arXiv:2604. 15645v2 Announce Type: replace Abstract: We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow.
arXiv:2608. 00850v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a versatile approach for solving nonlinear partial differential equations (PDEs), yet achieving high accuracy efficiently using these techniques remains challenging for high-dimensional or multiscale systems.
The paper introduces a hybrid quantum–classical regression framework that uses a lightweight classical embedding as a learnable geometric preconditioner to improve the conditioning of a downstream variational quantum circuit. It further incorporates a curriculum optimization protocol that gradually increases circuit depth and switches from SPSA-based exploration to Adam-based fine‑tuning. Experiments on PDE‑informed and standard regression datasets show that this approach consistently outperforms pure QNN baselines, yielding more stable convergence and reduced structured errors, especially in data‑limited regimes.
arXiv:2606. 03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes.
arXiv:2607. 20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.
arXiv:2606. 01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
Cross-Backend QIEO is a runtime core for quantum-inspired evolutionary optimization that unifies execution across OpenMP5, CUDA, HIP, and multiple high-level languages. It compiles a single C++ implementation per hardware target and dispatches to CPU, multi-core, NVIDIA, or AMD backends at runtime, adapting kernels to each device’s memory hierarchy. The framework is validated with real-world bindings: a Python neural‑network hyperparameter optimizer achieving 88.60 % MNIST accuracy, a MATLAB wind‑farm layout optimizer matching particle swarm results and outperforming genetic algorithms, and a Julia package that reduces mean SSE by 2.1× in Lotka–Volterra parameter estimation.
arXiv:2608. 05819v1 Announce Type: new Abstract: Classical simulation remains essential for developing and validating quantum algorithms, but its cost grows rapidly with circuit size.
arXiv:2605. 06734v2 Announce Type: replace-cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states.
arXiv:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.
The paper introduces a reinforcement‑learning framework that automatically discovers compact parametrized quantum circuits for modeling power GaN HEMTs and logic nanowire FETs. Using a graph neural network policy trained with proximal policy optimization, the method optimizes circuit architectures based on leave‑one‑group‑out cross‑validation error, achieving the lowest mean absolute error across 11 targets compared to six classical baselines. The results show significant reductions in error and variability for key device metrics (Ioff, VTH, SS) on both HEMT and NWFET datasets, demonstrating the viability of RL‑selected quantum circuits as compact, physically consistent surrogates without explicit physical constraints.
arXiv:2607. 21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box.