Generator and training technique in quantum generative adversarial networks

Parameterized quantum circuits play an essential role in the performance of many variational hybrid quantum-classical (HQC) algorithms. One challenge in implementing such algorithms is to choose an effective circuit that well represents the solution space while maintaining a low circuit depth and number of parameters. In this paper, we delve into the influence of utilizing quantum circuits with different types, depths, and widths in the Quantum Generative Adversarial Network (QGAN) generator on its overall performance. Our findings reveal that employing parametric two-qubit gates in the QGAN generator slightly outperforms the use of fixed-parameter two-qubit gates. Additionally, as the number of quantum circuit layers increases (i.e., the circuit depth), the performance of the QGAN generator improves, though there exists ’saturation’ where further layer addition leads to a decline in performance. Furthermore, considering the number of qubits available for the QGAN generator to accomplish the generation task, we observe a gradual decline in QGAN's performance as the number of ancillary qubits (i.e., circuit width) increases, though it still outperforms the scenario without auxiliary qubits. Additionally, we introduce the Unrolled QGAN model, which incorporates the Unrolled training technique to improve the performance of QGAN. Our experimental results demonstrate that this technique effectively enhances the performance of the QGAN.