Abstract Designing expressive yet hardware-efficient quantum circuits remains a central challenge in quantum machine learning (QML). Existing approaches to quantum circuit synthesis primarily optimize for classical performance metrics, often overlooking intrinsic quantum characteristics that govern circuit quality and generalization. This work introduces a quantumness-aware genetic optimization (QAGO) framework for circuit synthesis that integrates quantum properties such as entanglement and non-Clifford gate utilization into the evolutionary design process. We formulate circuit synthesis as both a single-objective optimization (SOO) and a multi-objective optimization (MOO) problem within a genetic framework. The SOO formulation explores quantumness-aware objectives balancing predictive performance and circuit-level quantum structure, while the MOO formulation approximates Pareto-efficient trade-offs between these goals. This dual perspective enables systematic analysis of how increasing quantum expressivity influences predictive behavior and resource cost. Experimental evaluations across three quantum kernel-based algorithms include quantum support vector machines, quantum kernel trainers, and projected quantum kernels demonstrate that circuits evolved under quantumness-aware objectives consistently outperform baseline kernel circuits such as ZZFeatureMap. Comprehensive benchmarking against classical baselines shows that QAGO achieves up to 2.5% area under the ROC curve improvement on the Higgs Boson dataset and 1.5%–9.2% gains across six of nine targets in the LIT-PCBA drug discovery benchmark. Compared to prior work (Wu et al 2021 J. Phys. G: Nucl. Part. Phys. 48 125003, Mensa et al 2023 Mach. Learn.: Sci. Technol. 4 015023) QAGO yields up to 4% improvement on Higgs and 5%–20% gains across eight of nine targets. These results provide quantitative evidence that optimizing for quantumness enhances both performance and quantum expressivity of QML models, establishing QAGO as a principled framework for quantumness-aware circuit evolution.
Abstract Designing expressive yet hardware-efficient quantum circuits remains a central challenge in quantum machine learning (QML). Existing approaches to quantum circuit synthesis primarily optimize for classical performance metrics, often overlooking intrinsic quantum characteristics that govern circuit quality and generalization. This work introduces a quantumness-aware genetic optimization (QAGO) framework for circuit synthesis that integrates quantum properties such as entanglement and non-Clifford gate utilization into the evolutionary design process. We formulate circuit synthesis as both a single-objective optimization (SOO) and a multi-objective optimization (MOO) problem within a genetic framework. The SOO formulation explores quantumness-aware objectives balancing predictive performance and circuit-level quantum structure, while the MOO formulation approximates Pareto-efficient trade-offs between these goals. This dual perspective enables systematic analysis of how increasing quantum expressivity influences predictive behavior and resource cost. Experimental evaluations across three quantum kernel-based algorithms include quantum support vector machines, quantum kernel trainers, and projected quantum kernels demonstrate that circuits evolved under quantumness-aware objectives consistently outperform baseline kernel circuits such as ZZFeatureMap. Comprehensive benchmarking against classical baselines shows that QAGO achieves up to 2.5% area under the ROC curve improvement on the Higgs Boson dataset and 1.5%–9.2% gains across six of nine targets in the LIT-PCBA drug discovery benchmark. Compared to prior work (Wu et al 2021 J. Phys. G: Nucl. Part. Phys. 48 125003, Mensa et al 2023 Mach. Learn.: Sci. Technol. 4 015023) QAGO yields up to 4% improvement on Higgs and 5%–20% gains across eight of nine targets. These results provide quantitative evidence that optimizing for quantumness enhances both performance and quantum expressivity of QML models, establishing QAGO as a principled framework for quantumness-aware circuit evolution.
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.
Designing effective quantum circuits remains a central challenge in quantum computing, as circuit structure strongly influences expressivity, trainability, and hardware feasibility. Current approaches, whether using manually designed circuit templates, fixed heuristics, or automated rules, face limitations in scalability, flexibility, and adaptability, often producing circuits that are poorly matched to the specific problem or quantum hardware. In this work, we propose the Evolutionary eXploration of Augmenting Quantum Circuits (EXAQC), an evolutionary approach to the automated design and training of parameterized quantum circuits (PQCs) which leverages and extends on strategies from neuroevolution and genetic programming. The proposed method jointly searches over gate types, qubit connectivity, parameterization, and circuit depth while respecting hardware and noise constraints. The method supports both Qiskit and Pennylane libraries, allowing the user to configure every aspect. This work highlights evolutionary search as a critical tool for advancing quantum machine learning and variational quantum algorithms, providing a principled pathway toward scalable, problem-aware, and hardware-efficient quantum circuit design. Preliminary results demonstrate that circuits evolved on classification tasks are able to achieve over 90% accuracy on most of the benchmark datasets with a limited computational budget, and are able to emulate target circuit quantum states with high fidelity scores.
Abstract Designing compact and efficient quantum circuits that are compatible with Noisy Intermediate-Scale Quantum (NISQ) hardware remains a central challenge in quantum computing. Most existing optimization approaches rely on fidelity-based fitness functions that require computing the full unitary matrix of the circuit. However, this quickly becomes computationally intractable beyond 10–12 qubits due to the exponential memory and time complexity O(22n) . In this work, we propose a scalable multi-objective genetic algorithm for quantum circuit optimization tailored to NISQ devices. Although evolutionary algorithms have demonstrated strong potential for circuit synthesis, current methods generally depend on full-unitary fidelity evaluation, which severely limits scalability. To address this bottleneck, we introduce two complementary strategies: (1) an independent block-based evaluation using graph partitioning, and (2) an overlapping sliding-window decomposition approach. Both strategies reduce computational complexity from O(22n) to O(22k) , where k≪n , making it possible to optimize circuits with more than 20 qubits in practice. Our methods are integrated into the NSGA-II multi-objective framework, enabling simultaneous optimization of fidelity, circuit depth, and gate cost, while maintaining structural compatibility with NISQ hardware–without requiring full-unitary simulation. Experimental results on benchmark circuits demonstrate high fidelity (above 0.94 for small circuits up to 8 qubits, above 0.85 for medium-scale circuits of 10–16 qubits, and above 0.80 for large-scale circuits up to 32 qubits), up to 45% reduction in circuit depth, and a 10× speedup compared to exact evaluation at 14 qubits.
Designing quantum circuits is a major challenge due to their sensitivity to noise and the complexity of quantum logic. This paper presents an evolutionary algorithm that balances multiple design criteria, namely fidelity and circuit depth, by combining them into a single scalarised fitness function. This approach enables efficient optimisation while avoiding the computational cost of fully multi-objective methods. A novel circuit representation and set of noise-aware genetic operators are introduced, and the technique is applied to evolve implementations of the Quantum Fourier Transform (QFT) for two- and three-qubit systems. Experimental results show that evolved circuits match or exceed ideal fidelity and outperform textbook QFT implementations under simulated noise. These findings highlight the potential of hardware-aware evolutionary design for producing circuits that are both accurate and resilient on near-term quantum hardware.
Quantum Support Vector Machines (QSVM) is one of the most promising frameworks in quantum machine learning, yet their performance depends on the design of the feature map. Conventional approaches rely on fixed quantum circuits, which often fail to generalize across datasets. To address this limitation, we propose GA-QSVM, a hybrid framework that employs Genetic Algorithms (GA) to automatically optimize feature maps. The proposed method introduces a configurable framework that flexibly defines the evolutionary parameters, enabling the construction of adaptive circuits. Experimental evaluation of datasets, including Digits, Fashion, Wine, and Breast Cancer, demonstrates that GA-QSVMs achieve a comparable accuracy compared to classical SVMs and standard QSVMs. Furthermore, transfer learning results indicate that GA-QSVM's circuits generalize effectively across datasets. These findings highlight the potential of evolutionary strategies to automate and enhance kernel design for future quantum machine learning applications.
Quantum algorithms will likely play a key role in future high-performance-computing (HPC) environments. These algorithms are typically expressed as quantum circuits composed of arbitrary gates or as unitary matrices. Executing these on physical devices, however, requires translation to device-compatible circuits, in a process called quantum compilation or circuit synthesis, since these devices support a limited number of native gates. Moreover, these devices typically have specific qubit topologies, which constrain how and where gates can be applied. Consequently, logical qubits in input circuits and unitaries may need to be mapped to and routed between physical qubits. Furthermore, current Noisy Intermediate-Scale Quantum (NISQ) devices present additional constraints. They are vulnerable to errors during gate application and their short decoherence times lead to qubits rapidly succumbing to accumulated noise and possibly corrupting computations. Therefore, circuits synthesized for NISQ devices need to minimize gates and execution times. The problem of synthesizing device-compatible circuits, while optimizing for low gate count and short execution times, can be shown to be computationally intractable using analytical methods. Therefore, interest has grown towards heuristics-based synthesis techniques, which are able to produce approximations of the desired algorithm, while optimizing depth and gate-count. In this work, we investigate using genetic algorithms (GA)—a proven gradient-free optimization technique based on natural selection—for circuit synthesis. In particular, we formulate the quantum synthesis problem as a multi-objective optimization (MOO) problem, with the objectives of minimizing the approximation error, number of multi-qubit gates, and circuit depth. We also employ fuzzy logic for runtime parameter adaptation of GA to enhance search efficiency and solution quality in our proposed method.