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Simulated Quantum Annealing (SQA) is a Markov Chain Monte-Carlo algorithm that samples the equilibrium thermal state of a Quantum Annealing (QA) Hamiltonian.
Monte Carlo simulation of quantum spin systems
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Optimization by simmulated annealing
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Quantum statistical Monte Carlo methods and applications to spin systems
M. Suzuki · 1986
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Improved bounds for mixing rates of Markov chains and multicommodity flow
A. Sinclair · 1992
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Quantum annealing in the transverse Ising model
T. Kadowaki and H. Nishimori · 1998
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Quantum computation by adiabatic evolution, 2000, arXiv:quant-ph/0001106
E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser · 2000
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Mixing in time and space for lattice spin systems: a combinatorial view
M. Dyer, A. Sinclair, E. Vigoda, and D. Weitz · 2002
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Quantum adiabatic evolution algorithms versus simulated annealing, 2002, arXiv:quant-ph/0201031
E. Farhi, J. Goldstone, and S. Gutmann · 2002
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Quantum annealing by the path-integral Monte Carlo method: The two-dimensional random Ising model
R. Martoňák, G. E. Santoro, and E. Tosatti · 2002
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A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
M. Jerrum, A. Sinclair, and E. Vigoda · 2004
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The quantum adiabatic optimization algorithm and local minima
B. W. Reichardt · 2004
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Optimization by quantum annealing: Lessons from hard 3-SAT cases
D. Battaglia, G. E. Santoro, and E. Tosatti · 2005
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Merlin-Arthur games and stoquastic complexity, 2006, arXiv:quant-ph/0611021
S. Bravyi, A. J. Bessen, and B. M. Terhal · 2006
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The complexity of stoquastic local Hamiltonian problems
S. Bravyi, D. P. DiVincenzo, R. I. Oliveira, and B. M. Terhal · 2006
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Markov chain comparison
M. Dyer, L. A. Goldberg, M. Jerrum, and R. Martin · 2006
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Markov Chains and Mixing Times
D. Levin, Y. Peres, and E. Wilmer · 2008
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Complexity of stoquastic frustration-free Hamiltonians
S. Bravyi and B. M. Terhal · 2009
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P. Caputo, E. Lubetzky, F. Martinelli, A. Sly, and F. L. Toninelli · 2014
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Tunneling through high energy barriers in simulated quantum annealing, 2014, arXiv:1410.8484
E. Crosson and M. Deng · 2014
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L. T. Brady and W. van Dam · 2015
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What is the computational value of finite range tunneling?, 2015, arXiv:1512.02206
V. S. Denchev, S. Boixo, S. V. Isakov, N. Ding, R. Babbush, V. Smelyanskiy, J. Martinis, and H. Neven · 2015
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Quantum adiabatic algorithms, small gaps, and different paths
E. Farhi, J. Goldstone, D. Gosset, S. Gutmann, H. B. Meyer, and P. Shor · 2009
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Obstructions to classically simulating the quantum adiabatic algorithm
M. B. Hastings · 2013
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Quantum annealing with more than one hundred qubits
S. Boixo, T. F. Rønnow, S. V. Isakov, Z. Wang, D. Wecker, D. A. Lidar, J. M. Martinis, and M. Troyer · 2014
Cited alongside, same era.
Monte Carlo simulation of stoquastic Hamiltonians, 2014, arXiv:1402.2295
S. Bravyi · 2014
Cited alongside, same era.
E. Inack and S. Pilati · 2015
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Understanding quantum tunneling through quantum Monte Carlo simulations, 2015, arXiv:1510.08057
S. V. Isakov, G. Mazzola, V. N. Smelyanskiy, Z. Jiang, S. Boixo, H. Neven, and M. Troyer · 2015
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The performance of the quantum adiabatic algorithm on spike Hamiltonians, 2015, arXiv:1511.06991
L. Kong and E. Crosson · 2015
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S. Muthukrishnan, T. Albash, and D. A. Lidar · 2015
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L. T. Brady and W. van Dam · 2016
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Rapidly mixing Monte Carlo for 1D stoquastic Hamiltonians, 2016
E. Crosson and A. Harrow · 2016
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