Thermodynamic Computing via Autonomous Quantum Thermal Machines
This provides an alternative physics-based analogue implementation of neural networks and a platform for thermodynamic computing, potentially impacting hardware design for AI.
The authors developed a physics-based model for classical computation using autonomous quantum thermal machines, which can implement any linearly-separable function and be networked to perform any desired function, as demonstrated with gates like NOT, 3-MAJORITY, and NOR.
We develop a physics-based model for classical computation based on autonomous quantum thermal machines. These machines consist of few interacting quantum bits (qubits) connected to several environments at different temperatures. Heat flows through the machine are here exploited for computing. The process starts by setting the temperatures of the environments according to the logical input. The machine evolves, eventually reaching a non-equilibrium steady state, from which the output of the computation can be determined via the temperature of an auxilliary finite-size reservoir. Such a machine, which we term a ``thermodynamic neuron'', can implement any linearly-separable function, and we discuss explicitly the cases of NOT, 3-MAJORITY and NOR gates. In turn, we show that a network of thermodynamic neurons can perform any desired function. We discuss the close connection between our model and artificial neurons (perceptrons), and argue that our model provides an alternative physics-based analogue implementation of neural networks, and more generally a platform for thermodynamic computing.