3 citations · 7 across the 17 of their papers we have counts for
7 papers · 1 filter
Virtual Quantum Markov Chains
Yu-Ao Chen, Chengkai Zhu, Keming He +2
Quantum Markov chains generalize classical Markov chains for random variables to the quantum realm and exhibit unique inherent properties, making them an important feature in quant…
Enhancement of non-Stabilizerness within Indefinite Causal Order
Yin Mo, Chengkai Zhu, Zhiping Liu +2
In quantum computing, the nonstabilizerness of quantum operations is crucial for understanding and quantifying quantum speedups. In this study, we explore the phenomena of nonstabi…
Computable and Faithful Lower Bound on Entanglement Cost
Xin Wang, Mingrui Jing, Chengkai Zhu
Quantifying the minimum entanglement needed to prepare quantum states and implement quantum processes is a key challenge in quantum information theory. In this work, we develop com…
Quantum sequential scattering model for quantum state learning
Mingrui Jing, Geng Liu, Hongbin Ren +1
Learning probability distribution is an essential framework in classical learning theory. As a counterpart, quantum state learning has spurred the exploration of quantum machine le…
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Hao-kai Zhang, Chenghong Zhu, Mingrui Jing +1
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many applications can be viewed as learning a quantu…
Retrieving non-linear features from noisy quantum states
Benchi Zhao, Mingrui Jing, Lei Zhang +4
Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in quantum computing, such as entanglement spectroscopy, entropy est…
Ground state preparation with shallow variational warm-start
Youle Wang, Chenghong Zhu, Mingrui Jing +1
Preparing the ground states of a many-body system is essential for evaluating physical quantities and determining the properties of materials. This work provides a quantum ground s…