Differentiable Learning of Quantum Circuit Born Machine
- Jin-Guo Liu, Lei Wang
- Computer SciencePhysical Review A
- 11 April 2018
This work devise an efficient gradient-based learning algorithm for the quantum circuit Born machine by minimizing the kerneled maximum mean discrepancy loss and simulated generative modeling of the Bars-and-Stripes dataset and Gaussian mixture distributions using deep quantum circuits.
Unsupervised Generative Modeling Using Matrix Product States
- Zhaoyu Han, Jun Wang, H. Fan, Lei Wang, Pan Zhang
- Computer SciencePhysical Review X
- 6 September 2017
This work proposes a generative model using matrix product states, which is a tensor network originally proposed for describing (particularly one-dimensional) entangled quantum states, and enjoys efficient learning analogous to the density matrix renormalization group method.
Learning and Inference on Generative Adversarial Quantum Circuits
- J. Zeng, Y. Wu, Jin-Guo Liu, Lei Wang, Jiangping Hu
- Computer Science, PhysicsPhysical Review A
- 10 August 2018
An adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together is devised, which can infer missing data with quadratic speed up via amplitude amplification.
Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design
- Xiu-Zhe Luo, Jin-Guo Liu, Pan Zhang, Lei Wang
- Computer ScienceQuantum
- 23 December 2019
Yao, an extensible, efficient open-source framework for quantum algorithm design, is introduced, which achieves state-of-the-art performance in simulating small to intermediate-sized quantum circuits that are relevant to near-term applications.
Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits
- Jin-Guo Liu, Li-xin Mao, Pan Zhang, Lei Wang
- Physics, Computer ScienceMachine Learning: Science and Technology
- 24 December 2019
An efficient variational algorithm is devised to jointly optimize the classical neural network and the quantum circuit to solve quantum statistical mechanics problems and obtain thermal observables such as the variational free energy, entropy, and specific heat.
Efficient Quantum Tomography with Fidelity Estimation.
- Zhaoyu Han, Jun Wang, Lei Wang
- 8 December 2017
We propose a machine-learning-based quantum state tomography scheme for pure states, along with a built-in fidelity estimation approach to access the reliability of the tomographic state. We prove…
Scalable quantum tomography with fidelity estimation
- Jun Wang, Zhaoyu Han, Lei Wang
- Computer SciencePhysical Review A
- 9 December 2017
We propose a quantum tomography scheme for pure qudit systems which adopts a certain version of random basis measurements and a generative learning method, along with a built-in fidelity estimation…
Tropical Tensor Network for Ground States of Spin Glasses.
- Jin-Guo Liu, Lei Wang, Pan Zhang
- PhysicsPhysical Review Letters
- 16 August 2020
The approach brings together the concepts from graphical models, tensor networks, differentiable programming, and quantum circuit simulation, and easily utilizes the computational power of graphical processing units (GPUs).
Thermal variational quantum simulation on a superconducting quantum processor
- Xueyi Guo, Shang-Shu Li, H. Fan
- Education, Physics
- 13 July 2021
Solving finite-temperature properties of quantum many-body systems is generally challenging to classical computers due to their high computational complexities. In this article, we present…
Lecture Note on Deep Learning and Quantum Many-Body Computation
- Jin-Guo Liu, Shuo-Hui Li, Lei Wang
This note introduces deep learning from a computational quantum physicist’s perspective. The focus is on deep learning’s impacts to quantum many-body computation, and vice versa. The latest version…