Genetic algorithm for quantum circuit design

Stein, Christoph, Michael Färber

Designing efficient quantum circuits that leverage quantum advantage compared to classical computing has become increasingly critical. Genetic algorithms have shown potential in generating such circuits through artificial evolution. However, integrating quantum advantage into the fitness function of these algorithms remains unexplored. In this paper, we aim to enhance the efficiency of quantum circuit design by proposing two novel approaches for incorporating quantum advantage metrics into the fitness function of genetic algorithms.1 We evaluate our approaches based on the Bernstein-Vazirani Problem and the Unstructured Database Search Problem as test cases. The results demonstrate that our approaches not only improve the convergence speed of the genetic algorithm but also produce circuits comparable to expert-designed solutions. Our findings suggest that automated quantum circuit design using genetic algorithms that incorporate a measure of quantum advantage is a promising approach to accelerating the development of quantum algorithms.

Cristian Ruican, Mihai Udrescu, Lucian Prodan +1 more

Razieh Abdolahi, M. Reza Soleymani, Walaa Hamouda

This paper introduces a novel Quantum Genetic Algorithm (QGA) designed to solve the Quadratic Assignment Problem (QAP), a structure widely applied in communication network optimization, where efficient solutions are critical. This approach utilizes quantum circuits as the objective function, enabling simultaneous evaluation of entire individuals at each generation of the Genetic Algorithm (GA) through quantum superposition and the SWAP test. A preprocessing step involving inverse stereographic projection is applied to ensure proper normalization of data. The method is validated in a case study, analyzing the impact of varying the number of elites and an additional algorithm parameter on solution quality and convergence speed. This work underscores the potential of quantum computing for addressing large-scale optimization challenges in communication networks.

Cristian Ruican, Mihai Udrescu, Lucian Prodan +1 more

Cristian Ruican, Mihai Udrescu, Lucian Prodan +1 more

Devendra Nath Tripathi, Neel Kanth Kundu, Abhishek Dixit

Indoor localization using Received Signal Strength Indication (RSSI) is a notoriously challenging problem due to signal noise from multipath fading and interference. Although quantum machine learning (QML) presents a new paradigm for tackling such problems, the design of effective quantum feature maps remains a significant bottleneck. This paper introduces a novel approach for automated quantum feature map design using a Hierarchical Genetic Algorithm (HGA). We propose a sequential Hybrid Quantum-Classical Neural Network (HQNN) architecture where the initial quantum feature map is not handcrafted but is evolved by the HGA. The chromosome for the GA represents a hierarchical sequence of parametric and nonparametric quantum gate layers, allowing for the discovery of complex data-encoding structures. We evaluated the performance of our approach on wireless indoor user localization datasets. The results are compared with a standard HQNN using a ‘ZZFeatureMap’ proposed in a prior work, demonstrating the potential and challenges of evolutionary quantum circuit design. Our findings indicate that while the HGA effectively explores a vast search space of quantum circuits and improves against the benchmark in majority of cases, achieving superior performance on sparse, high-interference datasets requires careful consideration of the fitness landscape and generalization capabilities of the evolved feature maps.