Thermodynamic Computing via Autonomous Quantum Thermal Machines
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866910858756489216 |
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| author | Lipka-Bartosik, Patryk Perarnau-Llobet, Martí Brunner, Nicolas |
| author_facet | Lipka-Bartosik, Patryk Perarnau-Llobet, Martí Brunner, Nicolas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_15905 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Thermodynamic Computing via Autonomous Quantum Thermal Machines Lipka-Bartosik, Patryk Perarnau-Llobet, Martí Brunner, Nicolas Quantum Physics Mesoscale and Nanoscale Physics Statistical Mechanics Artificial Intelligence Machine Learning 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. |
| title | Thermodynamic Computing via Autonomous Quantum Thermal Machines |
| topic | Quantum Physics Mesoscale and Nanoscale Physics Statistical Mechanics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2308.15905 |