arXiv subjects

Quoc Chuong Nguyen

Publications and source records attributed to Quoc Chuong Nguyen.

8 recordsLinked to original sources

arXiv2026

Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods

Selecting an effective encoding quantum circuit is a key challenge in quantum kernel methods because different feature maps can lead to different performance. Conventional methods require constructing and evaluating every circuit for each new dataset, making it computationally expensive. We present Qmes, an open-source Python package that automatically recommends circuits through meta-learning. Qmes characterizes a dataset using classical complexity measures and queries a pre-trained model to recommend circuits without quantum evaluation at inference time. The package provides modular components for meta-feature extraction, quantum-kernel evaluation, recommender training, model selection, and user-defined circuit extension. We validate Qmes on 105 classification and 86 regression benchmark datasets. Qmes reduces the mean recommendation regret by 2.2x and 4.2x for classification and regression, respectively, compared to a non-adaptive baseline, with statistical significance confirmed via a paired Wilcoxon signed-rank test ($p < 10^{-4}$). Qmes thus enables efficient and practical encoding-circuit selection for quantum kernel methods.

quant-ph↗

arXiv2026

Benchmarking loss functions for trainable quantum feature maps

Many quantum machine learning models employ quantum feature maps to encode classical data into quantum states. While fixed feature maps often lack sufficient expressivity for complex nonlinear classification tasks, trainable quantum feature maps (TQFMs) enable adaptive quantum kernels with enhanced learning capability. Different loss functions can induce distinct optimization dynamics, yet their effects remain poorly understood. In this work, we apply the Log-Likelihood Loss function for TQFMs and provide a systematic comparison with Distance Loss and Measurement Loss. Through extensive numerical experiments, we compare their optimization dynamics, computational costs, and classification performance. Our results show that Log-Likelihood Loss consistently achieves more stable optimization than Measurement Loss while retaining linear computational complexity. The resulting benchmark offers practical guidance for balancing trainability, computational efficiency, and predictive performance in quantum kernel optimization.

quant-ph↗

arXiv2026

Discovery of connectivity-trainability trade-off of IQP Circuits for Hamiltonian Optimization

Instantaneous Quantum Polynomial-time (IQP) circuits are promising candidates for near-term quantum advantage due to the conjectured classical hardness of their sampling task. However, their capabilities for optimization remain largely unexplored. We present a systematic investigation of the performance and trainability of IQP circuits for Hamiltonian optimization. Our results reveal a trade-off between optimization performance and circuit connectivity, demonstrating that the circuit structure plays a key role in determining the ability of IQP circuits to reach low-energy states.

cs.SI↗

arXiv2025

Network Sampling: An Overview and Comparative Analysis

Network sampling is a crucial technique for analyzing large or partially observable networks. However, the effectiveness of different sampling methods can vary significantly depending on the context. In this study, we empirically compare representative methods from three main categories: node-based, edge-based, and exploration-based sampling. We used two real-world datasets for our analysis: a scientific collaboration network and a temporal message-sending network. Our results indicate that no single sampling method consistently outperforms the others in both datasets. Although advanced methods tend to provide better accuracy on static networks, they often perform poorly on temporal networks, where simpler techniques can be more effective. These findings suggest that the best sampling strategy depends not only on the structural characteristics of the network but also on the specific metrics that need to be preserved or analyzed. Our work offers practical insights for researchers in choosing sampling approaches that are tailored to different types of networks and analytical objectives.

cs.SI↗

arXiv2025

FQsun: A Configurable Wave Function-Based Quantum Emulator for Power-Efficient Quantum Simulations

Quantum computers are promising powerful computers for solving complex problems, but access to real quantum hardware remains limited due to high costs. Although the software simulators on CPUs/GPUs such as Qiskit, ProjectQ, and Qsun offer flexibility and support for many qubits, they struggle with high power consumption and limited processing speed, especially as qubit counts scale. Accordingly, quantum emulators implemented on dedicated hardware, such as FPGAs and analog circuits, offer a promising path for addressing energy efficiency concerns. However, existing studies on hardware-based emulators still face challenges in terms of limited flexibility and lack of fidelity evaluation. To overcome these gaps, we propose FQsun, a quantum emulator that enhances performance by integrating four key innovations: efficient memory organization, a configurable Quantum Gate Unit (QGU), optimized scheduling, and multiple number precisions. Five FQsun versions with different number precisions are implemented on the Xilinx ZCU102, consuming a maximum power of 2.41W. Experimental results demonstrate high fidelity, low mean square error, and high normalized gate speed, particularly with 32-bit versions, establishing FQsun's capability as a precise quantum emulator. Benchmarking on famous quantum algorithms reveals that FQsun achieves a superior power-delay product, outperforming software simulators on CPUs in the processing speed range.

quant-ph↗

arXiv2025

Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing Problems

The Hierarchical Directed Capacitated Arc Routing Problem (HDCARP) is an extension of the Capacitated Arc Routing Problem (CARP), where the arcs of a graph are divided into classes based on their priority. The traversal of these classes is determined by either precedence constraints or a hierarchical objective, resulting in two distinct HDCARP variants. To the best of our knowledge, only one matheuristic has been proposed for these variants, but it performs relatively slowly, particularly for large-scale instances (Ha et al., 2024). In this paper, we propose a fast heuristic to efficiently address the computational challenges of HDCARP. Furthermore, we incorporate Reinforcement Learning (RL) into our heuristic to effectively guide the selection of local search operators, resulting in a hybrid algorithm. We name this hybrid algorithm as the Hybrid Reinforcement Learning and Heuristic Algorithm for Directed Arc Routing (HRDA). The hybrid algorithm adapts to changes in the problem dynamically, using real-time feedback to improve routing strategies and solution's quality by integrating heuristic methods. Extensive computational experiments on artificial instances demonstrate that this hybrid approach significantly improves the speed of the heuristic without deteriorating the solution quality. Our source code is publicly available at: https://github.com/HySonLab/ArcRoute

cs.LG↗

arXiv2024

Monte Carlo Analysis of Boid Simulations with Obstacles: A Physics-Based Perspective

Boids, developed by Craig W. Reynolds in 1986, is one of the earliest emergent models where the global pattern emerges from the interaction between many individuals within the local scale. In the original model, Boids follow three rules: separation, alignment, and cohesion; which allow them to move around and create a flock without intention in the empty environment. In the real world, however, the Boids' movement also faces obstacles preventing the flock's direction. In this project, I propose two new simple rules of the Boids model to represent the more realistic movement in nature and analyze the model from the physics perspective using the Monte Carlo method. From those results, the physics metrics related to the forming of the flocking phenomenon show that it is reasonable to explain why birds or fishes prefer to move in a flock, rather than sole movement.

cond-mat.stat-mech↗

arXiv2024

Toward a comprehensive simulation framework for hypergraphs: a Python-base approach

Hypergraphs, or generalization of graphs such that edges can contain more than two nodes, have become increasingly prominent in understanding complex network analysis. Unlike graphs, hypergraphs have relatively few supporting platforms, and such dearth presents a barrier to more widespread adaptation of hypergraph computational toolboxes that could enable further research in several areas. Here, we introduce HyperRD, a Python package for hypergraph computation, simulation, and interoperability with other powerful Python packages in graph and hypergraph research. Then, we will introduce two models on hypergraph, the general Schelling's model and the SIR model, and simulate them with HyperRD.

cs.MS↗