Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacity
This work addresses the barrier of expert-level knowledge required for quantum machine learning design, offering a method to enhance model capacity for complex tasks.
The paper tackles the challenge of designing high-performance quantum machine learning models by proposing a novel evolutionary optimization method that discovers variational quantum circuit architectures with improved learning capabilities, as demonstrated through numerical simulations.
Recent advancements in quantum computing (QC) and machine learning (ML) have garnered significant attention, leading to substantial efforts toward the development of quantum machine learning (QML) algorithms to address a variety of complex challenges. The design of high-performance QML models, however, requires expert-level knowledge, posing a significant barrier to the widespread adoption of QML. Key challenges include the design of data encoding mechanisms and parameterized quantum circuits, both of which critically impact the generalization capabilities of QML models. We propose a novel method that encodes quantum circuit architecture information to enable the evolution of quantum circuit designs. In this approach, the fitness function is based on the effective dimension, allowing for the optimization of quantum circuits towards higher model capacity. Through numerical simulations, we demonstrate that the proposed method is capable of discovering variational quantum circuit architectures that offer improved learning capabilities, thereby enhancing the overall performance of QML models for complex tasks.