31 curated articles
NVIDIA’s Quantum Computing Division has introduced Ising, an open-source model family designed to deploy neural-network-driven control layers for fault-tolerant quantum error correction (QEC). Detailed in a technical disclosure ("NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300X...
Reliable qubit readout is a critical bottleneck toward fault-tolerant quantum computing (FTQC). In superconducting quantum processors, readout operati...
Grokking, the delayed transition from memorization to generalization, is a fundamental phenomenon in gradient-based learning, yet its dynamics within ...
Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formula...
Differential privacy provides a rigorous framework for quantifying privacy leakage in data analysis, while its quantum extensions have become increasi...
Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-toler...
Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of ...
Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear...
Quantum convolutional neural networks (QCNNs) have become increasingly popular in quantum machine learning (QML) due to their efficient parameterizati...
In the NISQ era, there is a need for resource-efficient proof-of-principle experiments that can be built up to genuine utility. Single-qubit classifie...
WiMi Hologram Cloud Inc. (NASDAQ: WiMi) has announced ongoing research into the utilization of machine learning models to optimize operational paramet...
Realizing quantum neural networks (QNNs) in industry requires knowing which quantum computing paradigm suits which task. Motivated by AI accelerators ...
Geometric Machine Learning (GML) successes have been achieved through the thorough study and design of new equivariant neural networks. In comparison,...
As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the...
Practical hardware limitations, including finite transmitter and receiver filter lengths as well as the finite resolution of digital-to-analog and ana...
We introduce a hybrid approach utilising a quantum machine learning surrogate model to approximate the non-linear collision dynamics of the LBM. It ef...
Measurement-based quantum computing is a universal model of quantum computation in which successive product measurements of an entangled resource stat...
Photonic quantum processing unit for quantum and classical machine learning tasks. A collaborative research group consisting of quantum information sc...
Bosonic hardware developer Alice & Bob has published a computer architecture blueprint authored by senior architect Kevin D. Kissell detailing a new "...
Early detection of oral cancer markedly improves clinical outcomes, yet specialized diagnostic tools remain scarce in low-resource settings. Smartphon...
Parameterized Instantaneous Quantum Polynomial (IQP) circuits have proven useful in quantum generative learning models, particularly for binary distri...
Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challeng...
Graphs provide a natural language for relational data in chemistry, biology and optimisation. Graph neural networks (GNNs) have driven much of the rec...
Quantum neural networks have emerged as powerful models for approximating nonlinear functions. Yet their use in solving integro-differential equations...
Insider Brief Press release – AQSolotl and QuantrolOx today announced a strategic partnership to integrate quantum control hardware and automated mach...
Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning com...
Quantum machine learning methods are increasingly explored for modeling complex environmental systems, including groundwater heat plume dynamics. In t...
The Learning With Errors (LWE) problem constitutes the mathematical foundation of modern Post-Quantum Cryptography (PQC). Cryptanalysis of LWE ranges ...
We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantu...
Machine learning models have scaled to unprecedented sizes, making training across distributed devices the de facto standard in the field. In this wor...
The success of variational quantum learning models crucially depends on choosing parametrizations that reflect the structure of the problem at hand. S...