Chaotic quantum system learns data with simple controls

Fujitsu researchers have demonstrated a quantum data learning method requiring only global, time-independent control, a significant reduction in the complex control typically needed for quantum systems. The team’s chaotic quantum diffusion model generates data using chaotic Hamiltonian time evolution, offering a potentially more robust alternative to existing methods reliant on circuit-based random unitary dynamics. This approach substantially reduces implementation overhead for diverse analog quantum platforms while maintaining accuracy comparable to current quantum denoising diffusion probabilistic models, with potential applications in fields like chemoinformatics and quantum physics. The work improves trainability and robustness, broadening the applicability of quantum generative modeling. This simplification stems from the implementation of chaotic Hamiltonian time evolution within a novel “chaotic quantum diffusion model,” a framework designed to generate projected ensembles of quantum data. These circuit-based methods are often costly to implement and susceptible to errors arising from imperfect control, particularly on analog quantum hardware.…