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.
Abstract (translated)
这份报告介绍了一类新的推理架构，称为量子电路推理模型（Quantum Circuit Reasoning Models，简称 QCRM），这类模型将变分量子线路（Variational Quantum Circuits，VQC）的概念从能量最小化和分类任务扩展到了结构化的逻辑推理和推理领域。我们认为基本的量子力学操作——叠加、纠缠、干涉以及测量——自然地映射到诸如假设分支、约束传播、一致性强制执行和决策制定等重要的推理原语上。由此产生的框架结合了灵感来自量子计算的过程与可微分优化，使得推理能够作为振幅演化和由干涉驱动的自洽状态选择过程而出现。 我们建立了QCRM的数学基础，并定义了它的参数化电路架构，展示了如何将逻辑规则编码为命题-量子比特状态上的幺正变换。此外，我们还形式化了一个基于经典梯度下降法训练模型的目标函数，并讨论了在经典硬件上进行模拟实施的方法。最后，我们提出了量子推理层（Quantum Reasoning Layer，QRL），这是一种可微分的混合组件，适用于科学、生物医学和化学推断领域中的组合式推理模型。
URL
https://arxiv.org/abs/2512.07871
