Stochastic optimal control of open quantum systems
Aar\'on Villanueva, Hilbert Kappen

TL;DR
This paper introduces Quantum Diffusion Control (QDC), a novel stochastic optimal control method for open quantum systems that uses adaptive importance sampling to efficiently find high-accuracy control solutions without gradient computations.
Contribution
The paper develops a new QDC algorithm for quantum state preparation that leverages path integral control techniques and adaptive importance sampling, avoiding gradient calculations.
Findings
QDC effectively solves open quantum control problems with high accuracy.
The method is applicable to single- and multi-qubit systems.
QDC demonstrates potential for implementation on quantum hardware.
Abstract
We address the generic problem of optimal quantum state preparation for open quantum systems. It is well known that open quantum systems can be simulated by quantum trajectories described by a stochastic Schr\"odinger equation. In this context, the state preparation becomes a stochastic optimal control (SOC) problem. The latter requires the solution of the Hamilton-Jacobi-Bellman equation, which is, in general, challenging to solve. A notable exception are the so-called path integral (PI) control problems, for which one can estimate the optimal control solution by direct sampling of the cost objective. In this work, we derive a class of quantum state preparation problems that are amenable to PI control techniques, and propose a corresponding algorithm, which we call Quantum Diffusion Control (QDC). Unlike conventional quantum control algorithms, QDC avoids computing gradients of the…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Mechanics and Applications · Quantum Computing Algorithms and Architecture
