Evolutionary-based searching method for quantum circuit architecture

Yoshio Rubio, Cynthia Olvera, Oscar Montiel

Daniel Johannsen, Piyush P. Kurur, Johannes Lengler

Anqi Zhang, Shengmei Zhao

Quantum architecture search (QAS) is desired to construct a powerful and general QAS platform which can significantly accelerate quantum advantages in error-prone and depth limited quantum circuits in today Noisy Intermediate-Scale Quantum (NISQ) era. In this paper, we propose an evolutionary-based quantum architecture search (EQAS) scheme for the optimal layout to balance the higher expressive power and the trainable ability. In EQAS, each layout of quantum circuits, i.e quantum circuit architecture(QCA), is first encoded into a binary string, which is called quantum genes later. Then, an algorithm to remove the redundant parameters in QCA is performed according to the eigenvalues of the corresponding quantum Fisher information matrix (QFIM). Later, each QCA is evaluated by the normalized fitness, so that the sampling rate could be obtained to sample the parent generation by the Roulette Wheel selection strategy. Thereafter, the mutation and crossover are applied to get the next generation. EQAS is verified by the classification task in quantum machine learning for three datasets. The results show that the proposed EQAS can search for the optimal QCA with less parameterized gates, and the higher accuracies are obtained by adopting EQAS for the classification tasks over three dataset.

Weiwei Zhu, Jiangtao Pi, Qiuyuan Peng

With the rapid development of quantum computing, the variational quantum algorithms capitalize on the classical optimizer and parametrized quantum circuit to provide outperformance on specific tasks such as combinatorial optimization problems. However, the performance of these hybrid quantum-classical algorithms heavily relies on the design of quantum circuit architecture. Being restricted by the noise of the near-term quantum device, how to trade off the computational power of quantum circuits and the noise of quantum gates is a challenging task for design circuit architecture. In this paper, we give a brief view of the recently proposed methods for quantum architecture search including the differentiable circuit search method, deep reinforcement learning based method, and evolutionary based methods.

Taro YABUKI, Hitoshi Iba

Linwei Shang, Hao Cao, Yang Jie Wu +2 more

Quantum neural architecture search (QNAS) has emerged as a promising approach for automatically designing parameterized quantum circuits (PQCs) for near-term quantum machine learning tasks. However, quantum evolutionary algorithm (QEA)-based QNAS methods often suffer from slow distribution concentration and insufficient update adaptivity in high-dimensional discrete search spaces, which limits both search efficiency and final model performance. To address these issues, this paper proposes Structural Probability Adaptive Quantum Neural Architecture Search (SPA-QNAS), a search-dynamics-enhanced QNAS method built upon the EQNAS benchmark framework. SPA-QNAS introduces two complementary mechanisms into the QPV-driven evolutionary search loop: Structural Probability Enhancement (SPE) and Adaptive Evolutionary Control (AEC). SPE reinforces elite structural decisions to accelerate the concentration of the structural sampling distribution toward high-fitness regions, while AEC adaptively regulates the rotation updates of non-elite individuals according to fitness feedback, thereby improving update stability and suppressing ineffective disturbances. Under the same search space, circuit template, and quantum resource budget as EQNAS, SPA-QNAS is evaluated on the MNIST and Warship benchmark datasets. Experimental results across multiple independent runs demonstrate that SPA-QNAS achieves higher classification accuracy and more stable performance compared with EQNAS. In representative experiments, SPA-QNAS achieves classification accuracies of 99.42% on MNIST and 85.33% on Warship under the same search space and quantum resource budget as EQNAS. These results indicate that improving QPV-based evolutionary update dynamics is an effective way to enhance the stability and robustness of QNAS under fixed quantum resource constraints.