Supersymmetric Quantum Neural Networks: Bridging Superalgebras and AI Architectures

Abstract: This paper proposes a novel paradigm for quantum artificial intelligence: the design and implementation of Supersymmetric Quantum Neural Networks (S-QNNs) that explicitly integrate superalgebraic structures into quantum circuit-based AI architectures. By embedding the symmetry principles of supersymmetry—captured by quantum superalgebras such as osp(1∣2) and sl(1∣1)—into the computational fabric of quantum neural networks, we aim to create models that exhibit both structural elegance and computational efficiency. The resulting framework allows for interpretable, energy-efficient, and mathematically grounded learning systems that leverage both fermionic (discrete) and bosonic (continuous) quantum states to encode and process information. This interdisciplinary approach opens new directions for building explainable and scalable quantum AI systems, particularly in the era of Noisy Intermediate-Scale Quantum (NISQ) devices. Keywords: Supersymmetric Neural Networks, Quantum Superalgebras, Quantum AI, Hybrid Quantum-Classical Learning, osp(1|2), sl(1|1), Interpretable AI, Quantum Symmetry, NISQ