On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks

Yizhou Liu1, Weijie J. Su2, and Tongyang Li3,4

1Department of Engineering Mechanics, Tsinghua University, 100084 Beijing, China
2Department of Statistics and Data Science, University of Pennsylvania
3Center on Frontiers of Computing Studies, Peking University, 100871 Beijing, China
4School of Computer Science, Peking University, 100871 Beijing, China

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Classical algorithms are often not effective for solving nonconvex optimization problems where local minima are separated by high barriers. In this paper, we explore possible quantum speedups for nonconvex optimization by leveraging the $global$ effect of quantum tunneling. Specifically, we introduce a quantum algorithm termed the quantum tunneling walk (QTW) and apply it to nonconvex problems where local minima are approximately global minima. We show that QTW achieves quantum speedup over classical stochastic gradient descents (SGD) when the barriers between different local minima are high but thin and the minima are flat. Based on this observation, we construct a specific double-well landscape, where classical algorithms cannot efficiently hit one target well knowing the other well but QTW can when given proper initial states near the known well. Finally, we corroborate our findings with numerical experiments.

Classical algorithms are often not effective for solving nonconvex optimization problems where local minima are separated by high barriers. In this paper, we explore possible quantum speedups for nonconvex optimization by leveraging the global effect of quantum tunneling. Specifically, we introduce a quantum algorithm termed the quantum tunneling walk (QTW) and apply it to nonconvex problems where local minima are approximately global minima. We show that QTW achieves quantum speedup over classical stochastic gradient descents (SGD) when the barriers between different local minima are high but thin and the minima are flat. Based on this observation, we construct a specific double-well landscape, where classical algorithms cannot efficiently hit one target well knowing the other well but QTW can when given proper initial states near the known well. Finally, we corroborate our findings with numerical experiments.

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Cited by

[1] Dylan Herman, Cody Googin, Xiaoyuan Liu, Yue Sun, Alexey Galda, Ilya Safro, Marco Pistoia, and Yuri Alexeev, "Quantum computing for finance", Nature Reviews Physics 5 8, 450 (2023).

[2] Chenyi Zhang and Tongyang Li, "Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions", arXiv:2212.03906, (2022).

[3] Weiyuan Gong, Chenyi Zhang, and Tongyang Li, "Robustness of Quantum Algorithms for Nonconvex Optimization", arXiv:2212.02548, (2022).

[4] Aaron Sidford and Chenyi Zhang, "Quantum speedups for stochastic optimization", arXiv:2308.01582, (2023).

The above citations are from Crossref's cited-by service (last updated successfully 2023-09-22 21:33:03) and SAO/NASA ADS (last updated successfully 2023-09-22 21:33:04). The list may be incomplete as not all publishers provide suitable and complete citation data.