Abstract Most of the existing qubit mapping algorithms are deterministic, resulting in a lack of diversity in the generated quantum circuit mappings, which makes it difficult to strike a balance between quality and diversity, and prevents them from being flexibly adapted to different quantum computing tasks. To solve this problem, this paper proposes the Genetic Algorithm-based Quantum Circuit Mapping (GAQCM) framework, which is based on the idea of genetic algorithm, evaluates the quality of mapping by the fitness function, and continuously improves the mapping scheme through several iterations. The GAQCM framework is designed to be flexible, providing a variety of choices, crossover operations and several unique mutation methods are designed to improve the performance of the algorithm. The framework also introduces a neighbourhood gate-based initialisation strategy to improve the algorithm efficiency and convergence speed. Experimental results show that the GAQCM framework reduces the number of SWAP gates by an average of 44.6% and 62.0%, and reduces the hardware gate overhead by 13.8% and 14.9% in the t|ket > and Qiskit compilers, respectively, compared to 2QAN. The framework allows users to customise the fitness function and parameter settings according to their specific needs, and is suitable for a variety of quantum computing tasks.
Abstract Most of the existing qubit mapping algorithms are deterministic, resulting in a lack of diversity in the generated quantum circuit mappings, which makes it difficult to strike a balance between quality and diversity, and prevents them from being flexibly adapted to different quantum computing tasks. To solve this problem, this paper proposes the Genetic Algorithm-based Quantum Circuit Mapping (GAQCM) framework, which is based on the idea of genetic algorithm, evaluates the quality of mapping by the fitness function, and continuously improves the mapping scheme through several iterations. The GAQCM framework is designed to be flexible, providing a variety of choices, crossover operations and several unique mutation methods are designed to improve the performance of the algorithm. The framework also introduces a neighbourhood gate-based initialisation strategy to improve the algorithm efficiency and convergence speed. Experimental results show that the GAQCM framework reduces the number of SWAP gates by an average of 44.6% and 62.0%, and reduces the hardware gate overhead by 13.8% and 14.9% in the t|ket > and Qiskit compilers, respectively, compared to 2QAN. The framework allows users to customise the fitness function and parameter settings according to their specific needs, and is suitable for a variety of quantum computing tasks.
In response to the current challenges in quantum circuit mapping algorithms, such as low mapping quality, insufficient diversity, and the difficulty in balancing mapping quality and diversity, a Quantum Multi-Strategy Fusion Mapping Optimization Algorithm (QMS-FMO) is proposed in this paper. This algorithm enhances population diversity by integrating the neighboring gate strategy of Genetic Algorithm-based Quantum Circuit Mapping (GAQCM), the quantum state characteristics of QPSO, and the parameter adjustment strategy of GSQPSO. The fitness function, which comprehensively considers SWAP gates, CNOT gate costs, particle position information, and quantum behavioral factors, is designed with an adaptive adjustment mechanism to accurately evaluate mapping solutions. During solution updating, individuals are selected using the GAQCM selection strategy, and position update formulas from QPSO and GSQPSO are combined with crossover and mutation operations based on qubit coupling strength, leveraging quantum characteristics to avoid local optima. Experimental results demonstrate that, compared with the t|ket> and Qiskit compilers, QMS-FMO reduces the number of inserted SWAP gates by 46.1% compared to t|ket> and 65.2% compared to Qiskit for specific qubit scales, significantly improving the quality and efficiency of quantum circuit mapping.
Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, 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. Our analysis revealed that, while the "swap, addition" strategy achieved the highest fidelity scores, it consistently increased circuit depth. In contrast, combining "swap, addition, delete" strategies offers a more balanced approach, delivering near-optimal results while also having the potential of reducing circuit depth.
Multi‐objective optimization in quantum systems poses formidable challenges stemming from the high dimensionality, complex constraints, and conflicting performance metrics inherent to quantum control tasks and circuit synthesis. This study presents a comprehensive exploration of genetic AI algorithms—evolutionary strategies inspired by natural selection—tailored specifically for multi‐objective optimization in quantum computing contexts. We propose a hybrid framework that synergistically combines the elitist, Pareto‐based selection mechanics of NSGA-II with quantum‐inspired crossover and mutation operators designed to preserve entanglement structures and exploit hardware‐specific gate sets. In this framework, candidate solutions encoding circuit topologies or control‐pulse parameters undergo iterative evolution, guided by fitness vectors that balance gate fidelity, circuit depth, and resource overhead. To benchmark performance, we apply the algorithm to canonical quantum computing problems: Fourier transform circuits, QAOA ansätze for MaxCut instances, and random Clifford+T circuits. Through extensive simulation on noise-modelled quantum devices, we evaluate Pareto front coverage, convergence speed, diversity maintenance, and robustness under decoherence. Our results demonstrate that the proposed genetic AI algorithm outperforms classical single‐objective genetic methods and established multi‐objective evolutionary algorithms (e.g., SPEA2) by achieving broader Pareto fronts and faster convergence rates, while maintaining solution diversity. Furthermore, noise-resilience experiments show graceful degradation, indicating practical applicability on near-term quantum hardware. These findings substantiate the potential of evolutionary computation paradigms—augmented with quantum‐inspired operators—to address the intricate trade-off surfaces that define NISQ-era optimization problems, paving the way toward automated quantum circuit design and control parameter tuning for real-world quantum computing applications.
We propose a gate-based Quantum Genetic Algorithm (QGA) for real-valued global optimization. In this model, individuals are represented by quantum circuits whose measurement outcomes are decoded into real-valued vectors through binary discretization. Evolutionary operators act directly on circuit structures, allowing mutation and crossover to explore the space of gate-based encodings. Both fixed-depth and variable-depth variants are introduced, enabling either uniform circuit complexity or adaptive structural evolution. Fitness is evaluated through quantum sampling, using the mean decoded output of measurement outcomes as the argument of the objective function. To isolate the impact of quantum resources, we compare gate sets with and without the Hadamard gate, showing that superposition consistently improves convergence and robustness across benchmark functions such as the Rastrigin function. Furthermore, we demonstrate that introducing pairwise inter-individual entanglement in the population accelerates early convergence, revealing that quantum correlations among individuals provide an additional optimization advantage. Together, these results show that both superposition and entanglement enhance the search dynamics of evolutionary quantum algorithms, establishing gate-based QGAs as a promising framework for quantum-enhanced global optimization.
Neutral atom quantum computers (NAQC) are emerging as a promising, scalable quantum computing platform because of their long qubit coherence, flexible qubit arrangement, and multiqubit gate capabilities. However, circuit execution often requires physically moving qubits, making compilation a critical optimization challenge. We propose a circuit independent mathematical framework built on graph-theoretic combinatorial optimization that determines the minimal number of required qubit transfers. This model captures spatial constraints specific to NAQC platforms with zone-limited gate operations and multi-qubit gates. From this framework, we encode the qubit mapping problem as a nonlinear integer program and solve it using a genetic algorithm, enabling trade-offs between minimizing the total traveled distance and the number of parallel transfer operations. Compared to the state-of-the-art scalable compiler for zoned architectures, our approach consistently finds fewer transfers. Depending on the optimization focus, our method produces shorter traveled distances or fewer parallel transfer operations. This work provides both theoretical guaranties and a practical tool for efficient, architecture-aware quantum circuit compilation. As a result, practitioners can generate hardware-aware mappings that reduce movement-induced errors and better exploit atom transfer parallelism, directly improving execution efficiency on NAQC devices.
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. ---