Generative Adversarial Networks for Circuit Synthesis with Physical Constraints
Jincheng Zhang
The synthesis of quantum circuits, crucial for realizing quantum algorithms, faces significant challenges in satisfying both functional requirements and physical limitations inherent in quantum hardware. Traditional circuit synthesis methods often struggle to efficiently explore the vast design space while adhering to constraints such as gate fidelity, qubit connectivity, and circuit depth. This work explores the application of Generative Adversarial Networks (GANs) to automate quantum circuit synthesis, directly incorporating physical constraints into the learning process. We propose a framework where a generator network creates quantum circuits, and a discriminator network evaluates these circuits against a defined set of physical constraints. Through adversarial training, the generator learns to produce circuits that are not only functionally correct but also physically realizable, offering a novel approach to quantum circuit design. The core claim of this research is that GANs can be effectively trained to generate quantum circuits satisfying both functional requirements and physical constraints. This approach represents a significant step towards practical quantum circuit synthesis, addressing a critical bottleneck in the development of quantum computing.
•Sep 5, 2026•0 citations