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

The Journal of Physical Chemistry

Genetic Algorithm with Migration on Topology Conserving Maps for Optimal Control of Quantum Systems

Bjarne Amstrup, Gábor J. Tóth, Gabor Szabo, HERSCHEL A. RABITZ, András Louml rincz

Published Apr 1, 1995• 45 citations
Publisher

Abstract

The laboratory implementation of molecular optimal control has to overcome the problem caused by the changing environmental parameters, such as the temperature of the laser rod, the resonator parameters, the mechanical parameters of the laboratory equipment and other dependent parameters such as the time delay between pulses or the pulse amplitudes. In this paper a solution is proposed: instead of trying to set the parameter(s) with very high precision, their changes are monitored and the control is adjusted to the current values. The optimization in the laboratory can then be run at several values of the parameter(s) with an extended genetic algorithm (GA) which is tailored to such parametric optimization. The extended GA does not presuppose, but can take advantage, and in fact, explores whether the mapping from the parameter(s) to optimal control field is continuous. Then the op1 timization for the different values of the parameter(s) is done cooperatively which reduces the optimizat...

DOI
10.1021/j100014a048

Related papers

Based on Genetic Algorithm for Quantum Circuit Optimization

Jincheng Zhang

This paper explores the application of genetic algorithms (GAs) to optimize quantum circuits. The core claim is that GA can effectively improve the efficiency and accuracy of quantum computation by strategically modifying the topology and control parameters of quantum circuits. The proposed mechanism treats a quantum circuit as a genetic individual, leveraging the principles of genetic algorithms – including crossover, mutation, and selection – to iteratively enhance circuit performance. This approach represents a novel method for quantum circuit optimization, offering a potentially robust and adaptable solution compared to traditional optimization techniques. We present a framework for implementing this approach, focusing on the algorithmic steps and considerations for successful application. The exploration will cover the challenges and potential benefits of integrating evolutionary algorithms with the unique characteristics of quantum computation. ---

•Sep 5, 2026•0 citations

Genetic algorithm based quantum circuit synthesis with adaptive parameters control

Cristian Ruican, Mihai Udrescu, Lucian Prodan +1 more

Genetic algorithms were among the early choices for quantum circuit synthesis, because of their ability to evolve a given starting circuit towards one of the possible solutions. The synthesis method presented here is the first GA-based approach that dynamically adjusts its control parameters. The adaptive parameter control takes into account the analysis performed on each genetic operator, in order to automatically find an acceptable tradeoff between runtime and appropriate exploration. The experimental results prove that this method improves the synthesis runtime and the size of the circuit to be handled up to 7 qubits (previous GA-based techniques are effective only for 3-4 qubit circuits).

IEEE Congress on Evolutionary Computation (CEC)•May 1, 2009•9 citations

Quantum computing control system configuration optimizer with adaptive genetic algorithm

Shucheng Huang, Junchao Wang, Jianmin Pang +1 more

As quantum computing continues to advance, the complexity of control systems for superconducting qubits also increases. This paper presents a genetic algorithm (GA) approach for optimizing the control systems in quantum computing. Focusing on minimizing costs, maximizing resource utilization, and reducing device count, the study demonstrates the algorithm's effectiveness in configuring control systems efficiently. Experimental results from five quantum chips, ranging from 64 to 1024 bits, demonstrate significant improvements in cost efficiency and device management after 500 generations of optimization. The findings highlight the algorithm's adaptability to different quantum system scales and its potential in enhancing the scalability and efficiency of quantum computing architectures.

International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE)•Oct 16, 2024•0 citations

Efficient algorithms for the laboratory discovery of optimal quantum controls

Gabriel Turinici, Claude Le Bris, HERSCHEL A. RABITZ

The laboratory closed-loop optimal control of quantum phenomena, expressed as minimizing a suitable cost functional, is currently implemented through an optimization algorithm coupled to the experimental apparatus. In practice, the most commonly used search algorithms are variants of genetic algorithms. As an alternative choice, a direct search deterministic algorithm is proposed in this paper. For the simple simulations studied here, it outperforms the existing approaches. An additional algorithm is introduced in order to reveal some properties of the cost functional landscape.

Physical Review E•Jul 20, 2004•20 citations

Optimizing an Electron's Path to Ionization Using a Genetic Algorithm

Jason J. Bennett, Kevin Choice

A Rydberg atom is an atom with a highly excited and weakly bound valence electron. A widespread method of studying quantum mechanics with Rydberg atoms is to ionize the electron and measure its arrival time. We use a Genetic Algorithm (GA) to control the electron's path to ionization. The Rydberg electron's energy levels are strongly shifted by the presence of an electric field. The energy levels shift and curve, but never cross. At an avoided crossing the electron can jump from one level to the next. By engineering the electric field's time dependence, we thereby control the path to ionization. A GA is an optimization method modeled on natural selection. We use a GA to evolve electric field pulses to achieve a target path to ionization. Our algorithm initially generates random members of a population and then assigns each member a fitness score based on how well they achieve our target solution. We use elitism to pass the best members of the population directly into the next generation. We use tournament style selection to choose fit members to mate and pass their genes to the next generation. We then mutate the offspring to provide genetic diversity to our population. We present our results on the effects of varying GA parameters and modifying the GA to better model the experiment.

•Jan 1, 2017•0 citations

Nonlinear Quantum Topology: Adaptive Mapping via Geometric Path Optimization

Jincheng Zhang

Quantum topology, a fascinating field, offers a unique perspective on the properties of space and time. This paper explores the application of non-linear quantum topology to adaptive mapping, specifically through the use of geometric path optimization. We introduce a novel algorithm that leverages the dynamic evolution of quantum topology represented by geometric paths to achieve precise parameter control and optimization. The core mechanism centers around constructing a quantum path representation, modeling its evolution, and employing optimization techniques tailored to these evolving paths. The proposed framework offers a fundamentally new approach to controlling quantum systems, promising advancements in quantum information processing and manipulation. This work provides a conceptual foundation for exploring complex quantum systems through a novel mapping strategy.

•Aug 31, 2026•0 citations
The laboratory implementation of molecular optimal control has to overcome the problem caused by the changing environmental parameters, such as the temperature of the laser rod, the resonator parameters, the mechanical parameters of the laboratory equipment and other dependent parameters such as the time delay between pulses or the pulse amplitudes. In this paper a solution is proposed: instead of trying to set the parameter(s) with very high precision, their changes are monitored and the control is adjusted to the current values. The optimization in the laboratory can then be run at several values of the parameter(s) with an extended genetic algorithm (GA) which is tailored to such parametric optimization. The extended GA does not presuppose, but can take advantage, and in fact, explores whether the mapping from the parameter(s) to optimal control field is continuous. Then the op1 timization for the different values of the parameter(s) is done cooperatively which reduces the optimizat...
This paper explores the application of genetic algorithms (GAs) to optimize quantum circuits. The core claim is that GA can effectively improve the efficiency and accuracy of quantum computation by strategically modifying the topology and control parameters of quantum circuits. The proposed mechanism treats a quantum circuit as a genetic individual, leveraging the principles of genetic algorithms – including crossover, mutation, and selection – to iteratively enhance circuit performance. This approach represents a novel method for quantum circuit optimization, offering a potentially robust and adaptable solution compared to traditional optimization techniques. We present a framework for implementing this approach, focusing on the algorithmic steps and considerations for successful application. The exploration will cover the challenges and potential benefits of integrating evolutionary algorithms with the unique characteristics of quantum computation. ---
Genetic algorithms were among the early choices for quantum circuit synthesis, because of their ability to evolve a given starting circuit towards one of the possible solutions. The synthesis method presented here is the first GA-based approach that dynamically adjusts its control parameters. The adaptive parameter control takes into account the analysis performed on each genetic operator, in order to automatically find an acceptable tradeoff between runtime and appropriate exploration. The experimental results prove that this method improves the synthesis runtime and the size of the circuit to be handled up to 7 qubits (previous GA-based techniques are effective only for 3-4 qubit circuits).
As quantum computing continues to advance, the complexity of control systems for superconducting qubits also increases. This paper presents a genetic algorithm (GA) approach for optimizing the control systems in quantum computing. Focusing on minimizing costs, maximizing resource utilization, and reducing device count, the study demonstrates the algorithm's effectiveness in configuring control systems efficiently. Experimental results from five quantum chips, ranging from 64 to 1024 bits, demonstrate significant improvements in cost efficiency and device management after 500 generations of optimization. The findings highlight the algorithm's adaptability to different quantum system scales and its potential in enhancing the scalability and efficiency of quantum computing architectures.
The laboratory closed-loop optimal control of quantum phenomena, expressed as minimizing a suitable cost functional, is currently implemented through an optimization algorithm coupled to the experimental apparatus. In practice, the most commonly used search algorithms are variants of genetic algorithms. As an alternative choice, a direct search deterministic algorithm is proposed in this paper. For the simple simulations studied here, it outperforms the existing approaches. An additional algorithm is introduced in order to reveal some properties of the cost functional landscape.
A Rydberg atom is an atom with a highly excited and weakly bound valence electron. A widespread method of studying quantum mechanics with Rydberg atoms is to ionize the electron and measure its arrival time. We use a Genetic Algorithm (GA) to control the electron's path to ionization. The Rydberg electron's energy levels are strongly shifted by the presence of an electric field. The energy levels shift and curve, but never cross. At an avoided crossing the electron can jump from one level to the next. By engineering the electric field's time dependence, we thereby control the path to ionization. A GA is an optimization method modeled on natural selection. We use a GA to evolve electric field pulses to achieve a target path to ionization. Our algorithm initially generates random members of a population and then assigns each member a fitness score based on how well they achieve our target solution. We use elitism to pass the best members of the population directly into the next generation. We use tournament style selection to choose fit members to mate and pass their genes to the next generation. We then mutate the offspring to provide genetic diversity to our population. We present our results on the effects of varying GA parameters and modifying the GA to better model the experiment.
Quantum topology, a fascinating field, offers a unique perspective on the properties of space and time. This paper explores the application of non-linear quantum topology to adaptive mapping, specifically through the use of geometric path optimization. We introduce a novel algorithm that leverages the dynamic evolution of quantum topology represented by geometric paths to achieve precise parameter control and optimization. The core mechanism centers around constructing a quantum path representation, modeling its evolution, and employing optimization techniques tailored to these evolving paths. The proposed framework offers a fundamentally new approach to controlling quantum systems, promising advancements in quantum information processing and manipulation. This work provides a conceptual foundation for exploring complex quantum systems through a novel mapping strategy.