Generative Flow Networks with Parameterized Quantum Circuits

The convergence of quantum and classical computing, particularly in the realm of quantum machine learning, has gained substantial attention. Hybrid quantum algorithms, merging the strengths of both computing paradigms, offer a promising avenue, especially in the era of NISQ (noisy intermediate-scale quantum) computing. Leveraging these hybrid approaches allows researchers to employ quantum algorithms on existing quantum processors while harnessing classical methods to handle complex computational challenges. A core component of these hybrid systems is the Parameterized Quantum Circuit Element (PQCE). These circuits, defined by parameterized unitary operations, possess exceptional expressiveness, effectively modeling intricate distributions. This study introduces a novel application of Parameterized Quantum Circuits in Generative Flow Networks for object generation tasks. This model demonstrates efficiency and quality on par with classical deep neural network-based approaches. Despite longer training times, the quantum model achieves comparable results with significantly fewer parameters (151), emphasizing its potential in scenarios prioritizing parameter efficiency.