Fermi-Dirac machines as quantizations of neurons
Fermi-Dirac machines were proposed recently as an approach to solving semidefinite optimization problems on quantum computers. Here, we reinterpret them as canonical quantizations of classical neurons. By viewing a classical neuron as an activation function applied to a parameterized classical Hamiltonian, we quantize this model by replacing classical variables with operators whose eigenvalues encode their possible values. This follows the standard approach to canonical quantization in quantum mechanics. Crucially, when the Hamiltonian consists of commuting operators, our construction reduces exactly to a classical neuron. More generally, our approach yields an activation observable, defi...
| Authors | Alexander He, Nana Liu, Mark M. Wilde |
|---|---|
| Primary category | quant-ph |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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