arXiv:2607. 14293v1 Announce Type: cross Abstract: Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications.
arXiv:2607. 20871v1 Announce Type: cross Abstract: Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and remains largely manual.
arXiv:2607. 08928v1 Announce Type: cross Abstract: This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset.
arXiv:2607. 20302v1 Announce Type: new Abstract: Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data, potentially with fewer parameters and favorable scaling relative to classical models.
arXiv:2606. 01291v1 Announce Type: cross Abstract: Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector simulation memory scales exponentially ($\mathcal{O}(2^n)$), and global cost functions suffer from barren plateaus where gradient variance decays exponentially ($\mathcal{O}(1/2^n)$).
arXiv:2607. 14160v1 Announce Type: new Abstract: Wildfire detection from satellite imagery is a semantic image segmentation problem that has proven to be difficult due to challenges such as class imbalance, feature complexity, and atmospheric interference.
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
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:2607. 05724v1 Announce Type: new Abstract: Quantum convolutional neural networks (QCNNs) combine the power of quantum computing and classical CNN for computational speedup in classification tasks.
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:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.
arXiv:2607. 00961v1 Announce Type: cross Abstract: Realizing quantum neural networks (QNNs) in industry requires knowing which quantum computing paradigm suits which task.
arXiv:2606. 30688v1 Announce Type: cross Abstract: Symmetry provides a quantum neural network structure, but on its own it does not keep the network trainable once noise is present.