6 papers
Generative learning of time series with holographic quantum circuit sampling
Bingzhi Zhang, Matthew W. Lee, Tom Finke +1
Quantum generative models face growing qubit requirements and concentrated loss landscapes when learning long time series. Here we introduce holographic quantum circuit sampling (H…
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…
Emergence of chaos with exceptional points in reset-driven Floquet dynamics
Jia-jin Feng, Quntao Zhuang
We investigate the spectral structure of reset-driven Floquet quantum channels generated by the Hamiltonian evolution of a many-body system followed by periodic resetting of a bath…
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…