Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Quantum Architecture Search with Unsupervised Representation Learning (2401.11576v3)

Published 21 Jan 2024 in quant-ph and cs.LG

Abstract: Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) on Noisy Intermediate-Scale Quantum (NISQ) devices. QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs). Most QAS algorithms tightly couple the search space and search algorithm, typically requiring the evaluation of numerous quantum circuits, resulting in high computational costs and limiting scalability to larger quantum circuits. Predictor-based QAS algorithms mitigate this issue by estimating circuit performance based on structure or embedding. However, these methods often demand time-intensive labeling to optimize gate parameters across many circuits, which is crucial for training accurate predictors. Inspired by the classical neural architecture search algorithm Arch2vec, we investigate the potential of unsupervised representation learning for QAS without relying on predictors. Our framework decouples unsupervised architecture representation learning from the search process, enabling the learned representations to be applied across various downstream tasks. Additionally, it integrates an improved quantum circuit graph encoding scheme, addressing the limitations of existing representations and enhancing search efficiency. This predictor-free approach removes the need for large labeled datasets. During the search, we employ REINFORCE and Bayesian Optimization to explore the latent representation space and compare their performance against baseline methods. Our results demonstrate that the framework efficiently identifies high-performing quantum circuits with fewer search iterations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (58)
  1. A case study of variational quantum algorithms for a job shop scheduling problem. EPJ Quantum Technology, 9(1), feb 2022. doi: 10.1140/epjqt/s40507-022-00123-4.
  2. Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4):043001, 2019.
  3. Variational quantum algorithms. Nature Reviews Physics, 3(9):625–644, 2021.
  4. Li Ding and Lee Spector. Evolutionary quantum architecture search for parametrized quantum circuits. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp.  2190–2195, 2022.
  5. Quantum circuit architecture search for variational quantum algorithms. npj Quantum Information, 8(1):62, 2022.
  6. Quantum neural architecture search with quantum circuits metric and bayesian optimization. arXiv preprint arXiv:2206.14115, 2022.
  7. Qneat: Natural evolution of variational quantum circuit architecture. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp.  647–650, 2023.
  8. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  855–864, 2016.
  9. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017.
  10. Gsqas: Graph self-supervised quantum architecture search. arXiv preprint arXiv:2303.12381, 2023a.
  11. A gnn-based predictor for quantum architecture search. Quantum Information Processing, 22(2):128, 2023b.
  12. A2c is a special case of ppo, 2022.
  13. Learning graph representations of biochemical networks and its application to enzymatic link prediction. Bioinformatics, 37(6):793–799, 2021.
  14. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016.
  15. Quantum architecture search via deep reinforcement learning. arXiv preprint arXiv:2104.07715, 2021.
  16. AA Leman and Boris Weisfeiler. A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsiya, 2(9):12–16, 1968.
  17. Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information. Pattern Recognition, 113:107824, 2021.
  18. Quantum fidelity measures for mixed states. Reports on Progress in Physics, 82(7):076001, 2019.
  19. Clustering with t-sne, provably. SIAM Journal on Mathematics of Data Science, 1(2):313–332, 2019.
  20. Variational quantum circuit model for knowledge graph embedding. Advanced Quantum Technologies, 2(7-8):1800078, 2019.
  21. Barren plateaus in quantum neural network training landscapes. Nature communications, 9(1):4812, 2018.
  22. Quantum circuit learning. Physical Review A, 98(3):032309, 2018.
  23. Jonas Mockus. On bayesian methods for seeking the extremum and their application. In IFIP Congress, pp.  195–200, 1977.
  24. Mark EJ Newman. The structure of scientific collaboration networks. Proceedings of the national academy of sciences, 98(2):404–409, 2001.
  25. Reinforcement learning for optimization of variational quantum circuit architectures. Advances in Neural Information Processing Systems, 34:18182–18194, 2021.
  26. Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407, 2018.
  27. Graph representation learning via graphical mutual information maximization. In Proceedings of The Web Conference 2020, pp.  259–270, 2020.
  28. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  701–710, 2014.
  29. A variational eigenvalue solver on a photonic quantum processor. Nature communications, 5(1):4213, 2014.
  30. Solving the max-cut problem using eigenvalues. Discrete Applied Mathematics, 62(1-3):249–278, 1995.
  31. A quantum-classical hybrid method for image classification and segmentation. In 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC), pp.  450–455, 2022. doi: 10.1109/SEC54971.2022.00068.
  32. John Preskill. Quantum computing in the nisq era and beyond. Quantum, 2:79, 2018.
  33. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
  34. Proximal policy optimization algorithms, 2017.
  35. Uniskgrep: A unified representation learning framework of social network and knowledge graph. Neural Networks, 158:142–153, 2023.
  36. Jonathon Shlens. A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100, 2014.
  37. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2(12):1900070, 2019.
  38. Quantum agents in the gym: a variational quantum algorithm for deep q-learning. Quantum, 6:720, 2022.
  39. Scalable bayesian optimization using deep neural networks. In International conference on machine learning, pp.  2171–2180. PMLR, 2015.
  40. Limitations of optimization algorithms on noisy quantum devices. Nature Physics, 17(11):1221–1227, 2021.
  41. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000, 2019.
  42. qubit-adapt-vqe: An adaptive algorithm for constructing hardware-efficient ansätze on a quantum processor. PRX Quantum, 2(2):020310, 2021.
  43. Line: Large-scale information network embedding. In Proceedings of the 24th international conference on world wide web, pp.  1067–1077, 2015.
  44. The variational quantum eigensolver: a review of methods and best practices. Physics Reports, 986:1–128, 2022.
  45. Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.
  46. Deep graph infomax. arXiv preprint arXiv:1809.10341, 2018.
  47. Improvement of quantum approximate optimization algorithm for max–cut problems. Sensors, 22(1):244, 2021.
  48. Structural deep network embedding. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.  1225–1234, 2016.
  49. Noise-induced barren plateaus in variational quantum algorithms. Nature communications, 12(1):6961, 2021.
  50. Review of quantum image processing. Archives of Computational Methods in Engineering, 29(2):737–761, 2022.
  51. R. J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning Journal, 8:229–256, 1992.
  52. How powerful are graph neural networks?, 2019.
  53. Does unsupervised architecture representation learning help neural architecture search? Advances in neural information processing systems, 33:12486–12498, 2020.
  54. Unsupervised possibilistic clustering. Pattern Recognition, 39(1):5–21, 2006.
  55. Evolutionary-based quantum architecture search. arXiv preprint arXiv:2212.00421, 2022.
  56. D-vae: A variational autoencoder for directed acyclic graphs. Advances in Neural Information Processing Systems, 32, 2019.
  57. Neural predictor based quantum architecture search. Machine Learning: Science and Technology, 2(4):045027, 2021.
  58. Differentiable quantum architecture search. Quantum Science and Technology, 7(4):045023, 2022.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Yize Sun (13 papers)
  2. Zixin Wu (3 papers)
  3. Yunpu Ma (57 papers)
  4. Volker Tresp (158 papers)
Citations (5)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now