Optimal control of families of quantum gates

Abstract

Quantum Optimal Control (QOC) enables the realization of accurate operations, such as quantum gates, and support the development of quantum technologies. To date, many QOC frameworks have been developed but those remain only naturally suited to optimize a single targeted operation at a time. We extend this concept to optimal control with a continuous family of targets, and demonstrate that an optimization based on neural networks can find families of time-dependent Hamiltonians that realize desired classes of quantum gates in minimal time.

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Cited by in corpus (11)

• Lie-algebraic classical simulations for quantum computing
• Preparing Schrödinger cat states in a microwave cavity using a neural network
• Efficient control pulses for continuous quantum gate families through coordinated re-optimization
• Differentiable master equation solver for quantum device characterisation
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