Scalable Multi-Objective Genetic Algorithm for Quantum Circuit Optimization

Scalable Multi-Objective Genetic Algorithm for Quantum Circuit Optimization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Scalable Multi-Objective Genetic Algorithm for Quantum Circuit Optimization Roumaissa Ghlib, Rania Bouhadouza, Faicel HNAIEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8490170/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The design of quantum circuits that are compact, efficient, and compatible with Noisy Intermediate-Scale Quantum (NISQ) hardware remains a key challenge in quantum computing. Most current approaches rely on a fidelity-based fitness function that requires computing the full unitary matrix. This becomes infeasible beyond 10–12 qubits due to exponential memory and time complexity. In this work we present a scalable multi-objective genetic algorithm for quantum circuit optimization targeting NISQ devices. To overcome the limitations of full-unitary fidelity evaluation, we introduce two efficient strategies: a projection-based approximation and a structure-aware local metric. Integrated into NSGA-II, our approach reduces computational cost while preserving fidelity, depth, and hardware compatibility. ponentially, making traditional algorithms—despite heuristics—difficult to scale efficiently. Quantum computing introduces a new paradigm by leveraging principles such as superposition and entanglement (1). Hybrid algorithms like the Quantum Approximate Optimization Algorithm (QAOA) (2) and the Varia-tional Quantum Eigensolver (VQE) (3) combine quantum circuits with classical optimizers to solve such problems, making them well-suited for today’s Noisy Intermediate-Scale Quantum (NISQ) hardware (4). However, running quantum algorithms on real hardware is challenging. NISQ devices are limited by gate fidelity, qubit connectivity, and decoher-ence, making deep or gate-heavy circuits impractical. Multi-qubit gates in particular, like CNOT, significantly reduce overall fidelity (5; 6; 7; 8), requiring careful trade-offs in circuit design. To address these challenges, evolutionary algorithms—especially genetic algorithms—have gained interest for automatically generating quantum circuits that balance fidelity, gate count, and depth (9; 10; 11). Yet most rely on evaluating full unitary matrices, which becomes computationally infeasible beyond 10–12 qubits due to exponential scaling (12). In this work, we make the following contributions: ; We introduce a scalable quantum circuit optimization framework built on a multi-objective genetic algorithm. ; We propose two scalable fidelity evaluation methods that avoid the need to compute full unitary matrices, which significantly reduces the computational overhead while preserving fidelity relevance. ; We integrate these fidelity strategies into a Pareto-based NSGA-II optimization loop that jointly minimizes fidelity error, circuit depth, and gate cost. ; We validate the framework on a diverse set of circuits, including random circuits and problem-oriented instances such as QAOA and VQE, demonstrating improved scalability and enhanced hardware-aware circuit design quality. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Quantum computing NSGA-II Multi-objective optimization Circuit synthesis Fidelity Circuit optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 24 Jan, 2026 Reviews received at journal 22 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers invited by journal 09 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Editor invited by journal 09 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8490170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":573229426,"identity":"84bb70a5-93b9-4c0c-8899-476411047769","order_by":0,"name":"Roumaissa Ghlib","email":"","orcid":"","institution":"École Nationale Supérieure d’Informatique (ESI),","correspondingAuthor":false,"prefix":"","firstName":"Roumaissa","middleName":"","lastName":"Ghlib","suffix":""},{"id":573229427,"identity":"68f76dfe-6848-4b7a-9545-44b5943beb57","order_by":1,"name":"Rania Bouhadouza","email":"","orcid":"","institution":"École Nationale Supérieure d’Informatique 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Most current approaches rely on a fidelity-based fitness function that requires computing the full unitary matrix. This becomes infeasible beyond 10–12 qubits due to exponential memory and time complexity. In this work we present a scalable multi-objective genetic algorithm for quantum circuit optimization targeting NISQ devices. To overcome the limitations of full-unitary fidelity evaluation, we introduce two efficient strategies: a projection-based approximation and a structure-aware local metric. Integrated into NSGA-II, our approach reduces computational cost while preserving fidelity, depth, and hardware compatibility. ponentially, making traditional algorithms—despite heuristics—difficult to scale efficiently. Quantum computing introduces a new paradigm by leveraging principles such as superposition and entanglement (1). Hybrid algorithms like the Quantum Approximate Optimization Algorithm (QAOA) (2) and the Varia-tional Quantum Eigensolver (VQE) (3) combine quantum circuits with classical optimizers to solve such problems, making them well-suited for today’s Noisy Intermediate-Scale Quantum (NISQ) hardware (4). However, running quantum algorithms on real hardware is challenging. NISQ devices are limited by gate fidelity, qubit connectivity, and decoher-ence, making deep or gate-heavy circuits impractical. Multi-qubit gates in particular, like CNOT, significantly reduce overall fidelity (5; 6; 7; 8), requiring careful trade-offs in circuit design. To address these challenges, evolutionary algorithms—especially genetic algorithms—have gained interest for automatically generating quantum circuits that balance fidelity, gate count, and depth (9; 10; 11). Yet most rely on evaluating full unitary matrices, which becomes computationally infeasible beyond 10–12 qubits due to exponential scaling (12). 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