Kannan, N. Earnest, A. Javadi-Abhari, H. Hoffmann, and F. T. Chong, “Navigating the dynamic noise landscape of variational quantum algorithms with qismet,” in Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, 2023, pp. 515–529.
  131. G. S. Ravi, K. N. Smith, P. Murali, and F. T. Chong, “Adaptive job and resource management for the growing quantum cloud,” in 2021 IEEE International Conference on Quantum Computing and Engineering (QCE).   IEEE, 2021, pp. 301–312.
  132. G. S. Ravi, K. N. Smith, P. Gokhale, and F. T. Chong, “Quantum computing in the cloud: Analyzing job and machine characteristics,” in 2021 IEEE International Symposium on Workload Characterization (IISWC).   IEEE, 2021, pp. 39–50.
  133. J. Yao, J. Wang, F. Yue, J. Xu, and Z. Shan, “Mtmc: A scheduling framework of multi-tasking mapping on multi-chips,” 2022. [Online]. Available: https://www.researchsquare.com/article/rs-1857463/latest.pdf
  134. M. Zhang, L. Xie, Z. Zhang, Q. Yu, G. Xi, H. Zhang, F. Liu, Y. Zheng, Y. Zheng, and S. Zhang, “Exploiting different levels of parallelism in the quantum control microarchitecture for superconducting qubits,” in MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, 2021, pp. 898–911.
  135. S. Niu and A. Todri-Sanial, “How parallel circuit execution can be useful for nisq computing?” in 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE).   IEEE, 2022, pp. 1065–1070.
  136. Y. Ohkura, T. Satoh, and R. Van Meter, “Simultaneous execution of quantum circuits on current and near-future nisq systems,” IEEE Transactions on Quantum Engineering, vol. 3, pp. 1–10, 2022.
  137. P. Das, S. S. Tannu, P. J. Nair, and M. Qureshi, “A case for multi-programming quantum computers,” in Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, pp. 291–303.
  138. L. Liu and X. Dou, “Qucloud: A new qubit mapping mechanism for multi-programming quantum computing in cloud environment,” in 2021 IEEE International symposium on high-performance computer architecture (HPCA).   IEEE, 2021, pp. 167–178.
  139. T. Huang, Y. Zhu, R. S. M. Goh, and T. Luo, “When quantum annealing meets multitasking: Potentials, challenges and opportunities,” Array, p. 100282, 2023.

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