A Novel Hierarchy of Quantum Kernel Networks on Smoothed Particle Hydrodynamics

• URL: http://arxiv.org/abs/2604.24159v1
• Date: Mon, 27 Apr 2026 08:15:09 GMT
• Title: A Novel Hierarchy of Quantum Kernel Networks on Smoothed Particle Hydrodynamics
• Abstract summary: This study pioneers the integration of quantum kernel networks on particle hydrodynamics.<n>We developed versatile Lagrangian quantum network models built upon an improved quantum perceptron (QMLP)<n>The framework is validated through some continuous benchmarks on eurypalynous quantum neural networks.
• Score: 3.934403110868379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, quantum computing and artificial intelligence are driving revolutionary advancements in computational science. This study pioneers the integration of quantum kernel networks on smoothed particle hydrodynamics (SPH). SPH has matured into a highly versatile meshfree/particle method, exceptionally suited for tracking spatiotemporal trajectories and dynamic modeling phenomena. We developed a hierarchy of Lagrangian quantum network models built upon an improved quantum multilayer perceptron (QMLP). Specifically, a sequential hybrid quantum-classical framework is constructed, utilizing Pauli-Z expectation values over traditional probability outputs to ensure robust gradient-based optimization and mitigate barren plateaus. It combines smoothing kernels with quantum learning, establishing a novel quantum intelligent SPH paradigm. The framework is validated through some continuous benchmarks on eurypalynous quantum neural networks, static multi-level nebula vortex interference reconstructions and transient scalar field advectional tests. Numerical results demonstrate that while pure elementary quantum circuits struggle with parameter-specific generalization in unstructured domains, the proposed hybrid crossed-QMLP seamlessly matches the fitting accuracy of classical SPH in quantum optimized space. Although this approach currently faces limitations in computational efficiency and hardware implementation, it nonetheless paves the way for a novel investigation into quantum SPH, by mapping unstructured Lagrangian particle topologies into integrated quantum circuits.

Related papers

• Simulation of Lindbladian dynamics via adaptive variational quantum trajectory compression [6.296586170753371]
  We propose a resource-efficient algorithm for simulating Lindbladian dynamics on NISQ devices.<n>For open quantum systems with Pauli dissipations, we first derive a compact and stable mixed-unitary adjoint channel.<n>To further reduce the circuit depth required for implementing the sampled trajectories, we introduce an adaptive variational quantum trajectory compression framework.
  arXiv  Detail & Related papers  (2026-07-10T02:48:39Z)
• Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization [50.880384999888044]
  We develop misspecified GP bandit algorithms and derive regret bounds that characterize the trade-off between approximation error and information gain.<n>Our methods outperform full quantum kernels in sample efficiency, while substantially reducing computational overhead.
  arXiv  Detail & Related papers  (2026-07-01T15:38:00Z)
• Physics-Informed Hybrid Quantum-Classical Dispatching for Large-Scale Renewable Power Systems:A Noise-Resilient Framework [9.378801906395179]
  High-penetration energy introduces significantity and non-Classicality into power system dispatching optimization.<n>Existing approaches typically treat the power grid as a "black box"<n>This paper proposes a Hybrid Quantum-Bridging Dispatching (PIHQ-CD) framework.
  arXiv  Detail & Related papers  (2026-01-26T13:35:54Z)
• Multi-Partitioned Meshfree Quantum Finite Particle Method: A Hybrid Quantum Framework for Fluid Flow [3.857420815948075]
  This study established a quantum-classical hybrid framework that integrates quantum computing paradigm with meshfree finite particle method.<n>By harnessing quantum superposition and entanglement, it hybridized the critical computational kernels.<n>Results demonstrate that integrating quantum computing to hybridize conventional linear combinations of particle dynamics serves as an effective high performance computing paradigm.
  arXiv  Detail & Related papers  (2025-09-14T13:59:42Z)
• Iterative Quantum Feature Maps [0.0]
  Quantum machine learning models that leverage quantum circuits as quantum feature maps (QFMs) are recognized for their enhanced expressive power in learning tasks.<n> deploying deep QFMs on real quantum hardware remains challenging due to circuit noise and hardware constraints.<n>We propose Iterative Quantum Feature Maps (IQFMs), a hybrid quantum-classical framework that constructs a deep architecture by iteratively connecting shallow QFMs with classically computed augmentation weights.
  arXiv  Detail & Related papers  (2025-06-24T09:40:10Z)
• A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
  Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Flowing Through Hilbert Space: Quantum-Enhanced Generative Models for Lattice Field Theory [0.9208007322096533]
  We develop a hybrid quantum-classical normalizing flow model to explore quantum-enhanced sampling in such regimes.<n>Our approach embeds parameterized quantum circuits within a classical normalizing flow architecture, leveraging amplitude encoding and quantum entanglement to enhance expressivity in the generative process.
  arXiv  Detail & Related papers  (2025-05-15T17:58:16Z)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Preparing random states and benchmarking with many-body quantum chaos [48.044162981804526]
  We show how to predict and experimentally observe the emergence of random state ensembles naturally under time-independent Hamiltonian dynamics. The observed random ensembles emerge from projective measurements and are intimately linked to universal correlations built up between subsystems of a larger quantum system. Our work has implications for understanding randomness in quantum dynamics, and enables applications of this concept in a wider context.
  arXiv  Detail & Related papers  (2021-03-05T08:32:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.