Mitigated barren plateaus in the time-nonlocal optimization of analog quantum-algorithm protocols

Abstract

Quantum machine learning has emerged as a promising utilization of near-term quantum computation devices. However, algorithmic classes such as variational quantum algorithms have been shown to suffer from barren plateaus due to vanishing gradients in their parameters spaces. We present an approach to quantum algorithm optimization that is based on trainable Fourier coefficients of Hamiltonian system parameters. Our is exclusive to the extension of discrete quantum variational algorithms to analog quantum optimal control schemes and is nonlocal in time. We demonstrate the viability of our on the objectives of compiling the quantum Fourier transform and preparing ground states of random problem Hamiltonians. In comparison to the temporally local discretization in quantum optimal control and parametrized circuits, our exhibits faster and more consistent convergence. We uniformly sample objective gradients across the parameter space and find that in our the variance decays at a nonexponential rate with the number of qubits, while it decays at an exponential rate in the temporally local benchmark . This indicates the mitigation of barren plateaus in our . We propose our as a viable candidate for near-term quantum machine learning. Published by the American Physical Society 2024

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