Quantum Circuit Born Machine

Quantum Circuit Born Machines (QCBMs) are quantum-inspired generative models aiming to leverage quantum computation for superior performance in generating data, particularly in data-scarce regimes. Current research focuses on improving QCBM training efficiency, addressing issues like mode collapse through techniques such as maximal coding rate reduction and incorporating non-linear activations for enhanced performance, often comparing them to classical generative models like GANs. These efforts are driven by the need to establish practical quantum advantage in generative modeling, with a strong emphasis on rigorous evaluation of generalization capabilities using novel metrics. The ultimate goal is to demonstrate the potential of QCBMs for real-world applications where classical methods struggle.