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Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models.
Polynomial-time approximation algorithms for the ising model
Mark Jerrum and Alistair Sinclair · 1993
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Lecture notes for physics 229: Quantum information and computation
John Preskill · 1998
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Spin-glass behavior in the random-anisotropy heisenberg model
Orlando V Billoni, Sergio A Cannas, and Francisco A Tamarit · 2005
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Introduction to quantum algorithms for physics and chemistry
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Shou-Shu Gong, Wei Zhu, and DN Sheng · 2014
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Quantum speed-ups for solving semidefinite programs
Fernando GSL Brandao and Krysta M Svore · 2017
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Quantum sdp-solvers: Better upper and lower bounds
Joran Van Apeldoorn, András Gilyén, Sander Gribling, and Ronald de Wolf · 2017
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Tomography and generative training with quantum boltzmann machines
Mária Kieferová and Nathan Wiebe · 2017
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A quantum algorithm to train neural networks using low-depth circuits
Guillaume Verdon, Michael Broughton, and Jacob Biamonte · 2017
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Error mitigation for short-depth quantum circuits
Kristan Temme, Sergey Bravyi, and Jay M Gambetta · 2017
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Quantum boltzmann machine
Mohammad H Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, and Roger Melko · 2018
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Quantum chemistry in the age of quantum computing
Yudong Cao, Jonathan Romero, Jonathan P Olson, Matthias Degroote, Peter D Johnson, Mária Kieferová, Ian D Kivlichan, Tim Menke, Borja Peropadre, Nicolas PD Sawaya, et al · 2019
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Variational thermal quantum simulation via thermofield double states
Jingxiang Wu and Timothy H Hsieh · 2019
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Generation of thermofield double states and critical ground states with a quantum computer
D Zhu, S Johri, NM Linke, KA Landsman, NH Nguyen, CH Alderete, AY Matsuura, TH Hsieh, and C Monroe · 2019
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Product spectrum ansatz and the simplicity of thermal states
John Martyn and Brian Swingle · 2019
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Quantum hamiltonian-based models and the variational quantum thermalizer algorithm
Guillaume Verdon, Jacob Marks, Sasha Nanda, Stefan Leichenauer, and Jack Hidary · 2019
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How to build the thermofield double state
William Cottrell, Ben Freivogel, Diego M Hofman, and Sagar F Lokhande · 2019
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Variational ansatz-based quantum simulation of imaginary time evolution
Sam McArdle, Tyson Jones, Suguru Endo, Ying Li, Simon C Benjamin, and Xiao Yuan · 2019
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Hybrid quantum-classical algorithms and quantum error mitigation
Suguru Endo, Zhenyu Cai, Simon C Benjamin, and Xiao Yuan · 2020
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Boltzmann machine learning with a variational quantum algorithm
Yuta Shingu, Yuya Seki, Shohei Watabe, Suguru Endo, Yuichiro Matsuzaki, Shiro Kawabata, Tetsuro Nikuni, and Hideaki Hakoshima · 2020
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Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution
Mario Motta, Chong Sun, Adrian TK Tan, Matthew J O’Rourke, Erika Ye, Austin J Minnich, Fernando GSL Brandão, and Garnet Kin-Lic Chan · 2020
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Cirq, October 2020
Quantum AI team and collaborators · 2020
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Tensorflow quantum: A software framework for quantum machine learning
Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J Martinez, Jae Hyeon Yoo, Sergei V Isakov, Philip Massey, Murphy Yuezhen Niu, Ramin Halavati, Evan Peters, et al · 2020
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
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An adaptive optimizer for measurement-frugal variational algorithms
Jonas M Kübler, Andrew Arrasmith, Lukasz Cincio, and Patrick J Coles · 2020
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Stochastic gradient descent for hybrid quantum-classical optimization
Ryan Sweke, Frederik Wilde, Johannes Jakob Meyer, Maria Schuld, Paul K Fährmann, Barthélémy Meynard-Piganeau, and Jens Eisert · 2020
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Noise-assisted quantum autoencoder
Chenfeng Cao and Xin Wang · 2020
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A neural-network variational quantum algorithm for many-body dynamics
Chee-Kong Lee, Pranay Patil, Shengyu Zhang, and Chang-Yu Hsieh · 2020
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A spectral condition for spectral gap: Fast mixing in high-temperature ising models
Ronen Eldan, Frederic Koehler, and Ofer Zeitouni · 2020
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Variational quantum boltzmann machines
Christa Zoufal, Aurélien Lucchi, and Stefan Woerner · 2020
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Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
David Wierichs, Christian Gogolin, and Michael Kastoryano · 2020
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On the universality of the quantum approximate optimization algorithm
Mauro ES Morales, Jacob D Biamonte, and Zoltán Zimborás · 2020
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Spin transport in a tunable heisenberg model realized with ultracold atoms
Paul Niklas Jepsen, Jesse Amato-Grill, Ivana Dimitrova, Wen Wei Ho, Eugene Demler, and Wolfgang Ketterle · 2020
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Noise-induced barren plateaus in variational quantum algorithms
Samson Wang, Enrico Fontana, Marco Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J Coles · 2020
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An alternative method for extracting the von neumann entropy from rényi entropies
Eric D’Hoker, Xi Dong, and Chih-Hung Wu · 2021
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Quantum computation of finite-temperature static and dynamical properties of spin systems using quantum imaginary time evolution
Shi-Ning Sun, Mario Motta, Ruslan N Tazhigulov, Adrian TK Tan, Garnet Kin-Lic Chan, and Austin J Minnich · 2021
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Accurately computing the log-sum-exp and softmax functions
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Turbulent relaxation after a quench in the heisenberg model
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Exploring finite temperature properties of materials with quantum computers
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