Thermodynamic computing via autonomous quantum thermal machines

Area of Science:

• Quantum physics
• Thermodynamics
• Computational science

Background:

• Classical computation faces limitations in energy efficiency and scalability.
• Quantum thermal machines offer novel approaches to information processing.

Purpose of the Study:

• To develop a physics-based model for classical computation using quantum thermal machines.
• To demonstrate the potential of thermodynamic computing for implementing neural networks.

Main Methods:

• Modeling autonomous quantum thermal machines with interacting qubits and multiple thermal environments.
• Exploiting heat flow and nonequilibrium steady states for computation.
• Defining a
• thermodynamic neuron
• capable of implementing linearly separable functions.

Main Results:

• A single thermodynamic neuron can perform logical operations like NOT, 3-MAJORITY, and NOR gates.
• Networks of thermodynamic neurons can implement any arbitrary function.
• The model establishes a direct link between quantum thermal machines and artificial neural networks.

Conclusions:

• This work presents a novel platform for thermodynamic computing.
• The proposed model offers a physics-based analog implementation of neural networks.
• Quantum thermal machines provide a promising avenue for future computational paradigms.