Quantum Boltzmann Machine

Quantum Boltzmann Machines (QBMs) are hybrid quantum-classical models aiming to leverage quantum computation for enhanced performance in machine learning tasks, particularly those involving large datasets or complex optimization problems. Current research focuses on improving QBM training efficiency through techniques like coresets and adversarial autoencoders, as well as exploring their application in diverse areas such as reinforcement learning, image classification, and genomics. This research is significant because QBMs offer the potential for improved data efficiency and faster training compared to classical Boltzmann Machines, impacting fields requiring efficient data processing and optimization.