Learning adiabatic quantum algorithms over optimization problems

[1]  Ioannis G. Kevrekidis,et al.  On learning Hamiltonian systems from data. , 2019, Chaos.

[2]  O. Lychkovskiy.  Non-diagonal problem Hamiltonian for adiabatic quantum computation , 2018 .

[3]  Enrico Blanzieri,et al.  Quantum annealing learning search for solving QUBO problems , 2018, Quantum Information Processing.

[4]  Kazutaka Takahashi.  Hamiltonian Engineering for Adiabatic Quantum Computation: Lessons from Shortcuts to Adiabaticity , 2018, Journal of the Physical Society of Japan.

[5]  Hilbert J Kappen,et al.  Learning quantum models from quantum or classical data , 2018, Journal of Physics A: Mathematical and Theoretical.

[6]  Hans-J. Briegel,et al.  Machine learning \& artificial intelligence in the quantum domain , 2017, ArXiv.

[7]  Clément Bouttier,et al.  Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations , 2017, J. Mach. Learn. Res..

[8]  Catherine C. McGeoch.  Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice , 2014, Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice.

[9]  Sergio Boixo,et al.  Spectral Gap Amplification , 2011, SIAM J. Comput..

[10]  Jérémie Roland,et al.  Anderson localization makes adiabatic quantum optimization fail , 2009, Proceedings of the National Academy of Sciences.

[11]  M. Ruskai,et al.  Bounds for the adiabatic approximation with applications to quantum computation , 2006, quant-ph/0603175.

[12]  R. Cleve,et al.  Efficient Quantum Algorithms for Simulating Sparse Hamiltonians , 2005, quant-ph/0508139.

[13]  Seth Lloyd,et al.  Adiabatic quantum computation is equivalent to standard quantum computation , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[14]  Fred W. Glover,et al.  Tabu search and finite convergence , 2002, Discret. Appl. Math..

[15]  N. Cerf,et al.  Quantum search by local adiabatic evolution , 2001, quant-ph/0107015.

[16]  E. Farhi,et al.  A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem , 2001, Science.

[17]  Alexander Elgart,et al.  The Adiabatic Theorem of Quantum Mechanics , 1998 .

[18]  Ulrich Faigle,et al.  Some Convergence Results for Probabilistic Tabu Search , 1992, INFORMS J. Comput..

[19]  Awi Federgruen,et al.  Ergodicity in Parametric Nonstationary Markov Chains: An Application to Simulated Annealing Methods , 1987, Oper. Res..

[20]  A. Federgruen,et al.  Simulated annealing methods with general acceptance probabilities , 1987, Journal of Applied Probability.

[21]  V. Fock,et al.  Beweis des Adiabatensatzes , 1928 .

[22]  Peter J. Love,et al.  A variational eigenvalue solver on a quantum processor , 2018 .

[23]  Alán Aspuru-Guzik,et al.  The theory of variational hybrid quantum-classical algorithms , 2015, 1509.04279.

[24]  D. McMahon.  Adiabatic Quantum Computation , 2008 .

[25]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[26]  Stefan Teufel,et al.  Adiabatic perturbation theory in quantum dynamics , 2003 .