Abstract Quantum control allows a wide range of quantum operations employed in molecular physics, nuclear magnetic resonance and quantum information processing. Thanks to the existing microelectronics industry, semiconducting qubits, where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots, constitute a highly competitive candidate for scalable solid-state quantum technologies. In quantum information processing, advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience. In this review, we first introduce the basics of various widely-used control methods, including resonant excitation, adabatic passage, shortcuts to adiabaticity, composite pulses, and quantum optimal control. Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits, such as Loss–DiVincenzo spin qubit, spinglet-triplet qubit, exchange-only qubit and charge qubit.Abstract Quantum control allows a wide range of quantum operations employed in molecular physics, nuclear magnetic resonance and quantum information processing. Thanks to the existing microelectronics industry, semiconducting qubits, where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots, constitute a highly competitive candidate for scalable solid-state quantum technologies. In quantum information processing, advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience. In this review, we first introduce the basics of various widely-used control methods, including resonant excitation, adabatic passage, shortcuts to adiabaticity, composite pulses, and quantum optimal control. Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits, such as Loss–DiVincenzo spin qubit, spinglet-triplet qubit, exchange-only qubit and charge qubit.

arXiv:2412.03286Journal of SemiconductorsJan 1, 20260 citations

Understanding mechanism in quantum optimal control

Abhra Mitra

Jan 1, 20080 citations

Machine-learning-inspired quantum optimal control of nonadiabatic geometric quantum computation via reverse engineering

Meng-Yun Mao, Zheng Cheng, Yan Xia +2 more

Quantum control plays an irreplaceable role in practical use of quantum computers. However, some challenges have to be overcome to find more suitable and diverse control parameters. We propose a promising and generalizable average-fidelity-based machine-learning-inspired method to optimize the control parameters, in which a neural network with periodic feature enhancement is used as an ansatz. In the implementation of a single-qubit gate by cat-state nonadiabatic geometric quantum computation via reverse engineering, compared with the control parameters in the simple form of a trigonometric function, our approach can yield significantly higher-fidelity $(>99.99%)$ phase gates, such as the $\ensuremath{\pi}/8$ gate (t gate). Single-qubit gates are robust against systematic noise, additive white Gaussian noise, and decoherence. We numerically demonstrate that the neural network possesses the ability to expand the model space. With the help of our optimization, we provide a feasible way to implement cascaded multiqubit gates with high quality in a bosonic system. Therefore, the machine-learning-inspired method may be feasible in quantum optimal control of nonadiabatic geometric quantum computation.Quantum control plays an irreplaceable role in practical use of quantum computers. However, some challenges have to be overcome to find more suitable and diverse control parameters. We propose a promising and generalizable average-fidelity-based machine-learning-inspired method to optimize the control parameters, in which a neural network with periodic feature enhancement is used as an ansatz. In the implementation of a single-qubit gate by cat-state nonadiabatic geometric quantum computation via reverse engineering, compared with the control parameters in the simple form of a trigonometric function, our approach can yield significantly higher-fidelity $(>99.99%)$ phase gates, such as the $\ensuremath{\pi}/8$ gate (t gate). Single-qubit gates are robust against systematic noise, additive white Gaussian noise, and decoherence. We numerically demonstrate that the neural network possesses the ability to expand the model space. With the help of our optimization, we provide a feasible way to implement cascaded multiqubit gates with high quality in a bosonic system. Therefore, the machine-learning-inspired method may be feasible in quantum optimal control of nonadiabatic geometric quantum computation.

arXiv:2309.16470Physical Review ASep 27, 20239 citations

Quantum optimal control using the adjoint method

Alfio Borzı̀

Nanoscale Systems Mathematical Modeling Theory and ApplicationsNov 29, 201210 citations

Introduction to the Foundations of Quantum Optimal Control

Ugo Boscain, Mario Sigalotti, Dominique Sugny

Optimal Control Theory is a powerful mathematical tool, which has known a rapid development since the 1950s, mainly for engineering applications. More recently, it has become a widely used method to improve process performance in quantum technologies by means of highly efficient control of quantum dynamics. This review aims at providing an introduction to key concepts of optimal control theory which is accessible to physicists and engineers working in quantum control or in related fields. The different mathematical results are introduced intuitively, before being rigorously stated. This review describes modern aspects of optimal control theory, with a particular focus on the Pontryagin Maximum Principle, which is the main tool for determining open-loop control laws without experimental feedback. The different steps to solve an optimal control problem are discussed, before moving on to more advanced topics such as the existence of optimal solutions or the definition of the different types of extremals, namely normal, abnormal, and singular. The review covers various quantum control issues and describes their mathematical formulation suitable for optimal control. The optimal solution of different low-dimensional quantum systems is presented in detail, illustrating how the mathematical tools are applied in a practical way.Optimal Control Theory is a powerful mathematical tool, which has known a rapid development since the 1950s, mainly for engineering applications. More recently, it has become a widely used method to improve process performance in quantum technologies by means of highly efficient control of quantum dynamics. This review aims at providing an introduction to key concepts of optimal control theory which is accessible to physicists and engineers working in quantum control or in related fields. The different mathematical results are introduced intuitively, before being rigorously stated. This review describes modern aspects of optimal control theory, with a particular focus on the Pontryagin Maximum Principle, which is the main tool for determining open-loop control laws without experimental feedback. The different steps to solve an optimal control problem are discussed, before moving on to more advanced topics such as the existence of optimal solutions or the definition of the different types of extremals, namely normal, abnormal, and singular. The review covers various quantum control issues and describes their mathematical formulation suitable for optimal control. The optimal solution of different low-dimensional quantum systems is presented in detail, illustrating how the mathematical tools are applied in a practical way.

Oct 19, 20203 citations

(>99.99%)

phase gates, such as the $\ensuremath{\pi}/8$ gate (t gate). Single-qubit gates are robust against systematic noise, additive white Gaussian noise, and decoherence. We numerically demonstrate that the neural network possesses the ability to expand the model space. With the help of our optimization, we provide a feasible way to implement cascaded multiqubit gates with high quality in a bosonic system. Therefore, the machine-learning-inspired method may be feasible in quantum optimal control of nonadiabatic geometric quantum computation.