Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

TL;DR

This work tackles the challenge of efficiently synthesizing quantum circuits for NISQ hardware by evaluating how mutation strategies within a GA framework affect performance. It introduces a modular quantum circuit environment with a Clifford+T optimizer and fidelity-focused fitness that balances depth and T-count, and it uses a two-stage hyperparameter search to compare mutation strategies across 4–6 qubit problems. The findings show that swap and delete mutations, especially in combination, yield the most efficient and robust quantum circuit optimizers, while changes to mutation rate and adaptive schemes offer limited gains. The study provides empirical guidance for designing GA-based quantum circuit optimizers and suggests avenues for scaling to larger systems and benchmarking against alternative optimization methods.

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.

Figures (17)

• Figure 1: The GA optimization process. Yellow represents the outer layer, orange represents GA steps, and red represents the repeated optimization cycle. White boxes indicate inputs, blue boxes indicate outputs, green boxes indicate essential steps, and purple boxes indicate optional steps.
• Figure 2: An example list of quantum operations and its corresponding circuit.
• Figure 3: Using Python lists to define circuit depth and operations for each candidate during population initialization.
• Figure 4: An example of the change strategy. The modified operation is highlighted in orange.
• Figure 5: An example of the delete strategy. The removed operation is highlighted in orange.