As established by Claude Shannon, the communication rate over a channel is limited by the channel capacity. Entanglement can break the classical limits of communication over noisy channels as proposed by Bennett et al. in 2002. Yet experimental verification of this phenomena was missing until 2020–2021 when experimental collaborators and my group jointly proposed and verified the first entanglement-assisted communication protocol that beats the classical capacity of traditional communication schemes without entanglement [1,2]. Our efforts now focus on the network generalization of such protocols, including the initial results on a multi-access channel [3] and more general network communication scenarios.

Recent publications:

• [1] Practical Route to Entanglement-Assisted Communication Over Noisy Bosonic Channels, H. Shi, Z. Zhang, and Q. Zhuang, Phys. Rev. Appl. 13, 034029 (2020).

• [2] Entanglement-Assisted Communication Surpassing the Ultimate Classical Capacity, S. Hao, H. Shi, W. Li, J. H. Shapiro, Q. Zhuang, and Z. Zhang, Phys. Rev. Lett. 126, 250501 (2021).

• [3] Entanglement-Assisted Capacity Regions and Protocol Designs for Quantum Multiple-Access Channels, H. Shi, M.-H. Hsieh, S. Guha, Z. Zhang, and Q. Zhuang, Npj Quantum Inf. 7, 74 (2021).

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:

• [1] Physical-Layer Supervised Learning Assisted by an Entangled Sensor Network, Q. Zhuang and Z. Zhang, Phys. Rev. X 9, 041023 (2019).

• [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).

• [3] Fast Suppression of Classification Error in Variational Quantum Circuits, B. Zhang and Q. Zhuang, ArXiv 2107.08026 (2021).

• [4] Computational Phase Transition in Quantum Approximate Optimization Algorithm -- the Difference between Hard and Easy, B. Zhang A. Sone and Q. Zhuang, ArXiv 2109.13346 (2021).

Distributed quantum sensing (DQS) enhances the measurement precision of global parameters in a sensor network via multipartite entanglement. Our group led the initial design of DQS in the optical domain [1], and provided the theory support for the first experimental demonstration of a reconfigurable entangled sensor network [2]. To cope with noise and loss, my group and collaborators have proposed using continuous-variable quantum error correction to assist DQS systems that operate under noise [3]. See our recent invited topical review for Quantum Science and Technology [4] for a summary of the current state-of-the-art for DQS.

Recent publications:

• [1] Distributed Quantum Sensing Using Continuous-Variable Multipartite Entanglement, Q. Zhuang, Z. Zhang, and J. H. Shapiro, Phys. Rev. A 97, 032329 (2018).

• [2] Demonstration of a Reconfigurable Entangled Radio-Frequency Photonic Sensor Network, Y. Xia, W. Li, W. Clark, D. Hart, Q. Zhuang, and Z. Zhang, Phys. Rev. Lett. 124, 150502 (2020).

• [3] Distributed Quantum Sensing Enhanced by Continuous-Variable Error Correction, Q. Zhuang, J. Preskill, and L. Jiang, New J. Phys. 22, 022001 (2020).

• [4] Distributed Quantum Sensing, Z. Zhang and Q. Zhuang, Quantum Sci. Technol. 6, 043001 (2021).

Quantum phenomena, such as entanglement, can boost the performance of sensing and detection. On the fundamental side, we recently derived the ultimate limits of quantum channel discrimination [1]. On the application side, our group has solved the optimal receiver design problem in quantum illumination in the past [2,3]. We also proposed to utilize entanglement to assist absorption spectroscopy that are widely applicable to various scenarios [4]. More recently, we generalize the quantum advantage in quantum illumination to important radar detection tasks such as target ranging [5] and revealed 10s of dB quantum advantage from entanglement in the range-delay accuracy [6].

Recent publications:

• [1] Ultimate Limits for Multiple Quantum Channel Discrimination, Q. Zhuang and S. Pirandola, Phys. Rev. Lett. 125, 080505 (2021)

• [2] Optimum Mixed-State Discrimination for Noisy Entanglement-Enhanced Sensing, Q. Zhuang, Z. Zhang, and J. H. Shapiro, Phys. Rev. Lett. 118, 040801 (2017).

• [3] Quantum Illumination for Enhanced Detection of Rayleigh-Fading Targets, Q. Zhuang, Z. Zhang, and J. H. Shapiro, Phys. Rev. A 96, 020302 (2017).

• [4] Entanglement-Assisted Absorption Spectroscopy, Haowei Shi, Zheshen Zhang, Stefano Pirandola, and Quntao Zhuang, Phys. Rev. Lett. 125, 180502 (2020).

• [5] Quantum Ranging with Gaussian Entanglement, Q. Zhuang, Phys. Rev. Lett. 126, 240501 (2021).

• [6] Ultimate Accuracy Limit of Quantum Pulse-Compression Ranging, Q. Zhuang and J. H. Shapiro, ArXiv 2109.11079 (2021).