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Cited by 88Quantum Computing Algorithms and ArchitectureQuantum many-body systemsQuantum Information and Cryptography

Neural predictor based quantum architecture search

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Zhang, Shi-Xin, Hsieh, Chang-Yu, Zhang, Shengyu, Yao, Hong·2021·Machine Learning: Science and Technology
DOI 10.1088/2632-2153/ac28dd
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Abstract

Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum–classical hybrid computational paradigm in the near term. Both theoretical and practical developments of VQAs share many similarities with those of deep learning. For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning. Partly inspired by the recent success of AutoML and neural architecture search (NAS), quantum architecture search (QAS) is a collection of methods devised to engineer an optimal task-specific PQC. It has been proven that QAS-designed VQAs can outperform expert-crafted VQAs in various scenarios. In this work, we propose to use a neural network based predictor as the evaluation policy for QAS. We demonstrate a neural predictor guided QAS can discover powerful quantum circuit ansatz, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning. Notably, neural predictor guided QAS provides a better solution than that by the random-search baseline while using an order of magnitude less of circuit evaluations. Moreover, the predictor for QAS as well as the optimal ansatz found by QAS can both be transferred and generalized to address similar problems.

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  • Highly problem-specific ansätze to VQAs, not based on standard methods such as QAOA or VQE, and their effect on performance and resulting energy landscapes have been studied.

    “Highly problem-specific ansÃďtze to VQAs, which are not based on standard methods such as QAOA or VQE, and their effect on performance and resulting energy landscapes have been studied [83, 88, 151, 152].”

    From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [151]

    Section 2.3 describes searching for problem-tailored VQA ansätze and identifies the training loss landscape as a design consideration. Section 4.2 studies their performance using VQE and compares them with a QAOA-inspired baseline. The paper does not establish that the ansätze are outside VQE, nor does it report a study of their resulting energy landscapes. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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  1. Cerezo M, Arrasmith A, Babbush R, Benjamin S C, Endo S, Fujii K, McClean J R, Mitarai K, Yuan X, Cincio L, and Coles P J 2020 Variational quantum algorithms (arXiv:2012.09265) · arXiv
  2. Bharti K, Cervera-Lierta A, Kyaw T H, Haug T, Alperin-Lea S, Anand A, Degroote M, Heimonen H, Kottmann J S, Menke T, Mok W-K, Sim S, Kwek L-C and Aspuru-Guzik A 2021 Noisy intermediate-scale quantum (NISQ) algorithms (arXiv:2101.08448) · arXiv
  3. Grimsley H R, Economou S E, Barnes E and Mayhall N J 2019 An adaptive variational algorithm for exact molecular simulations on a quantum computer Nat. Commun. 10 3007 · DOI · Paper page
  4. Farhi E, Goldstone J and Gutmann S 2014 A quantum approximate optimization algorithm (arXiv:1411.4028) · arXiv
  5. Hadfield S, Wang Z, O'Gorman B, Rieffel E, Venturelli D and Biswas R 2019 From the quantum approximate optimization algorithm to a quantum alternating operator Ansatz Algorithms 12 34 · DOI · Paper page
  6. Arute F et al 2020 Quantum approximate optimization of non-planar graph problems on a planar superconducting processor (arXiv:2004.04197) · arXiv
  7. McClean J R, Harrigan M P, Mohseni M, Rubin N C, Jiang Z, Boixo S, Smelyanskiy V N, Babbush R and Neven H 2020 Low depth mechanisms for quantum optimization (arXiv:2008.08615) · arXiv
  8. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N and Lloyd S 2017 Quantum machine learning Nature 549 195 · DOI · Paper page
  9. Farhi E and Neven H 2018 Classification with quantum neural networks on near term processors (arXiv:1802.06002) · arXiv
  10. Mitarai K, Negoro M, Kitagawa M and Fujii K 2018 Quantum circuit learning Phys. Rev. A 98 032309 · DOI · Paper page
  11. Verdon G, Marks J, Nanda S, Leichenauer S, and Hidary J 2019 Quantum Hamiltonian-based models and the variational quantum thermalizer algorithm (arXiv:1910.02071) · arXiv
  12. Schuld M and Killoran N 2019 Quantum machine learning in feature Hilbert spaces Phys. Rev. Lett. 122 040504 · DOI · Paper page
  13. Benedetti M, Lloyd E, Sack S and Fiorentini M 2019 Parameterized quantum circuits as machine learning models Quantum Sci. Technol. 4 043001 · DOI
  14. Cong I, Choi S and Lukin M D 2019 Quantum convolutional neural networks Nat. Phys. 15 1273 · DOI · Paper page
  15. Sim S, Johnson P D and Aspuru-Guzik A 2019 Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms Adv. Quantum Technol. 2 1900070 · DOI · Paper page
  16. Farhi E, Gamarnik D, and Gutmann S 2020 The quantum approximate optimization algorithm needs to see the whole graph: a typical case (arXiv:2004.09002) · arXiv
  17. Akshay V, Philathong H, Morales M E S and Biamonte J D 2020 Reachability deficits in quantum approximate optimization Phys. Rev. Lett. 124 090504 · DOI · Paper page
  18. Rasmussen S E, Loft N J S, Bækkegaard T, Kues M, and Zinner N T 2020 Single-qubit rotations in parameterized quantum circuits (arXiv:2005.13548) · arXiv
  19. Akshay V, Philathong H, Zacharov I, and Biamonte J 2020 Reachability deficits implicit in Google's quantum approximate optimization of graph problems (arXiv:2007.09148) · arXiv
  20. Yao Q, Wang M, Chen Y, Dai W, Li Y-F, Tu W-W, Yang Q, and Yu Y 2018 Taking human out of learning applications: a survey on automated machine learning (arXiv:1810.13306) · arXiv
  21. Wistuba M, Rawat A, and Pedapati T, 2019 A survey on neural architecture search (arXiv:1905.01392) · arXiv
  22. Ren P, Xiao Y, Chang X, Huang P-Y, Li Z, Chen X, and Wang X, Comprehensive A 2020 Survey of neural architecture search: challenges and solutions (arXiv:2006.02903) · arXiv
  23. Zhang S-X, Hsieh C-Y, Zhang S, and Yao H 2020 Differentiable quantum architecture search (arXiv:2010.08561) · arXiv
  24. Las Heras U, Alvarez-Rodriguez U, Solano E and Sanz M 2016 Genetic algorithms for digital quantum simulations Phys. Rev. Lett. 116 230504 · DOI
  25. Li R, Alvarez-Rodriguez U, Lamata L and Solano E 2017 Approximate quantum adders with genetic algorithms: an IBM quantum experience Quantum Meas. Quantum Metrol. 4 1 · DOI
  26. Rattew A G, Hu S, Pistoia M, Chen R, and Wood S, Domain-agnostic A 2019 Noise-resistant, hardware-efficient evolutionary variational quantum Eigensolver (arXiv:1910.09694) · arXiv
  27. Cincio L, Rudinger K, Sarovar M and Coles P J 2020 Machine learning of noise-resilient quantum circuits (arXiv:2007.01210) · arXiv
  28. Chivilikhin D, Samarin A, Ulyantsev V, Iorsh I, Oganov A R, and Kyriienko O 2020 MoG-VQE: multiobjective genetic variational quantum eigensolver (arXiv:2007.04424) · arXiv
  29. Lu Z, Shen P-X, and Deng D-L 2020 Markovian quantum neuroevolution for machine learning (arXiv:2012.15131) · arXiv
  30. Niu M Y, Boixo S, Smelyanskiy V N and Neven H 2019 Universal quantum control through deep reinforcement learning npj Quantum Inf. 5 33 · DOI
  31. Ostaszewski M, Grant E, and Benedetti M 2019 Quantum circuit structure learning (arXiv:1905.09692) · arXiv
  32. Tang H L, Shkolnikov V O, Barron G S, Grimsley H R, Mayhall N J, Barnes E, and Economou S E 2019 Qubit-ADAPT-VQE: an adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor (arXiv:1911.10205) · arXiv
  33. Bender G, Kindermans P J, Zoph B, Vasudevan V and Le Q 2018 Understanding and simplifying one-shot architecture search ICML 883
  34. Luo R, Qin T, and Chen E 2019 Balanced one-shot neural architecture optimization (arXiv:1909.10815) · arXiv
  35. Guo Z, Zhang X, Mu H, Heng W, Liu Z, Wei Y and Sun J 2020 Single path one-shot neural architecture search with uniform sampling ECCV 544
  36. Liu H, Simonyan K and Yang Y 2019 DARTS: differentiable architecture search ICLR
  37. Du Y, Huang T, You S, Hsieh M-H, and Tao D 2020 Quantum circuit architecture search: error mitigation and trainability enhancement for variational quantum solvers (arXiv:2010.10217) · arXiv
  38. Deng B, Yan J, and Lin D 2017 Peephole: predicting network performance before training (arXiv:1712.03351) · arXiv
  39. Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li L J, Fei-Fei L, Yuille A, Huang J, and Murphy K 2017 Progressive neural architecture search (arXiv:1712.00559) · arXiv
  40. Wang L, Zhao Y, Jinnai Y, and Fonseca R 2019 AlphaX: eXploring neural architectures with deep neural networks and Monte Carlo tree search (arXiv:1903.11059) · arXiv
  41. White C, Ai R and Neiswanger W 2019 Neural architecture search via Bayesian optimization with a neural network model NeurIPS
  42. Shi H, Pi R, Xu H, Li Z, Kwok J T and Zhang T 2019 Bridging the Gap between sample-based and one-shot neural architecture search with BONAS NeurIPS
  43. Wen W, Liu H, Chen Y, Li H, Bender G and Kindermans P J 2020 Neural Predictor for Neural Architecture Search Lect. Notes Comput. Sci. Eng. 12374 660 · DOI
  44. Ning X, Zheng Y, Zhao T, Wang Y, and Yang H 2020 A generic graph-based neural architecture encoding scheme for predictor-based NAS (arXiv:2004.01899) · arXiv
  45. Chen X, Xie L, Wu J, Wei L, Xu Y, and Tian Q 2020 Fitting the search space of weight-sharing nas with graph convolutional networks (arXiv:2004.08423) · arXiv
  46. White C, Neiswanger W, Nolen S, and Savani Y 2020 A study on encodings for neural architecture search (arXiv:2007.04965) · arXiv
  47. Lukasik J, Friede D, Stuckenschmidt H, and Keuper M 2020 Neural architecture performance prediction using graph neural networks (arXiv:2010.10024) · arXiv
  48. Dudziak Ł, Chau T, Abdelfattah M S, Lee R, Kim H, and Lane N D 2020 BRP-NAS: prediction-based NAS using GCNs (arXiv:2007.08668) · arXiv
  49. Mauch L, Tiedemann S, Garcia J A, Cong B N, Yoshiyama K, Cardinaux F, and Kemp T 2020 Efficient sampling for predictor-based neural architecture search (arXiv:2011.12043) · arXiv
  50. Li L and Talwalkar A 2019 Random search and reproducibility for neural architecture search (arXiv:1902.07638) · arXiv
  51. Huang S, Li X, Cheng Z-Q, Zhang Z, and Hauptmann A 2018 GNAS: a greedy neural architecture search method for multi-attribute learning (arXiv:1804.06964) · arXiv
  52. White C, Nolen S, and Savani Y 2020 Local search is state of the art for NAS benchmarks (arXiv:2005.02960) · arXiv
  53. Li G, Qian G, Delgadillo I C, Müller M, Thabet A, and Ghanem B 2019 SGAS: sequential greedy architecture search (arXiv:1912.00195) · arXiv
  54. Real E, Moore S, Selle A, Saxena S, Suematsu Y L, Tan J, Le Q V and Kurakin A 2017 Large-scale evolution of image classifiers ICML 2902
  55. Xie L and Yuille A 2017 Genetic CNN ICCV 1388
  56. Liu H, Simonyan K, Vinyals O, Fernando C and Kavukcuoglu K 2018 Hierarchical representations for efficient architecture search ICLR
  57. Real E, Aggarwal A, Huang Y and Le Q V 2019 Regularized evolution for image classifier architecture search AAAI 4780 · DOI · Paper page
  58. Stanley K O, Clune J, Lehman J and Miikkulainen R 2019 Designing neural networks through neuroevolution Nat. Mach. Intell. 1 24 · DOI
  59. Zoph B and Le Q V 2017 Neural architecture search with reinforcement learning ICLR
  60. Baker B, Gupta O, Naik N and Raskar R 2017 Designing neural network architectures using reinforcement learning ICLR
  61. Cai H, Chen T, Zhang W, Yu Y and Wang J 2018 Efficient architecture search by network transformation AAAI 2787
  62. Zoph B, Vasudevan V, Shlens J and Le Q V 2018 Learning transferable architectures for scalable image recognition CVPR 8697
  63. Casale F P, Gordon J, and Fusi N 2019 Probabilistic neural architecture search (arXiv:1902.05116) · arXiv
  64. Cai H, Zhu L and Han S 2019 Proxyless nas: direct neural architecture search on target task and hardware ICLR
  65. Kingma D P and Welling M 2014 Auto-encoding variational Bayes ICLR
  66. Luo R, Tian F, Qin T, Chen E and Liu T Y 2018 Neural architecture optimization NeurIPS 7816
  67. Zhang M, Jiang S, Cui Z, Garnett R and Chen Y 2019 D-VAE: A variational autoencoder for directed acyclic graphs NeurIPS
  68. Friede D, Lukasik J, Stuckenschmidt H, and Keuper M 2019 A variational-sequential graph autoencoder for neural architecture performance prediction (arXiv:1912.05317) · arXiv
  69. Cheng H P, Zhang T, Li S, Yan F, Li M, Chandra V, Li H, and Chen Y 2020 NASGEM: neural architecture search via graph embedding method (arXiv:2007.04452) · arXiv
  70. Li J, Liu Y, Liu J, and Wang W 2020 Neural architecture optimization with graph VAE (arXiv:2006.10310) · arXiv
  71. Zheng X, Wang P, Wang Q, and Shi Z 2020 Disentangled neural architecture search (arXiv:2009.13226) · arXiv
  72. Tang Y, Wang Y, Xu Y, Chen H, Shi B, Xu C, Xu C, Tian Q and Xu C 2020 A semi-supervised assessor of neural architectures CVPR 1807
  73. Cao Y et al 2019 Quantum chemistry in the age of quantum computing Chem. Rev. 119 10856 · DOI · Paper page
  74. Hsieh C Y, Sun Q, Zhang S and Lee C K 2021 Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations npj Quantum Inf. 7 19 · DOI
  75. Yuan X, Endo S, Zhao Q, Li Y and Benjamin S C 2019 Theory of variational quantum simulation Quantum 3 191 · DOI · Paper page
  76. McArdle S, Jones T, Endo S, Li Y, Benjamin S C and Yuan X 2019 Variational ansatz-based quantum simulation of imaginary time evolution npj Quantum Inf. 5 75 · DOI · Paper page
  77. Lin S-H, Dilip R, Green A G, Smith A, and Pollmann F 2020 Real-and imaginary-time evolution with compressed quantum circuits (arXiv:2008.10322) · arXiv
  78. Lau J W Z, Bharti K, Haug T, and Kwek L C 2021 Quantum assisted simulation of time dependent Hamiltonians (arXiv:2101.07677) · arXiv
  79. Endo S, Sun J, Li Y, Benjamin S C and Yuan X 2020 Variational quantum simulation of general processes Phys. Rev. Lett. 125 010501 · DOI · Paper page
  80. Benedetti M, Fiorentini M, and Lubasch M 2020 Hardware-efficient variational quantum algorithms for time evolution (arXiv:2009.12361) · arXiv
  81. Bharti K and Haug T 2020 Quantum assisted simulator (arXiv:2011.06911) · arXiv
  82. Barison S, Vicentini F, and Carleo G 2021 An efficient quantum algorithm for the time evolution of parameterized circuits (arXiv:2101.04579) · arXiv
  83. Wang Y, Li G, and Wang X 2020 Variational quantum Gibbs state preparation with a truncated Taylor series (arXiv:2005.08797) · arXiv
  84. Chowdhury A N, Low G H, and Wiebe N 2020 A variational quantum algorithm for preparing quantum Gibbs states (arXiv:2002.00055) · arXiv
  85. Heya K, Suzuki Y, Nakamura Y, and Fujii K 2018 Variational quantum gate optimization (arXiv:1810.12745) · arXiv
  86. Saxena A, Shukla A, and Pathak A 2020 A hybrid scheme for prime factorization and its experimental implementation using IBM quantum processor (arXiv:2009.05840) · arXiv
  87. Karamlou A H, Simon W A, Katabarwa A, Scholten T L, Peropadre B, and Cao Y 2020 Analyzing the performance of variational quantum factoring on a superconducting quantum processor (arXiv:2012.07825) · arXiv
  88. Huang H Y, Bharti K, and Rebentrost P 2019 Near-term quantum algorithms for linear systems of equations (arXiv:1909.07344) · arXiv
  89. Xu X, Sun J, Endo S, Li Y, Benjamin S C, and Yuan X 2019 Variational algorithms for linear algebra (arXiv:1909.03898) · arXiv
  90. Bravo-Prieto C, LaRose R, Cerezo M, Subasi Y, Cincio L, and Coles P J 2019 Variational quantum linear solver (arXiv:1909.05820) · arXiv
  91. Kyriienko O, Paine A E, and Elfving V E 2020 Solving nonlinear differential equations with differentiable quantum circuits (arXiv:2011.10395) · arXiv
  92. McClean J R, Boixo S, Smelyanskiy V N, Babbush R and Neven H 2018 Barren plateaus in quantum neural network training landscapes Nat. Commun. 9 4812 · DOI · Paper page
  93. Wang S, Fontana E, Cerezo M, Sharma K, Sone A, Cincio L, and Coles P J 2020 Noise-induced Barren plateaus in variational quantum algorithms (arXiv:2007.14384) · arXiv
  94. Temme K, Bravyi S and Gambetta J M 2017 Error mitigation for short-depth quantum circuits Phys. Rev. Lett. 119 180509 · DOI · Paper page
  95. Patti T L, Najafi K, Gao X, and Yelin S F 2020 Entanglement devised Barren plateau mitigation (arXiv:2012.12658) · arXiv
  96. Cerezo M, Sone A, Volkoff T, Cincio L, and Coles P J 2020 Cost-function-dependent barren plateaus in shallow quantum neural networks (arXiv:2001.00550) · arXiv
  97. Franca D S and Garcia-Patron R 2020 Limitations of optimization algorithms on noisy quantum devices (arXiv:2009.05532) · arXiv
  98. Pirhooshyaran M and Terlaky T 2020 Quantum circuit design search, (arXiv:2012.04046) · arXiv
  99. Zlokapa A and Gheorghiu A 2020 A deep learning model for noise prediction on near-term quantum devices (arXiv:2005.10811) · arXiv
  100. Xiao H, Rasul K, and Vollgraf R 2017 Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms (arXiv:1708.07747) · arXiv
  101. Huang H Y, Kueng R and Preskill J 2020 Predicting many properties of a quantum system from very few measurements Nat. Phys. 16 1050 · DOI · Paper page
  102. Li G, Song Z, and Wang X 2020 VSQL: variational shadow quantum learning for classification (arXiv:2012.08288) · arXiv
  103. Broughton M, Verdon G, McCourt T, Martinez A J, Yoo J H, Isakov S V, Massey P, Niu M Y, Halavati R, Peters E, Leib M, Skolik A, Streif M, Dollen D V, McClean J R, Boixo S, Bacon D, Ho A K, Neven H, and Mohseni M 2020 TensorFlow quantum: a software framework for quantum machine learning (arXiv:2003.02989) · arXiv

Cited by

Sorted by citations

Authors
Zhang, S.-X., Hsieh, C.-Y., Zhang, S., & Yao, H.
Affiliations
  • Tencent (China) (CN)
  • Tsinghua University (CN)
ORCID
Venue
Machine Learning: Science and Technology
Publisher
IOP Publishing
Published
2021-10-08
Volume
2
Issue
4
Pages
045027
ISSN
2632-2153 (ISSN-L)
DOI
10.1088/2632-2153/ac28dd
OpenAlex
W3134249575
Cited by
88
References
29
Citations by year
2026: 8 · 2025: 22 · 2024: 30 · 2023: 17 · 2022: 7 · 2021: 4
FWCI
5.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Machine Learning Science and Technology (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (11825404)
  • Strategic Priority Research Program of Chinese Academy of Sciences (XDB28000000)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
Keywords
quantum architecture search, neural architecture search, variational quantum algorithms, variational quantum circuits, neural predictors, quantum machine learning, quantum simulation, automated machine learning, transfer learning, generalization

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