Investigating Quantum Circuit Designs Using Neuro-Evolution

TL;DR

This paper introduces EXAQC, an evolutionary algorithm for automated quantum circuit design that optimizes structure and parameters, improving scalability, hardware compatibility, and performance in quantum machine learning tasks.

Contribution

The paper presents a novel evolutionary approach, EXAQC, for automated quantum circuit design that jointly optimizes multiple circuit features while respecting hardware constraints.

Findings

Achieved over 90% accuracy on benchmark classification datasets.

Evolved circuits can emulate target quantum states with high fidelity.

Supports both Qiskit and Pennylane for flexible implementation.

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.