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20182026
most citedTopological Quantum Compiling with Reinforcement Learning

98 citations · 305 across the 21 of their papers we have counts for

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7 papers · 1 filter

quant-ph2025★ 1 cited

Artificial intelligence for representing and characterizing quantum systems

Yuxuan Du, Yan Zhu, Yuan-Hang Zhang +8

Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quant…

quant-ph2022★ 60 cited

Transformer Quantum State: A Multi-Purpose Model for Quantum Many-Body Problems

Yuan-Hang Zhang, Massimiliano Di Ventra

Inspired by the advancements in large language models based on transformers, we introduce the transformer quantum state (TQS): a versatile machine learning model for quantum many-b…

quant-ph2021

Efficient Quantum State Tomography with Mode-assisted Training

Yuan-Hang Zhang, Massimiliano Di Ventra

Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo based methods. However, unless specifically designed, such samplers only cons…

quant-ph2020★ 98 cited

Topological Quantum Compiling with Reinforcement Learning

Yuan-Hang Zhang, Pei-Lin Zheng, Yi Zhang +1

Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum compu…

quant-ph2019★ 62 cited

Building quantum neural networks based on swap test

Jian Zhao, Yuan-Hang Zhang, Chang-Peng Shao +3

Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, t…

quant-ph2018★ 9 cited

An Efficient Algorithmic Way to Construct Boltzmann Machine Representations for Arbitrary Stabilizer Code

Yuan-Hang Zhang, Zhian Jia, Yu-Chun Wu +1

Restricted Boltzmann machines (RBMs) have demonstrated considerable success as variational quantum states; however, their representational power remains incompletely understood. In…

quant-ph2018★ 31 cited

Efficient Machine Learning Representations of Surface Code with Boundaries, Defects, Domain Walls and Twists

Zhih-Ahn Jia, Yuan-Hang Zhang, Yu-Chun Wu +3

Machine learning representations of many-body quantum states have recently been introduced as an ansatz to describe the ground states and unitary evolutions of many-body quantum sy…