In the noisy intermediate-scale quantum (NISQ) era, an important goal is to achieve quantum advantage in information processing tasks. Among the candidates, variational quantum circuits (VQCs) are a class of quantum-classical hybrid systems applicable to various tasks, including optimization, state preparation, quantum simulation and machine learning. Our goal is to utilize VQCs to enable more efficient computing and sensing tasks. In the past, we considered VQC systems in the optical domain and utilize VQC to generate multipartite entanglement to benefit physical-domain data classification tasks [1,2]. A Viewpoint article in Physics [Physics 14, 79 (2021)] highlights our accomplishments: “The work paves the way for a broad range of quantum-enhanced classification methods that could be enabled by near-future quantum technologies”. Our more recent focus has been on qubit-based VQCs, where computation problems such as optimization can be solved. Our initial work explores the depth-efficiency effect of VQC in its discriminative power [3]. We also consider quantum approximate optimization algorithm (QAOA) in solving NP-complete problems such as 3-SAT [4].
Recent publications:
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[1] Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network, Q. Zhuang and Z. Zhang, Phys. Rev. X 9, 041023 (2019).
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[2] Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network, Y. Xia, W. Li, Q. Zhuang, and Z. Zhang, Phys. Rev. X 11, 021047 (2021).
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[3] Fast decay of classification error in variational quantum circuits, B. Zhang and Q. Zhuang, Quantum Sci. Technol. 7 035017 (2022).
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[4] Quantum computational phase transition in combinatorial problems, B. Zhang A. Sone and Q. Zhuang, npj Quantum Inf 8, 87 (2022).