Boyang Chen

Boyang Chen

I'm a PhD student at Tsinghua University

Publications

Error interference in quantum simulation

Published in Arxiv preprint, 2024

Understanding algorithmic error accumulation in quantum simulation is crucial due to its fundamental significance and practical applications in simulating quantum many-body system dynamics. Conventional theories typically apply the triangle inequality to provide an upper bound for the error. However, these often yield overly conservative and inaccurate estimates as they neglect error interference – a phenomenon where errors in different segments can destructively interfere. Here, we introduce a novel method that directly estimates the long-time algorithmic errors with multiple segments, thereby establishing a comprehensive framework for characterizing algorithmic error interference. We identify the sufficient and necessary condition for strict error interference and introduce the concept of approximate error interference, which is more broadly applicable to scenarios such as power-law interaction models, the Fermi-Hubbard model, and higher-order Trotter formulas. Our work demonstrates significant improvements over prior ones and opens new avenues for error analysis in quantum simulation, offering potential advancements in both theoretical algorithm design and experimental implementation of Hamiltonian simulation.

Recommended citation: Chen, B., Xu, J., Zhao, Q., & Yuan, X. (2024). Error Interference in Quantum Simulation. arXiv preprint arXiv:2411.03255. https://arxiv.org/pdf/2411.03255.pdf

Oracle separation between quantum commitments and quantum one-wayness

Published in Accepted by Eurocrypt 2025, 2024

We show that there exists a unitary quantum oracle relative to which quantum commitments exist but no (efficiently verifiable) one-way state generators exist. Both have been widely considered candidates for replacing one-way functions as the minimal assumption for cryptography: the weakest cryptographic assumption implied by all of computational cryptography. Recent work has shown that commitments can be constructed from one-way state generators, but the other direction has remained open. Our results rule out any black-box construction, and thus settle this crucial open problem, suggesting that quantum commitments (as well as its equivalency class of EFI pairs, quantum oblivious transfer, and secure quantum multiparty computation) appear to be strictly weakest among all known cryptographic primitives.

Recommended citation: Bostanci, J., Chen, B., & Nehoran, B. (2024). Oracle separation between quantum commitments and quantum one-wayness. arXiv preprint arXiv:2410.03358. https://arxiv.org/pdf/2410.03358.pdf

The power of a single Haar random state: constructing and separating quantum pseudorandomness

Published in Accepted by Eurocrypt 2025, 2024

Quantum measurements can produce randomness arising from the uncertainty principle. When measuring a state with von Neumann measurements, the intrinsic randomness can be quantified by the quantum coherence of the state on the measurement basis. Unlike projection measurements, there are additional and possibly hidden degrees of freedom in apparatus for generic measurements. We propose an adversary scenario for general measurements with arbitrary input states, based on which, we characterize the intrinsic randomness. Interestingly, we discover that under certain measurements, such as the symmetric and information-complete measurement, all states have nonzero randomness, inspiring a new design of source-independent random number generators without state characterization. Furthermore, our results show that intrinsic randomness can quantify coherence under general measurements, which generalizes the result in the standard resource theory of state coherence.

Recommended citation: Chen, B., Coladangelo, A., & Sattath, O. (2024). The power of a single Haar random state: constructing and separating quantum pseudorandomness. arXiv preprint arXiv:2404.03295. https://arxiv.org/pdf/2404.03295.pdf

Intrinsic randomness under general quantum measurements

Published in Phys. Rev. Research, 2023

Quantum measurements can produce randomness arising from the uncertainty principle. When measuring a state with von Neumann measurements, the intrinsic randomness can be quantified by the quantum coherence of the state on the measurement basis. Unlike projection measurements, there are additional and possibly hidden degrees of freedom in apparatus for generic measurements. We propose an adversary scenario for general measurements with arbitrary input states, based on which, we characterize the intrinsic randomness. Interestingly, we discover that under certain measurements, such as the symmetric and information-complete measurement, all states have nonzero randomness, inspiring a new design of source-independent random number generators without state characterization. Furthermore, our results show that intrinsic randomness can quantify coherence under general measurements, which generalizes the result in the standard resource theory of state coherence.

Recommended citation: Dai, H., Chen, B., Zhang, X., & Ma, X. (2023). Intrinsic randomness under general quantum measurements. Physical Review Research, 5(3), 033081. https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.5.033081

Analyzing convergence in quantum neural networks: Deviations from neural tangent kernels

Published in ICML 2023, 2023

A quantum neural network (QNN) is a parameterized mapping efficiently implementable on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. It can be used for supervised learning when combined with classical gradient-based optimizers. Despite the existing empirical and theoretical investigations, the convergence of QNN training is not fully understood. Inspired by the success of the neural tangent kernels (NTKs) in probing into the dynamics of classical neural networks, a recent line of works proposes to study over-parameterized QNNs by examining a quantum version of tangent kernels. In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization. As a result of the deviation, we prove the at-most sublinear convergence for QNNs with Pauli measurements, which is beyond the explanatory power of any kernel regression dynamics. We then present the actual dynamics of QNNs in the limit of over-parameterization. The new dynamics capture the change of convergence rate during training and implies that the range of measurements is crucial to the fast QNN convergence.

Recommended citation: You, X., Chakrabarti, S., Chen, B., & Wu, X. (2023, July). Analyzing convergence in quantum neural networks: Deviations from neural tangent kernels. In International Conference on Machine Learning (pp. 40199-40224). PMLR. https://proceedings.mlr.press/v202/you23a.html