Quantum Optimal Control via Semi-Automatic Differentiation
Michael H. Goerz, Sebasti\'an C. Carrasco, Vladimir S. Malinovsky

TL;DR
This paper introduces semi-automatic differentiation for quantum optimal control, combining gradient methods with automatic differentiation to efficiently optimize complex quantum functionals, demonstrated on superconducting qubits.
Contribution
It presents a novel semi-automatic differentiation framework that reduces computational overhead and enables direct optimization of non-analytic quantum functionals.
Findings
Efficient gradient computation reduces memory and runtime overhead.
First direct optimization of non-analytic gate concurrence.
Benchmarking on superconducting qubits shows improved control performance.
Abstract
We develop a framework of "semi-automatic differentiation" that combines existing gradient-based methods of quantum optimal control with automatic differentiation. The approach allows to optimize practically any computable functional and is implemented in two open source Julia packages, GRAPE.jl and Krotov.jl, part of the QuantumControl.jl framework. Our method is based on formally rewriting the optimization functional in terms of propagated states, overlaps with target states, or quantum gates. An analytical application of the chain rule then allows to separate the time propagation and the evaluation of the functional when calculating the gradient. The former can be evaluated with great efficiency via a modified GRAPE scheme. The latter is evaluated with automatic differentiation, but with a profoundly reduced complexity compared to the time propagation. Thus, our approach eliminates…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum Mechanics and Applications
