Thermodynamic Networks: Harnessing Non-Equilibrium Steady States for Computatio

Abstract:
I will talk about thermodynamic networks, which is a framework for autonomous physics-based computation using non-equilibrium steady states. These networks are modeled as a collection of finite-size reservoirs that exchange conserved quantities — such as electric charge or molecular number — while relaxing to a non-equilibrium steady state, which encodes the solution of a computational problem. We will identify Negative Differential Conductance (NDC) as the critical physical property governing the computational expressivity of the thermodynamic network. While networks lacking NDC are restricted to computing monotonic functions, the presence of NDC enables universal function approximation.

For the training of the network, I will mention two natural protocols that take advantage of the natural tendency of the system to equilibrate: equilibrium propagation and implicit differentiation. We will further apply this approach via two different platforms: quantum dot networks and enzymatic reaction networks. Both systems can be engineered to have NDC, enabling high performance in standard benchmarks, including sine function approximation and MNIST digit classification. The talk will be based on arXiv:2605.15985.

Remote guests are invited via Zoom:
Zoom link: https://us06web.zoom.us/j/84248911743?pwd=ZsiAOQbRgYCm5IFsArOnb18Fj5IsZh.1
Meeting ID: 842 4891 1743
Passcode: 394021