a-blueprint-for-equilibrium-based-differentiable-continuous-variable-thermodynamic-computing-31b9a836·1 events·first seen Aliases: A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
Researchers propose a blueprint for a thermodynamic computing stack that uses Langevin dynamics with tunable energy potentials implemented in stochastic analog hardware to run probabilistic machine learning workloads. The framework maps popular ML model classes onto hardware-native energy-based models via probabilistic graphical models, with runtime and energy analysis via theory and simulation. A preliminary experimental realization using superconducting circuits driven by thermal noise is presented. The work targets the energy and latency bottlenecks of conventional ML hardware.