Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Soft-Logic Circuits: A Differentiable Framework for Learned Reasoning on Hybrid Quantum-Classical Systems

Andrew Kiruluta

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.13140/rg.2.2.27258.35527

Related papers

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

Kiruluta, Andrew

•Jan 1, 2025•0 citations

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

Andrew Kiruluta

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.

quant-pharXiv:2512.07871•Nov 28, 2025•0 citations

A Sample-Efficient Quantum-Classical Reinforcement Learning Framework for Transmission Switching

Lin Ding, Jianhui Wang, Huan-Hsin Tseng +3 more

This paper presents a sample-efficient hybrid quantum-classical reinforcement learning (QCRL) framework for multi-objective transmission switching (MO-TS) in power systems. We introduce a Chunk-Based Data Reuploading Variational Quantum Circuit (CBDR-VQC) architecture, which is designed to enhance policy expressivity without increasing circuit depth or width. Unlike conventional VQC approaches that scale circuit size to improve learning capacity, often at the cost of hardware feasibility and trainability, our method encodes high-dimensional input features sequentially into a fixed-width, shallow quantum circuit with a learnable measurement scheme. This design maintains compatibility with near-term quantum devices while avoiding issues such as noise accumulation and barren plateaus. Embedded within a quantum-classical Soft Actor-Critic (QCSAC) framework, the proposed model achieves competitive performance for MO-TS on the IEEE 118-bus system. It significantly reduces the numbers of both required qubits and trainable parameters compared to both classical and wider VQC-based baselines, demonstrating its promise for resourceconstrained QCRL in real-world power system optimization.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Aug 30, 2025•1 citation

Extending Classical Logic for Reasoning About Quantum Systems

Rohit Chadha, Paulo Mateus, Amı́lcar Sernadas +1 more

Elsevier eBooks•Jan 1, 2009•14 citations

Differentiable Quantum Circuits and Generative Modeling

Jin-Guo Liu, Lei Wang

APS•Jan 1, 2019•0 citations

Structured Supervision from Quantum Models: Distilling Robustness into Classical Networks

Syed Rameez Naqvi, Zhengming Ding, Fang Qi +2 more

Quantum machine learning offers a practical opportunity to leverage near-term quantum devices without requiring quantum inference at deployment. We propose a quantum-classical hybrid knowledge distillation framework in which a variational quantum circuit, equipped with a Quantum Fourier Transform-inspired positional encoding, acts as a soft-label teacher for a compact classical student. Rather than serving as a standalone classifier, the quantum model is used offline to generate structured probability distributions that encode global spectral information and uncertainty. These quantum-derived soft labels are distilled into a lightweight classical network using a hybrid loss that combines hard and soft supervision. Experiments on MNIST demonstrate that students trained with quantum soft labels exhibit consistent and statistically meaningful robustness improvements to Gaussian noise and in-plane rotations compared to classical distillation baselines, while maintaining strong clean accuracy and calibration. These results highlight a distinct role for NISQ-era quantum models as supervisory signal generators, enabling quantum-informed learning within fully classical deployment pipelines.

Proceedings of the AAAI Symposium Series•May 18, 2026•0 citations
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.
This paper presents a sample-efficient hybrid quantum-classical reinforcement learning (QCRL) framework for multi-objective transmission switching (MO-TS) in power systems. We introduce a Chunk-Based Data Reuploading Variational Quantum Circuit (CBDR-VQC) architecture, which is designed to enhance policy expressivity without increasing circuit depth or width. Unlike conventional VQC approaches that scale circuit size to improve learning capacity, often at the cost of hardware feasibility and trainability, our method encodes high-dimensional input features sequentially into a fixed-width, shallow quantum circuit with a learnable measurement scheme. This design maintains compatibility with near-term quantum devices while avoiding issues such as noise accumulation and barren plateaus. Embedded within a quantum-classical Soft Actor-Critic (QCSAC) framework, the proposed model achieves competitive performance for MO-TS on the IEEE 118-bus system. It significantly reduces the numbers of both required qubits and trainable parameters compared to both classical and wider VQC-based baselines, demonstrating its promise for resourceconstrained QCRL in real-world power system optimization.
Quantum machine learning offers a practical opportunity to leverage near-term quantum devices without requiring quantum inference at deployment. We propose a quantum-classical hybrid knowledge distillation framework in which a variational quantum circuit, equipped with a Quantum Fourier Transform-inspired positional encoding, acts as a soft-label teacher for a compact classical student. Rather than serving as a standalone classifier, the quantum model is used offline to generate structured probability distributions that encode global spectral information and uncertainty. These quantum-derived soft labels are distilled into a lightweight classical network using a hybrid loss that combines hard and soft supervision. Experiments on MNIST demonstrate that students trained with quantum soft labels exhibit consistent and statistically meaningful robustness improvements to Gaussian noise and in-plane rotations compared to classical distillation baselines, while maintaining strong clean accuracy and calibration. These results highlight a distinct role for NISQ-era quantum models as supervisory signal generators, enabling quantum-informed learning within fully classical deployment pipelines.