176 citations · 1.4k across the 70 of their papers we have counts for
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Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity
Wei-Ming Li, Shi-Ju Ran
In quantum and quantum-inspired machine learning, the very first step is to embed the data in quantum space known as Hilbert space. Developing quantum kernel function (QKF), which…
Cold atoms meet lattice gauge theory
Monika Aidelsburger, Luca Barbiero, Alejandro Bermudez +19
The central idea of this review is to consider quantum field theory models relevant for particle physics and replace the fermionic matter in these models by a bosonic one. This is…
Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
Rui Hong, Peng-Fei Zhou, Bin Xi +3
The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields. Inspired by quantum potential neural network, we here pr…
Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions
Ying Lu, Yue-Min Li, Peng-Fei Zhou +1
State preparation is of fundamental importance in quantum physics, which can be realized by constructing the quantum circuit as a unitary that transforms the initial state to the t…
Automatically Differentiable Quantum Circuit for Many-qubit State Preparation
Peng-Fei Zhou, Rui Hong, Shi-Ju Ran
Constructing quantum circuits for efficient state preparation belongs to the central topics in the field of quantum information and computation. As the number of qubits grows fast,…
Accurate simulation and thermal tuning by temperature-adaptive boundary interactions on quantum many-body systems
Ding-Zu Wang, Guo-Feng Zhang, Maciej Lewenstein +1
Constructing quantum Hamiltonians for simulating and controlling the exotic physics of many-body systems belongs to the most important topics of condensed matter physics and quantu…