Investigating Quantum Circuit Designs Using Neuro-Evolution

2026-02-03

AI Summary

This work proposes EXAQC, an end-to-end automated design framework for parameterized quantum circuits that integrates neuroevolution with genetic programming. Addressing the limitations of conventional design approaches—particularly their poor scalability, limited flexibility, and inadequate hardware adaptability—EXAQC introduces, for the first time, a neuroevolutionary system to simultaneously optimize quantum gate types, qubit connectivity, parameterization strategies, and circuit depth while explicitly accounting for hardware constraints and noise effects. The framework is compatible with both Qiskit and PennyLane, and demonstrates strong empirical performance: using limited resources, it achieves over 90% accuracy across multiple classification benchmarks and faithfully simulates target quantum states with high fidelity.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• quantum circuit design
• parameterized quantum circuits
• scalability
• hardware efficiency
• quantum machine learning

Innovation

• neuroevolution
• parameterized quantum circuits
• evolutionary search
• quantum circuit design
• hardware-aware optimization