60 curated articles
Quantum computing developer D-Wave Quantum Inc. (NASDAQ: QBTS) has signed an agreement with Nasdaq Verafin—the financial crime management technology unit of Nasdaq—to evaluate the use of quantum-hybrid computing in combating financial crime. The collaboration focuses on leveraging D-Wave’s quantum a...
Quantum algorithms based on linear-system approaches for solving differential equations demand qubit and precision resources beyond near-term capabili...
Researchers from the Cleveland Clinic and IBM Research have developed a quantum machine learning framework to predict which tumor gene mutations gener...
Roadside units (RSUs) supporting connected and autonomous vehicle corridors need compact models to decide when cooperative maneuvers should be rewarde...
Analog quantum processors based on Rydberg atom arrays are a powerful platform for many-body quantum simulation, combinatorial optimization, and graph...
Insider Brief PRESS RELEASE — Cleveland Clinic and IBM researchers are using quantum computing to tackle one of the most challenging problems in immun...
Insider Brief A proposed quantum machine-learning framework could make larger quantum neural networks easier to train while preserving computations th...
Quantum networks can enable distributed computing and sensing. To realize these capabilities securely, quantum identity authentication is essential. W...
Insider Brief Press release – The Washington Institute for STEM, Entrepreneurship and Research (WISER) announces the successful completion of a resear...
Variational quantum algorithms are a leading approach to near-term quantum computing, but their scalability can be limited by barren plateaus and the ...
Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a se...
Neutral atom quantum systems offer a promising pathway to large-scale quantum computing due to high qubit uniformity and flexible connectivity. To exp...
Insider Brief Researchers have developed a new framework that uses the geometry of quantum states to help quantum machine learning models retain previ...
Real-time decoding is a critical bottleneck for large-scale fault-tolerant quantum computing. AI-based neural pre-decoders locally correct most physic...
Variational Quantum Algorithms (VQAs) are a leading paradigm for near-term quantum computing, combining parameterized quantum circuits with classical ...
Pauli-based simulators offer a promising route to large-scale classical simulation of quantum circuits in the low-magic regime. Yet their applicabilit...
Quantum machine learning on real noisy intermediate-scale quantum (NISQ) hardware has remained largely confined to binary or few-class tasks, limited ...
Bell nonlocality is both a defining signature of entanglement and a key quantum information resource. However, visualizing and certifying nonlocal cor...
Across public tabular benchmarks, quantum machine-learning (QML) models usually lose to carefully tuned classical baselines. We argue that this is a s...
We characterize and compare the inherent interpretability offerings of a standard linear model with a single qubit mixed state model for the task of s...
Kerala, India-based quantum software provider Bloq Quantum has announced a strategic partnership with MIUUL, a premier technology training and skillin...
Insider Brief Press release – Bloq Quantum, a leading provider of enterprise quantum software infrastructure, today announced a strategic partnership ...
The field of quantum machine learning (QML) evolved to value models believed to most directly rival those providing utility in classical ML, namely la...
The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adver...
This thesis investigates the application of machine-learning methods in the context of quantum computing and neutrino physics, with particular emphasi...
Parameterized quantum circuits~(PQCs) constitute a central building block of variational quantum algorithms~(VQAs) and quantum machine learning~(QML) ...
A central model in quantum machine learning is the quantum neural network (QNN), whose design requires balancing expressivity and trainability. Techni...
This article explores the potential of Quantum Machine Learning (QML), specifically assessing a Quantum Support Vector Machine (QSVM) and a Variationa...
Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems, in which these two tasks are typi...
NVIDIA’s Quantum Computing Division has introduced Ising, an open-source model family designed to deploy neural-network-driven control layers for faul...
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...