QuOCS: The quantum optimal control suite

Rossignolo, Marco; Reisser, Thomas; Marshall, Alastair; Rembold, Phila; Pagano, Alice; Vetter, Philipp J.; Said, Ressa S.; Müller, Matthias; Motzoi, Felix; Calarco, Tommaso; Jelezko, Fedor; Montangero, Simone

doi:10.1016/j.cpc.2023.108782

Cited by 8 publications

(3 citation statements)

References 105 publications

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“…The optimization algorithm is a homemade gradient algorithm that has some similarities with GRAPE. − QuOCS: The QOC suite [226] simulator. The software includes several state-of-the-art pulse sequences, but controls can also be created with an editor or with an optimizer based on GRAPE.…”

Section: A General Overview Of Standard Optimization Algorithmsmentioning

confidence: 99%

Introduction to theoretical and experimental aspects of quantum optimal control

Ansel,

Dionis,

Arrouas

et al. 2024

J. Phys. B: At. Mol. Opt. Phys.

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Quantum optimal control is a set of methods for designing time-varying electromagnetic fields to perform operations in quantum technologies. This tutorial paper introduces the basic elements of this theory based on the Pontryagin maximum principle, in a physicist-friendly way. An analogy with classical Lagrangian and Hamiltonian mechanics is proposed to present the main results used in this field. Emphasis is placed on the different numerical algorithms to solve a quantum optimal control problem. Several examples ranging from the control of two-level quantum systems to that of Bose-Einstein Condensates (BEC) in a one-dimensional optical lattice are studied in detail, using both analytical and numerical methods. Codes based on shooting method and gradient-based algorithms are provided. The connection between optimal processes and the quantum speed limit is also discussed in two-level quantum systems. In the case of BEC, the experimental implementation of optimal control protocols is described, both for two-level and many-level cases, with the current constraints and limitations of such platforms. This presentation is illustrated by the corresponding experimental results.

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Section: A General Overview Of Standard Optimization Algorithmsmentioning

confidence: 99%

Introduction to theoretical and experimental aspects of quantum optimal control

Ansel,

Dionis,

Arrouas

et al. 2024

J. Phys. B: At. Mol. Opt. Phys.

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“…as the figure of merit for a maximization with the Nelder-Mead algorithm [42] implemented in the quantum optimal control suite optimization software [43].…”

Section: B Optimization Of the Pulse Sequencementioning

confidence: 99%

Analyzing the collective emission of a Rydberg-blockaded single-photon source based on an ensemble of thermal atoms

Reuter,

Mäusezahl,

Moumtsilis

et al. 2024

Phys. Rev. A

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“…Despite this negative result, for a particular class of problems, such an algorithm may exist. For optimizing only coherent control, various algorithms were applied or developed, including the genetic algorithm [38], the Krotov algorithm [39], the Hamilton-Jacobi-Bellman equations [40], chopped random-basis quantum optimization (CRAB) [41], the Maday-Turinici algorithm [42], GRAPE [43][44][45][46], quantum feedback control [47][48][49][50][51][52], monotonically convergent algorithms [53,54], quantum reinforcement learning [55] and quantum machine learning [56], deep reinforcement learning [57], the combined approach via the quantum optimal control suite (QuOCS) [58], etc. For finding both coherent and incoherent controls, genetic evolutionary algorithms were initially used [19].…”

Section: Introductionmentioning

confidence: 99%

Quantum Control Landscapes for Generation of H and T Gates in an Open Qubit with Both Coherent and Environmental Drive

Petruhanov,

Pechen

2023

Photonics

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An important problem in quantum computation is the generation of single-qubit quantum gates such as Hadamard (H) and π/8 (T) gates, which are components of a universal set of gates. Qubits in experimental realizations of quantum computing devices are interacting with their environment. While the environment is often considered as an obstacle leading to a decrease in the gate fidelity, in some cases, it can be used as a resource. Here, we consider the problem of the optimal generation of H and T gates using coherent control and the environment as a resource acting on the qubit via incoherent control. For this problem, we studied the quantum control landscape, which represents the behavior of the infidelity as a functional of the controls. We considered three landscapes, with infidelities defined by steering between two, three (via Goerz–Reich–Koch approach), and four matrices in the qubit Hilbert space. We observed that, for the H gate, which is a Clifford gate, for all three infidelities, the distributions of minimal values obtained with a gradient search have a simple form with just one peak. However, for the T gate, which is a non-Clifford gate, the situation is surprisingly different—this distribution for the infidelity defined by two matrices also has one peak, whereas distributions for the infidelities defined by three and four matrices have two peaks, which might indicate the possible existence of two isolated minima in the control landscape. It is important that, among these three infidelities, only those defined with three and four matrices guarantee the closeness of the generated gate to a target and can be used as a good measure of closeness. We studied sets of optimized solutions for the most general and previously unexplored case of coherent and incoherent controls acting together and discovered that they form sub-manifolds in the control space, and unexpectedly, in some cases, two isolated sub-manifolds.

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QuOCS: The quantum optimal control suite

Cited by 8 publications

References 105 publications

Introduction to theoretical and experimental aspects of quantum optimal control

Introduction to theoretical and experimental aspects of quantum optimal control

Analyzing the collective emission of a Rydberg-blockaded single-photon source based on an ensemble of thermal atoms

Quantum Control Landscapes for Generation of H and T Gates in an Open Qubit with Both Coherent and Environmental Drive

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