arXiv:2606. 31334v1 Announce Type: new Abstract: Joint OFDM-RIS optimization for 6G is a mixed-integer nonlinear programming (MINLP) problem covering sum-rate maximization, energy efficiency, max-min fairness, and peak-to-average power ratio (PAPR)-constrained objectives.
arXiv:2509.00341v3 Announce Type: replace-cross Abstract: Conic programs arising in physics, quantum information, machine learning, and engineering are often defined over sparse graphs. Although such...
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:2607. 13160v1 Announce Type: cross Abstract: Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile edge computing (MEC) systems.
arXiv:2605. 13268v2 Announce Type: replace-cross Abstract: Trotter Suzuki product formulas are the standard route to Hamiltonian evolution on noisy intermediate-scale quantum (\NISQ{}) hardware, but their accuracy depends on three coupled choices: term grouping, product-formula order, and time-step allocation.
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.
The paper introduces GQ-FSL, a green quantized federated split learning framework designed for wireless edge networks. It uses stochastic quantization for both local training and wireless transmissions, allowing asymmetric precision between client and server submodels to balance device energy limits with global convergence. The authors develop energy models and a convergence bound for heterogeneous data, then formulate an optimization problem to set the DNN split point and precision levels, achieving lower energy consumption while meeting latency and accuracy targets.
arXiv:2511. 13592v2 Announce Type: replace-cross Abstract: The existing method of GS-PowerOpt solves the non-convex optimization problem of the form $\max_{\boldsymbol{x} \in \mathbb{R}^d} f(\boldsymbol{x})$ through maximizing a Gaussian-smoothed surrogate $F_{N,\sigma}(\boldsymbol{\mu}) = \mathbb{E}_{\boldsymbol{x}\sim\mathcal{N}(\boldsymbol{\mu},\sigma^2 I_d)}[e^{N f(\boldsymbol{x})}]$.
The paper presents a large‑scale, 3GPP TR 38.901‑compliant dataset for RIS‑aided millimeter‑wave B5G networks, covering 20 deployment variants with diverse user densities, fading, and blockage conditions. Each sample includes oracle RIS phase configurations from a brute‑force search, along with full CSI, per‑link channel decomposition, optimal phase matrices, and CQI labels, enabling a wide range of machine‑learning tasks. The authors also introduce a novel CSI‑to‑CQI mapping as a benchmark for scalable link‑quality prediction and evaluate it against state‑of‑the‑art models under various conditions.
arXiv:2609. 09295v1 Announce Type: cross Abstract: The fixed-charge network flow problem (FCNFP) couples continuous flow allocation with discrete arc-activation decisions, making it a canonical but computationally challenging model for a variety of network design and resource allocation problems.
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:2512.04705v3 Announce Type: replace-cross Abstract: The deployment of Early-Exiting Neural Networks (EENNs) on edge accelerators requires optimizing not only the network architecture but also i...
The paper presents a Quadratic Constrained Binary Optimization (QCBO) framework that provides provable guarantees for training quantized neural networks. It characterizes the topology of zero‑loss level sets, compiles finite‑depth architectures into bounded QCBOs, and introduces a sample‑wise Decomposed Lower‑Bound Optimization (DLBO) to scale Ising‑based optimization. Experiments on a coherent Ising machine show high accuracy on binary Fashion‑MNIST at 1.1‑bit precision and validate the approach on multi‑class datasets.