arXiv subjects

Arthur M. Faria

Publications and source records attributed to Arthur M. Faria.

5 recordsLinked to original sources

arXiv2025

Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning

We introduce Quantum Graph Attention Networks (QGATs) as trainable quantum encoders for inductive learning on graphs, extending the Quantum Graph Neural Networks (QGNN) framework. QGATs leverage parameterized quantum circuits to encode node features and neighborhood structures, with quantum attention mechanisms modulating the contribution of each neighbor via dynamically learned unitaries. This allows for expressive, locality-aware quantum representations that can generalize across unseen graph instances. We evaluate our approach on the QM9 dataset, targeting the prediction of various chemical properties. Our experiments compare classical and quantum graph neural networks-with and without attention layers-demonstrating that attention consistently improves performance in both paradigms. Notably, we observe that quantum attention yields increasing benefits as graph size grows, with QGATs significantly outperforming their non-attentive quantum counterparts on larger molecular graphs. Furthermore, for smaller graphs, QGATs achieve predictive accuracy comparable to classical GAT models, highlighting their viability as expressive quantum encoders. These results show the potential of quantum attention mechanisms to enhance the inductive capacity of QGNN in chemistry and beyond.

quant-ph↗

arXiv2025

Inductive Graph Representation Learning with Quantum Graph Neural Networks

Quantum Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are scalable and robust, existing QGNNs often lack flexibility due to graph-specific quantum circuit designs, limiting their applicability to diverse real-world problems. To address this, we propose a versatile QGNN framework inspired by GraphSAGE, using quantum models as aggregators. We integrate inductive representation learning techniques with parameterized quantum convolutional and pooling layers, bridging classical and quantum paradigms. The convolutional layer is flexible, allowing tailored designs for specific tasks. Benchmarked on a node regression task with the QM9 dataset, our framework, using a single minimal circuit for all aggregation steps, handles molecules with varying numbers of atoms without changing qubits or circuit architecture. While classical GNNs achieve higher training performance, our quantum approach remains competitive and often shows stronger generalization as molecular complexity increases. We also observe faster learning in early training epochs. To mitigate trainability limitations of a single-circuit setup, we extend the framework with multiple quantum aggregators on QM9. Assigning distinct circuits to each hop substantially improves training performance across all cases. Additionally, we numerically demonstrate the absence of barren plateaus as qubit numbers increase, suggesting that the proposed model can scale to larger, more complex graph-based problems.

quant-ph↗

arXiv2025

Strong-damping limit of quantum Brownian motion in a disordered environment

We consider a microscopic model of an inhomogeneous environment where an arbitrary quantum system is locally coupled to a harmonic bath via a finite-range interaction. We show that in the overdamped regime the position distribution obeys a classical Kramers-Moyal equation that involves an infinite number of higher derivatives, implying that the finite bath correlation length leads to non-Gaussian Markovian noise. We analytically solve the equation for a harmonically bound particle and analyze its non-Gaussian diffusion as well as its steady-state properties.

cond-mat.stat-mech↗

arXiv2025

Nonequilibrium fluctuation relations for non-Gaussian processes

Non-Gaussian noise is omnipresent in systems where the central-limit theorem is inapplicable. We here investigate the stochastic thermodynamics of small systems that are described by a general Kramers-Moyal equation that includes both Gaussian and non-Gaussian white noise contributions. We obtain detailed and integral fluctuation relations for the nonequilibrium entropy production of these Markov processes in the regime of weak noise. As an application, we analyze the properties of driven objects that are locally coupled to a heat bath via a finite-range interaction, by considering an overdamped particle that is pulled by a moving harmonic potential. We find that reducing the bath interaction range increases non-Gaussian features, and strongly suppresses the average nonequilibrium entropy production. We further discuss a generalized detailed-balance condition.

cond-mat.stat-mech↗

arXiv2020

Verification of finite bath fluctuation theorem for a non-ergodic system

The analysis of fluctuations generated by a thermal reservoir has produced many results throughout the history of science, ranging from the verification of the atomic hypothesis, running through critical phenomena to the most recent advances in the description of non-equilibrium thermodynamic processes. Motivated by recent theoretical and experimental works, we analyze the non-equilibrium and equilibrium fluctuations caused by a finite and chaotic heat bath in a simple system of interest. Finite bath and system of interest give rise to a non-ergodic composite system when interacting with each other. We have characterized the equilibrium distribution induced by the finite bath and numerically verified the finite-bath fluctuation theorem. We have also verified the convergence of our results to Crooks' fluctuation theorem as the number of degrees of freedom of the finite bath increases while the non-ergodic character remains.

cond-mat.stat-mech↗