46 curated articles
Speech emotion recognition (SER) remains fragile in real-world conditions because emotional cues are subtle, speaker-dependent, and easily confounded by recording variability, while high-performing deep models typically rely on large and carefully curated training sets. Quantum machine learning offe...
While world models learn compact representations of complex environments, they lack a physics-grounded metric to assess the structural fidelity of the...
We consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. We approach this task as a reinforce...
We report on a gate-based variational quantum classifier implemented with single photons and probabilistic gates, to emulate the standard quantum circ...
Integrated photonic technologies have recently shown significant advances, enabling the possibility to implement reconfigurable interferometers with i...
Artificial intelligence and machine learning have been widely adopted both in the industry and in everyday life, but at the cost of high compute deman...
Insider Brief Machine learning already drives decisions across finance, drug discovery, logistics, and manufacturing. But the datasets and problems in...
The growing computational demands of classical neural networks have intensified the search for energy-efficient and powerful computational alternative...
In the domain of variational quantum algorithms, quantum Fourier models (QFMs) provide a mathematically well defined structure for quantum machine lea...
Neutral atom platforms are analogue quantum simulators that offer the possibility to map graphs onto a 2D qubit register using programmable Rubidium a...
Graph embedding is a recurrent problem in quantum computing, for instance, quantum annealers need to solve a minor graph embedding in order to map a g...
The quantum internet aims to interconnect distant devices and enable large-scale computation through distributed quantum algorithms. One of the key ob...
Quantum machines are among the most promising technologies expected to provide significant improvements in the following years. However, bridging the ...
Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer par...
Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and w...
Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as b...
We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map persp...
Insider Brief PRESS RELEASE — Infleqtion (NYSE: INFQ), a global leader in quantum computing and quantum sensing powered by neutral-atom technology, an...
In this paper, we resolve an open question in the field of optimization algorithms for training parametrized quantum circuits: Does the popular Rotoso...
Image classification is a core task of intelligent sensing, conventionally follows a sequential imaging then processing pipeline. However, redundant h...
Sampling problems are promising candidates for demonstrating quantum advantage, and one approach known as quantum-enhanced Markov chain Monte Carlo [L...
Accurate state-of-health (SOH) estimation for lithium-ion batteries remains a challenging problem due to complex electrochemical degradation mechanism...
Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale mo...
To study generator design for parameterized unitaries in quantum machine learning (QML), we propose an observable-guided generator selection algorithm...
Researchers at UC San Diego’s Picasso Lab have released technical results evaluating the NVIDIA Ising neural pre-decoder. The study explores how light...
Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely ...
The quantum kernel method, a promising quantum machine learning algorithm, possesses substantial potential for demonstrating quantum advantage. Althou...
Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acqui...
Most quantum machine learning (QML) pipelines still rely on static encodings such as angle and amplitude maps, and this limits their ability to handle...
Insider Brief Small quantum computers could process massive datasets more efficiently than far larger classical systems, according to a study recently...
Quantum error correction (QEC) is essential for scalable quantum computing. However, it requires classical decoders that are fast and accurate enough ...
Variational quantum algorithms (VQAs) have established themselves as a central computational paradigm in the Noisy Intermediate-Scale Quantum (NISQ) e...
Quantum kernel methods (QKMs) have emerged as a prominent framework for supervised quantum machine learning. Unlike variational quantum algorithms, wh...
Deploying quantum machine learning algorithms on near-term quantum hardware requires circuits that respect device-specific gate sets, connectivity con...
Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this wor...
Insider Brief PRESS RELEASE — Can a handful of atoms outperform a much larger digital neural network on a real-world task? The answer may be yes. In a...
Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expre...
We propose a hardware efficient quantum residual neural network which implements residual connections through a deterministic linear combination of id...
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, e...
Hybrid quantum neural networks are increasingly explored for classification, yet it remains unclear how their performance and quantum behavior scale w...
Entanglement is a key quantity for characterizing quantum correlations in particle scattering processes, but its direct evaluation is computationally ...
We propose a quantum machine learning framework for estimating classical covariance matrices using parameterized quantum circuits within the Pauli-Cor...
Quantum computers have the potential to solve certain complex problems in a much more efficient way than classical computers. Nevertheless, current qu...
Quantum machine learning models generally lack principled design guidelines, often requiring full resource-intensive training across numerous choices ...
The potential of quantum computing is fundamentally constrained by the inherent susceptibility of qubits to noise and crosstalk, particularly during m...
Quantum Neural Networks (QNNs) are a promising class of quantum machine learning models with potential quantum advantages when implemented on scalable...