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arXiv:2511.01028 · Journal of Physics A Mathematical and Theoretical

Pseudo quantum advantages in perceptron storage capacity

Fabio Benatti, Masoud Gharahi, Giovanni Gramegna, Stefano Mancini, Vincenzo Parisi

quant-phmath-phmath.STSubmitted Nov 2, 2025• Announced Nov 4, 2025• 0 citations• Revised Apr 8, 2026 (v2)
v1 · Nov 2, 2025v2 · Apr 8, 2026
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Abstract

We investigate a generalized quantum perceptron architecture characterized by an oscillating activation function with a tunable frequency ranging from zero to infinity. Employing analytical techniques from statistical mechanics, we derive the optimal storage capacity and demonstrate that the classical result is recovered in the limit of vanishing frequency. As the frequency increases, however, the architecture exhibits enhanced quantum storage capabilities. Notably, this improvement stems solely from the specific form of the activation function and, in principle, could be emulated within a classical framework. Accordingly, we refer to this enhancement as a pseudo quantum advantage.

Journal reference
J. Phys. A: Math. Theor. 59 145203 (2026)
DOI
10.1088/1751-8121/ae56a0
Comments
24 pages, 1 figure; minor changes, typos corrected

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Driven by growing computational power and algorithmic developments, machine learning methods have become valuable tools for analyzing vast amounts of data. Simultaneously, the fast technological progress of quantum information processing suggests employing quantum hardware for machine learning purposes. Recent works discuss different architectures of quantum perceptrons, but the abilities of such quantum devices remain debated. Here, we investigate the storage capacity of a particular quantum perceptron architecture by using statistical mechanics techniques and connect our analysis to the theory of classical spin glasses. Specifically, we focus on one concrete quantum perceptron model and explore its storage properties in the limit of a large number of inputs.
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This paper explores a novel approach to neural network activation functions by drawing inspiration from quantum mechanical principles. Traditional activation functions, such as sigmoid and ReLU, often exhibit limitations in terms of learning efficiency and representational capacity. We hypothesize that classical activation functions can be effectively approximated by functions derived from quantum mechanics, specifically utilizing concepts like the quantum harmonic oscillator. The proposed activation functions aim to leverage the unique properties of quantum systems—superposition, entanglement, and tunneling—to potentially enhance neural network performance. We introduce a set of new activation functions and provide a theoretical framework for their design. The core claim of this work is that classical activation functions can be accurately approximated by quantum mechanical functions. The primary mechanism involves translating quantum mechanical models into differentiable functions suitable for use within a neural network architecture. We discuss the mathematical foundations, potential benefits, and challenges associated with this approach, focusing on the approximation of the quantum harmonic oscillator and its application to neural network activation. The research contributes to a fundamental shift in activation function design, moving beyond purely classical models towards a quantum-inspired paradigm.