30 citations · 129 across the 20 of their papers we have counts for
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Measurement-induced overconcentration in quantum generative models
Runzhe Mo, Bingzhi Zhang, Quntao Zhuang
Quantum measurement is a key resource for quantum generative learning, providing intrinsic stochasticity for generating diverse quantum samples. However, in measurement-assisted st…
When AI meets quantum information: A comprehensive review
Min Chen, Yu Gan, Xin Jin +15
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum syste…
Anticoncentrated -bit distribution from qubits
Bingzhi Zhang, Quntao Zhuang
Random circuit sampling (RCS) is a leading approach to demonstrate quantum advantage, with its believed classical hardness rooted in anticoncentration of output distributions and a…
An Analytic Theory of Quantum Imaginary Time Evolution
Min Chen, Bingzhi Zhang, Quntao Zhuang +1
Quantum imaginary time evolution (QITE) algorithm is one of the most promising variational quantum algorithms (VQAs), bridging the current era of Noisy Intermediate-Scale Quantum d…
Scaling Laws of Quantum Information Lifetime in Monitored Quantum Dynamics
Bingzhi Zhang, Fangjun Hu, Runzhe Mo +3
Quantum information is typically fragile under measurements and environmental coupling. Remarkably, we find that its lifetime can scale exponentially with system size when the envi…
Mixed-State Quantum Denoising Diffusion Probabilistic Model
Gino Kwun, Bingzhi Zhang, Quntao Zhuang
Generative quantum machine learning has gained significant attention for its ability to produce quantum states with desired distributions. Among various quantum generative models,…
Holographic deep thermalization for secure and efficient quantum random state generation
Bingzhi Zhang, Peng Xu, Xiaohui Chen +1
Randomness is a cornerstone of science, underpinning fields such as statistics, information theory, dynamical systems, and thermodynamics. In quantum science, quantum randomness, e…
Quantum-data-driven dynamical transition in quantum learning
Bingzhi Zhang, Junyu Liu, Liang Jiang +1
Quantum neural networks, parameterized quantum circuits optimized under a specific cost function, provide a paradigm for achieving near-term quantum advantage in quantum informatio…
Uncovering Quantum Many-body Scars with Quantum Machine Learning
Jiajin Feng, Bingzhi Zhang, Zhi-Cheng Yang +1
Quantum many-body scars are rare eigenstates hidden within the chaotic spectra of many-body systems, representing a weak violation of the eigenstate thermalization hypothesis (ETH)…
Quantum-enhanced learning with a controllable bosonic variational sensor network
Pengcheng Liao, Bingzhi Zhang, Quntao Zhuang
The emergence of quantum sensor networks has presented opportunities for enhancing complex sensing tasks, while simultaneously introducing significant challenges in designing and a…
Dynamical transition in controllable quantum neural networks with large depth
Bingzhi Zhang, Junyu Liu, Xiao-Chuan Wu +2
Understanding the training dynamics of quantum neural networks is a fundamental task in quantum information science with wide impact in physics, chemistry and machine learning. In…
Optimal entanglement-assisted electromagnetic sensing and communication in the presence of noise
Haowei Shi, Bingzhi Zhang, Jeffrey H. Shapiro +2
High time-bandwidth product signal and idler pulses comprised of independent identically distributed two-mode squeezed vacuum (TMSV) states are readily produced by spontaneous para…
Energy-dependent barren plateau in bosonic variational quantum circuits
Bingzhi Zhang, Quntao Zhuang
Bosonic continuous-variable Variational quantum circuits (VQCs) are crucial for information processing in cavity quantum electrodynamics and optical systems, widely applicable in q…
Fulfilling entanglement's optimal advantage via converting correlation to coherence
Haowei Shi, Bingzhi Zhang, Quntao Zhuang
Entanglement boosts performance limits in sensing and communication, and surprisingly the advantage over classical protocols can be even larger in presence of entanglement-breaking…
Entangling remote microwave quantum computers with hybrid entanglement swap and variational distillation
Bingzhi Zhang, Jing Wu, Linran Fan +1
Superconducting microwave circuits with Josephson junctions are a major platform for quantum computing. To unleash their full capabilities, the cooperative operation of multiple mi…
Quantum Computational Phase Transition in Combinatorial Problems
Bingzhi Zhang, Akira Sone, Quntao Zhuang
Quantum Approximate Optimization algorithm (QAOA) aims to search for approximate solutions to discrete optimization problems with near-term quantum computers. As there are no algor…
Fast suppression of classification error in variational quantum circuits
Bingzhi Zhang, Quntao Zhuang
Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and…
Quantum Internet under random breakdowns and intentional attacks
Bingzhi Zhang, Quntao Zhuang
Quantum networks will play a key role in distributed quantum information processing. As the network size increases, network-level errors like random breakdown and intentional attac…
Quantum communication capacity transition of complex quantum networks
Quntao Zhuang, Bingzhi Zhang
Quantum network is the key to enable distributed quantum information processing. As the single-link communication rate decays exponentially with the distance, to enable reliable en…
Entanglement formation in continuous-variable random quantum networks
Bingzhi Zhang, Quntao Zhuang
Entanglement is not only important for understanding the fundamental properties of many-body systems, but also the crucial resource enabling quantum advantages in practical informa…