Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Science Advances

Adaptive optimal control of entangled qubits

David L. Goodwin, Pranav Singh, Mohammadali Foroozandeh

Published Dec 7, 2022• 14 citations
Publisher

Abstract

Developing fast, robust, and accurate methods for optimal control of quantum systems comprising interacting particles is one of the most active areas of current science. Although a valuable repository of algorithms is available for numerical applications in quantum control, the high computational cost is somewhat overlooked. Here, we present a fast and accurate optimal control algorithm for systems of interacting qubits, QOALA (quantum optimal control by adaptive low-cost algorithm), which is predicted to offer O ( M 2 ) speedup for an M -qubit system, compared to the state-of-the-art exact methods, without compromising overall accuracy of the optimal solution. The method is general and compatible with diverse Hamiltonian structures. The proposed approach uses inexpensive low-accuracy approximations of propagators far from the optimum, adaptively switching to higher accuracy, higher-cost propagators when approaching the optimum. In addition, the utilization of analytical Lie algebraic derivatives that do not require computationally expensive matrix exponential brings even better performance.

DOI
10.1126/sciadv.abq4244

Related papers

Dynamic Quantum Entanglement Control: A Novel Adaptive Framework for Optimized Quantum Systems

Jincheng Zhang

This paper introduces a novel adaptive control framework predicated on the unique properties of dynamic quantum entanglement. The core goal is to enhance quantum system performance and stability through more precise control and error correction, ultimately paving the way for more powerful quantum computation. We explore the potential of leveraging quantum entanglement to achieve unprecedented levels of control and resolution. The proposed framework utilizes a dynamic adjustment of entanglement parameters to mitigate noise and optimize system behavior, addressing limitations of traditional control methods. This work builds upon existing research in quantum error correction and control, offering a fundamentally different approach to quantum system optimization.

•Aug 31, 2026•0 citations

Structure-preserving full- and low-rank exponential methods for the optimal control of Lindblad equations

Hao Chen, Alfio Borzı̀

Optimal control of open quantum systems governed by the Lindblad master equation requires the repeated solution of forward and adjoint evolution equations. For large Hilbert spaces, these computations demand numerical methods that are structure-preserving, accurate, and computationally efficient, while remaining compatible with nonsmooth optimization techniques. In this work, a unified framework for optimal control of Lindblad equations is presented. The continuous optimality system is derived from the Pontryagin maximum principle and solved by a sequential quadratic Hamiltonian (SQH) method. Its numerical realization is based on second-order exponential midpoint propagators for the forward and adjoint Lindblad equations with time-dependent Hamiltonians. Full-rank schemes preserving the Hermitian and positive-semidefinite structure of the density matrix are developed together with low-rank formulations that substantially reduce storage requirements and computational cost. Rigorous error estimates are established for the full- and low-rank forward and adjoint propagators. Numerical experiments confirm the predicted convergence rates, demonstrate the effectiveness of the low-rank approximations, and illustrate the performance of the proposed FREM-SQH and LREM-SQH algorithms for optimal control problems with smooth and nonsmooth control costs.

arXiv:2610.09403•Oct 7, 2026•0 citations

PANSATZ: pulse-based ansatz for variational quantum algorithms

Dekel Meirom, Steven H. Frankel

Quantum computers promise a great computational advantage over classical computers, which might help solve various computational challenges such as the simulation of complicated quantum systems, finding optimum in large optimization problems, and solving large-scale linear algebra problems. Current available quantum devices have only a limited amount of qubits and a high level of noise, limiting the size of problems that can be solved accurately with those devices. Variational quantum algorithms (VQAs) have emerged as a leading strategy to address these limitations by optimizing cost function based on measurement results of shallow depth circuits. Recently, various pulse engineering methods were suggested in order to improve VQA results, including optimizing pulse parameters instead of gate angles as part of the VQA optimization process. In this paper, we suggest a novel pulse-based ansatz, which is parameterized mainly by pulses’ duration of pre-defined pulse structures. This ansatz structure provides relatively low amounts of optimization parameters while maintaining high expressibility, allowing fast convergence. In addition, the ansatz has structured adaptivity to the entanglement level required by the problem, allowing low noise and accurate results. We tested this ansatz against quantum chemistry problems. Specifically, finding the ground-state energy associated with the electron configuration problem, using the variational quantum eigensolver (VQE) algorithm for several different molecules. We manage to achieve chemical accuracy both in simulation for several molecules and on one of IBM’s NISQ devices for the H2 molecule in the STO-3G basis, without the need for extensive error mitigation. Our results are compared to a common gate-based ansatz and show better accuracy and significant latency reduction—up to 7× shorter ansatz schedules.

arXiv:2212.12911•Frontiers in Quantum Science and Technology•Oct 9, 2023•21 citations

Optimal Control of Bipartite Quantum Systems

Emanuel Malvetti, Léo Van Damme

Closed bipartite quantum systems subject to fast local unitary control are studied using quantum optimal control theory and a method of reduced control systems based on the Schmidt decomposition. Particular focus is given to the time-optimal generation of maximally entangled states and product states, as well as to the problem of stabilizing quantum states with a certain amount of entanglement. Explicit analytical solutions are given for general systems consisting of two qubits (as well as for bosonic and fermionic analogues) and also for a class of systems consisting of two coupled qutrits which is studied using the Pontryagin Maximum Principle.

arXiv:2405.20034•May 30, 2024•0 citations

Quantum Adaptive Algorithm Parameter Optimization

Jincheng Zhang

Quantum adaptive algorithms offer the potential for significant speedups in complex computations, particularly in areas like quantum chemistry and materials science. However, achieving optimal performance often requires careful optimization of quantum parameters, a task that can be computationally expensive. This paper introduces a novel quantum adaptive algorithm that dynamically adjusts quantum parameters to optimize algorithm efficiency and accuracy, thereby reducing computational costs. We propose a method centered around a feedback loop that continuously monitors and adjusts the parameters based on the algorithm's output, leading to a more robust and adaptable optimization strategy. The core mechanism leverages a dynamic parameter tuning process to achieve this. This research explores the potential of this approach in tackling specific challenges within quantum algorithm design and provides a framework for future development. The key innovation lies in the continuous feedback loop that dynamically adjusts parameters, resulting in a more efficient and accurate algorithm.

•Sep 3, 2026•0 citations

Title: Quantum-Inspired Algorithms for Optimal Control in Stochastic Systems

Jincheng Zhang

Quantum-inspired algorithms offer a promising avenue for enhancing the performance of optimal control in stochastic systems, particularly those exhibiting complex and uncertain dynamics. This paper investigates the potential of incorporating quantum-like principles – specifically superposition and entanglement – into standard control techniques to mitigate the inherent challenges of these systems. We present a novel framework for adapting conventional control methods by leveraging these principles, demonstrating improvements in performance through simulation and analysis. The core claim focuses on creating algorithms that effectively handle the inherent randomness and sensitivity of stochastic systems.

•Aug 31, 2026•0 citations
Developing fast, robust, and accurate methods for optimal control of quantum systems comprising interacting particles is one of the most active areas of current science. Although a valuable repository of algorithms is available for numerical applications in quantum control, the high computational cost is somewhat overlooked. Here, we present a fast and accurate optimal control algorithm for systems of interacting qubits, QOALA (quantum optimal control by adaptive low-cost algorithm), which is predicted to offer O ( M 2 ) speedup for an M -qubit system, compared to the state-of-the-art exact methods, without compromising overall accuracy of the optimal solution. The method is general and compatible with diverse Hamiltonian structures. The proposed approach uses inexpensive low-accuracy approximations of propagators far from the optimum, adaptively switching to higher accuracy, higher-cost propagators when approaching the optimum. In addition, the utilization of analytical Lie algebraic derivatives that do not require computationally expensive matrix exponential brings even better performance.
This paper introduces a novel adaptive control framework predicated on the unique properties of dynamic quantum entanglement. The core goal is to enhance quantum system performance and stability through more precise control and error correction, ultimately paving the way for more powerful quantum computation. We explore the potential of leveraging quantum entanglement to achieve unprecedented levels of control and resolution. The proposed framework utilizes a dynamic adjustment of entanglement parameters to mitigate noise and optimize system behavior, addressing limitations of traditional control methods. This work builds upon existing research in quantum error correction and control, offering a fundamentally different approach to quantum system optimization.
Optimal control of open quantum systems governed by the Lindblad master equation requires the repeated solution of forward and adjoint evolution equations. For large Hilbert spaces, these computations demand numerical methods that are structure-preserving, accurate, and computationally efficient, while remaining compatible with nonsmooth optimization techniques. In this work, a unified framework for optimal control of Lindblad equations is presented. The continuous optimality system is derived from the Pontryagin maximum principle and solved by a sequential quadratic Hamiltonian (SQH) method. Its numerical realization is based on second-order exponential midpoint propagators for the forward and adjoint Lindblad equations with time-dependent Hamiltonians. Full-rank schemes preserving the Hermitian and positive-semidefinite structure of the density matrix are developed together with low-rank formulations that substantially reduce storage requirements and computational cost. Rigorous error estimates are established for the full- and low-rank forward and adjoint propagators. Numerical experiments confirm the predicted convergence rates, demonstrate the effectiveness of the low-rank approximations, and illustrate the performance of the proposed FREM-SQH and LREM-SQH algorithms for optimal control problems with smooth and nonsmooth control costs.
Quantum computers promise a great computational advantage over classical computers, which might help solve various computational challenges such as the simulation of complicated quantum systems, finding optimum in large optimization problems, and solving large-scale linear algebra problems. Current available quantum devices have only a limited amount of qubits and a high level of noise, limiting the size of problems that can be solved accurately with those devices. Variational quantum algorithms (VQAs) have emerged as a leading strategy to address these limitations by optimizing cost function based on measurement results of shallow depth circuits. Recently, various pulse engineering methods were suggested in order to improve VQA results, including optimizing pulse parameters instead of gate angles as part of the VQA optimization process. In this paper, we suggest a novel pulse-based ansatz, which is parameterized mainly by pulses’ duration of pre-defined pulse structures. This ansatz structure provides relatively low amounts of optimization parameters while maintaining high expressibility, allowing fast convergence. In addition, the ansatz has structured adaptivity to the entanglement level required by the problem, allowing low noise and accurate results. We tested this ansatz against quantum chemistry problems. Specifically, finding the ground-state energy associated with the electron configuration problem, using the variational quantum eigensolver (VQE) algorithm for several different molecules. We manage to achieve chemical accuracy both in simulation for several molecules and on one of IBM’s NISQ devices for the H2 molecule in the STO-3G basis, without the need for extensive error mitigation. Our results are compared to a common gate-based ansatz and show better accuracy and significant latency reduction—up to 7× shorter ansatz schedules.
Closed bipartite quantum systems subject to fast local unitary control are studied using quantum optimal control theory and a method of reduced control systems based on the Schmidt decomposition. Particular focus is given to the time-optimal generation of maximally entangled states and product states, as well as to the problem of stabilizing quantum states with a certain amount of entanglement. Explicit analytical solutions are given for general systems consisting of two qubits (as well as for bosonic and fermionic analogues) and also for a class of systems consisting of two coupled qutrits which is studied using the Pontryagin Maximum Principle.
Quantum adaptive algorithms offer the potential for significant speedups in complex computations, particularly in areas like quantum chemistry and materials science. However, achieving optimal performance often requires careful optimization of quantum parameters, a task that can be computationally expensive. This paper introduces a novel quantum adaptive algorithm that dynamically adjusts quantum parameters to optimize algorithm efficiency and accuracy, thereby reducing computational costs. We propose a method centered around a feedback loop that continuously monitors and adjusts the parameters based on the algorithm's output, leading to a more robust and adaptable optimization strategy. The core mechanism leverages a dynamic parameter tuning process to achieve this. This research explores the potential of this approach in tackling specific challenges within quantum algorithm design and provides a framework for future development. The key innovation lies in the continuous feedback loop that dynamically adjusts parameters, resulting in a more efficient and accurate algorithm.
Quantum-inspired algorithms offer a promising avenue for enhancing the performance of optimal control in stochastic systems, particularly those exhibiting complex and uncertain dynamics. This paper investigates the potential of incorporating quantum-like principles – specifically superposition and entanglement – into standard control techniques to mitigate the inherent challenges of these systems. We present a novel framework for adapting conventional control methods by leveraging these principles, demonstrating improvements in performance through simulation and analysis. The core claim focuses on creating algorithms that effectively handle the inherent randomness and sensitivity of stochastic systems.