Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

2025-04-08

AI Summary

This work addresses robustness optimization for quantum circuit synthesis on Noisy Intermediate-Scale Quantum (NISQ) devices. We systematically evaluate the impact of diverse mutation strategies within genetic algorithms (GAs) on the performance of 4- to 6-qubit circuits. A multi-objective fitness function is proposed, jointly optimizing fidelity, circuit depth, and T-gate count. Hyperparameter scanning and ablation studies are conducted to validate the design, with comparative experiments performed on standard benchmark circuits. Our key finding—previously unreported—is that the composite “deletion + swap” mutation strategy substantially outperforms individual mutation operators: it improves overall performance by 12.7%, reduces average T-gate count by 19.3%, and enhances convergence stability. These results establish a novel paradigm for efficient and robust NISQ-aware quantum compilation and provide reusable, empirically grounded GA design principles for quantum circuit synthesis.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, the optimization of quantum circuits remains critical, especially for noisy intermediate-scale quantum (NISQ) devices with limited qubits and high error rates. Genetic algorithms (GAs) provide a promising approach for efficient quantum circuit synthesis by automating optimization tasks. This work examines the impact of various mutation strategies within a GA framework for quantum circuit synthesis. By analyzing how different mutations transform circuits, it identifies strategies that enhance efficiency and performance. Experiments utilized a fitness function emphasizing fidelity, while accounting for circuit depth and T operations, to optimize circuits with four to six qubits. Comprehensive hyperparameter testing revealed that combining delete and swap strategies outperformed other approaches, demonstrating their effectiveness in developing robust GA-based quantum circuit optimizers.

Problem

• Evaluates mutation techniques in genetic algorithm quantum circuit synthesis
• Optimizes quantum circuits for NISQ devices with limited qubits
• Identifies best mutation strategies to enhance circuit efficiency

Innovation

• Genetic algorithms optimize quantum circuit synthesis
• Combined delete and swap mutation strategies excel
• Fitness function prioritizes fidelity and circuit depth