Training A Quantum Optimizer

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2003

Cited alongside, same era.

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Cited in the paper.

E. Farhi, J. Goldstone, and S. Gutmann, “A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem”, arXiv:1412.6062

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E. Farhi, J. Goldstone, and S. Gutmann, “Quantum Adiabatic Evolution Algorithms with Different Paths”, arxiv:quant-ph/0208135

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E. Crosson, E. Farhi, C. Yen-Yu Lin, H.-H. Lin, and P. Shor, “Different Strategies for Optimization Using the Quantum Adiabatic Algorithm”, arXiv:1401.7320

Original

Cited in the paper.

The detailed data for instances, annealing times, and overlaps are from E. Crosson, private communication

Cited in the paper.

In detail, in a random step, each parameter is incremented by a value chosen independently and uniformly from the interval [ − δ , + δ ] [-\delta,+\delta] , where initially δ = 0.1 \delta=0.1 . If fewer than 3 3 trials improve the objective out of 50 50 attempts, then δ \delta is multiplied by 1 / 2 1/2 , while if more than 5 5 improve it, δ \delta is multiplied by 2 2

Cited in the paper.

M.H.S. Amin,Peter J. Love and C.J.S. Truncik, Phys. Rev. Lett. 100

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2015

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T. Albash, W. Vinci, A. Mishra, P. A. Warburton, and D. A. Lidar, “Consistency Tests of Classical and Quantum Models for a Quantum Annealer”, Phys. Rev. A 91

2015

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