Recognizing good variational quantum circuits with Monte Carlo Tree Search (original) (raw)
Abstract
Many investigators have recently turned to the study of quantum architecture search since it is laborious to manually design a high-performing quantum model and corresponding training strategies. For some tasks, it is more realistic in practice to search for a model architecture. In this paper, we introduce the Monte Carlo Tree Search algorithm which has achieved great success in classical neural architecture search to find good variational quantum circuits for two real-world tasks of ground state energy estimations and multimodal fusion. We adapt the Monte Carlo Tree Search to the quantum scenario by considering more sophisticated classifiers within the tree nodes to partition the search space into several subregions based on the model performance. The experimental results indicate that our proposed method has the ability to recognize good models from the vast search space in both tasks. More importantly, the discovered variational quantum circuits demonstrate their advantages in fusing multimodal features under the comprehensive consideration of parameter number and performance.
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Data Availability
The CMU-MOSI dataset used in this work can be accessed at https://github.com/zqcai19/Q-MCTS/tree/main/best_vqc/data.
Notes
References
- Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multiarmed bandit problem. Mach Learn 47:235–256
Article Google Scholar - Cerezo M, Sone A, Volkoff T et al (2020) Cost-function-dependent barren plateaus in shallow quantum neural networks. arXiv:2001.00550
- Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: a survey. J Mach Learn Res 20(1):1997–2017
MathSciNet Google Scholar - Grant E, Wossnig L, Ostaszewski M et al (2019) An initialization strategy for addressing barren plateaus in parametrized quantum circuits. Quantum 3:214
Article Google Scholar - Hou M, Tang J, Zhang J, et al (2019) Deep multimodal multilinear fusion with high-order polynomial pooling. Adv Neural Inf Process Syst 32
- Huang Y, Du C, Xue Z, et al (2021) What makes multi-modal learning better than single (provably). Adv Neural Inf Process Syst 34:10,944–10,956
- Liang PP, Lyu Y, Fan X, et al (2021) MultiBench: multiscale benchmarks for multimodal representation learning. In: Thirty-fifth conference on neural information processing systems datasets and benchmarks track (Round 1)
- Liu Z, Shen Y, Lakshminarasimhan VB, et al (2018) Efficient low-rank multimodal fusion with modality-specific factors. arXiv:1806.00064
- McClean JR, Boixo S, Smelyanskiy VN et al (2018) Barren plateaus in quantum neural network training landscapes. Nat Commun 9(1):4812
Article Google Scholar - Nguyen N, Chen KC (2022) Quantum embedding search for quantum machine learning. IEEE Access 10:41,444–41,456
- Ostaszewski M, Trenkwalder LM, Masarczyk W et al (2021) Reinforcement learning for optimization of variational quantum circuit architectures. Adv Neural Inf Process Syst 34:18,182–18,194
- Pérez-Salinas A, Cervera-Lierta A, Gil-Fuster E et al (2020) Data re-uploading for a universal quantum classifier. Quantum 4:226
Article Google Scholar - Pirhooshyaran M, Terlaky T (2021) Quantum circuit design search. Quantum Mach Intell 3:1–14
Article Google Scholar - Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79
Article Google Scholar - Rattew AG, Hu S, Pistoia M et al (2019) A domain-agnostic, noise-resistant, hardware-efficient evolutionary variational quantum eigensolver. arXiv:1910.09694
- Romero J, Babbush R, McClean JR, et al (2018) Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz. Quantum Sci Technol 4(1):014,008
- Schuld M, Sweke R, Meyer JJ (2021) Effect of data encoding on the expressive power of variational quantum-machine-learning models. Phys Rev A 103(3):032,430
- Silver D, Huang A, Maddison CJ et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484–489
- Tsai YHH, Bai S, Liang PP et al (2019) Multimodal transformer for unaligned multimodal language sequences. In: Proceedings of the conference. Association for Computational Linguistics. Meeting, pp 6558
- Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30
- Wang L, Zhao Y, Jinnai Y et al (2020) Neural architecture search using deep neural networks and Monte Carlo Tree Search. In: Proceedings of the AAAI conference on artificial intelligence, pp 9983–9991
- Wang L, Xie S, Li T et al (2021) Sample-efficient neural architecture search by learning actions for Monte Carlo Tree Search. IEEE Trans Pattern Anal Mach Intell 44(9):5503–5515
Google Scholar - Zadeh A, Zellers R, Pincus E et al (2016) MOSI: multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos. arXiv:1606.06259
- Zadeh A, Liang PP, Mazumder N et al (2018a) Memory fusion network for multi-view sequential learning. In: Proceedings of the AAAI conference on artificial intelligence
- Zadeh A, Liang PP, Poria S et al (2018b) Multi-attention recurrent network for human communication comprehension. In: Proceedings of the AAAI conference on artificial intelligence
- Zhang K, Liu L, Hsieh MH et al (2022a) Escaping from the barren plateau via Gaussian initializations in deep variational quantum circuits. Adv Neural Inf Process Syst 35:18,612–18,627
- Zhang SX, Hsieh CY, Zhang S et al (2022b) Differentiable quantum architecture search. Quantum Sci Technol 7(4):045,023
Funding
This work is supported by the National Natural Science Foundation of China under grants 61971143 and 62174035.
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Authors and Affiliations
- School of Microelectronics, Fudan University, Shanghai, China
Zhiqiang Cai & Lingli Wang - Institute of Big Data, Fudan University, Shanghai, China
Jialin Chen & Ke Xu
Authors
- Zhiqiang Cai
- Jialin Chen
- Ke Xu
- Lingli Wang
Contributions
Z. C., J. C., and K. X. conducted the experiments. Z. C. wrote the main manuscript. All authors reviewed the manuscript.
Corresponding author
Correspondence toJialin Chen.
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The authors declare no competing interests.
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Cai, Z., Chen, J., Xu, K. et al. Recognizing good variational quantum circuits with Monte Carlo Tree Search.Quantum Mach. Intell. 6, 36 (2024). https://doi.org/10.1007/s42484-024-00173-0
- Received: 25 October 2023
- Accepted: 17 June 2024
- Published: 27 June 2024
- Version of record: 27 June 2024
- DOI: https://doi.org/10.1007/s42484-024-00173-0