Quantum Circuit Reasoning Models: A Variational Framework for Differentiable Logical Inference

• URL: http://arxiv.org/abs/2512.07871v1
• Date: Wed, 26 Nov 2025 23:15:14 GMT
• Title: Quantum Circuit Reasoning Models: A Variational Framework for Differentiable Logical Inference
• Authors: Andrew Kiruluta,
• Abstract summary: This report introduces a novel class of reasoning architectures, termed Quantum Circuit Reasoning Models (QCRM)<n>We show how logical rules can be encoded as unitary transformations over proposition-qubit states.<n>We propose the Quantum Reasoning Layer (QRL) as a differentiable hybrid component for composable reasoning models applicable to scientific, biomedical, and chemical inference domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This report introduces a novel class of reasoning architectures, termed Quantum Circuit Reasoning Models (QCRM), which extend the concept of Variational Quantum Circuits (VQC) from energy minimization and classification tasks to structured logical inference and reasoning. We posit that fundamental quantum mechanical operations, superposition, entanglement, interference, and measurement, naturally map to essential reasoning primitives such as hypothesis branching, constraint propagation, consistency enforcement, and decision making. The resulting framework combines quantum-inspired computation with differentiable optimization, enabling reasoning to emerge as a process of amplitude evolution and interference-driven selection of self-consistent states. We develop the mathematical foundation of QCRM, define its parameterized circuit architecture, and show how logical rules can be encoded as unitary transformations over proposition-qubit states. We further formalize a training objective grounded in classical gradient descent over circuit parameters and discuss simulation-based implementations on classical hardware. Finally, we propose the Quantum Reasoning Layer (QRL) as a differentiable hybrid component for composable reasoning models applicable to scientific, biomedical, and chemical inference domains.

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