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Adaptive pruning-based optimization of parameterized quantum circuits · Around

2020

Adaptive pruning-based optimization of parameterized quantum circuits

Sim, Sukin, Romero, Jonathan, Gonthier, Jerome F. et al.

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Variational hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.

While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space.
In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call "Parameter-Efficient Circuit Training" (PECT).
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set.

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