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Using models to improve optimizers for variational quantum algorithms · Around

2020

Using models to improve optimizers for variational quantum algorithms

Sung, Kevin J., Yao, Jiahao, Harrigan, Matthew P. et al.

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Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers.

These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit.
In practice, finite sampling error and gate errors make this a stochastic optimization with unique challenges that must be addressed at the level of the optimizer.
The sharp trade-off between precision and sampling time in conjunction with experimental constraints necessitates the development of new optimization strategies to minimize overall wall clock time in this setting.

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