Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918325614804992 |
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| author | Brand, Dean Dibenedetto, Domenica Petruccione, Francesco |
| author_facet | Brand, Dean Dibenedetto, Domenica Petruccione, Francesco |
| contents | We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory effects and spiking behavior. Applications of this model to a sound localization benchmark show that it outperforms a phenomenological quantum LIF model as well as a classical LIF. This work establishes a foundational model for quantum neuromorphic computing, offering a pathway towards biologically inspired quantum spiking neural networks and new paradigms in quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit Brand, Dean Dibenedetto, Domenica Petruccione, Francesco Quantum Physics We present a theoretical framework for a quantized memristive Leaky Integrate-and-Fire (LIF) neuron, uniting principles from neuromorphic engineering and open quantum systems. Starting from a classical memristive LIF circuit, we apply canonical quantization techniques to derive a quantum model grounded in circuit quantum electrodynamics. Numerical simulations demonstrate key dynamical features of the quantized memristor and LIF neuron in the weak-coupling and adiabatic regime, including memory effects and spiking behavior. Applications of this model to a sound localization benchmark show that it outperforms a phenomenological quantum LIF model as well as a classical LIF. This work establishes a foundational model for quantum neuromorphic computing, offering a pathway towards biologically inspired quantum spiking neural networks and new paradigms in quantum machine learning. |
| title | Canonical Quantization of a Memristive Leaky Integrate-and-Fire Neuron Circuit |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2506.21363 |