noisy intermediate-scale quantum era
PulseAugur coverage of noisy intermediate-scale quantum era — every cluster mentioning noisy intermediate-scale quantum era across labs, papers, and developer communities, ranked by signal.
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Meta-learning algorithms enhance quantum Gibbs state preparation for Boltzmann Machines
Researchers have developed two meta-learning algorithms, Meta-Variational Quantum Thermalizer (Meta-VQT) and Neural Network Meta-VQT (NN-Meta VQT), designed to efficiently prepare quantum Gibbs states on NISQ devices. T…
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Quantum layers show promise for boosting VLM accuracy and reducing parameters
Researchers have explored the integration of quantum layers into vision-language models (VLMs) to assess potential performance and security enhancements. By using classically simulated quantum circuits within the LLaVA-…
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Hybrid Quantum-Classical Pipeline Enhances Medical Image Classification
Researchers have developed a novel hybrid quantum-classical pipeline for medical image classification, specifically for polyp detection. This system utilizes neutral-atom reservoir computing integrated with a guided aut…
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New framework enhances quantum reinforcement learning on noisy hardware
Researchers have developed an adaptive error mitigation framework called APGEM for hybrid quantum reinforcement learning on noisy intermediate-scale quantum (NISQ) systems. This system dynamically selects the most suita…
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New quantum incremental learning framework uses mixed-state prototypes
Researchers have developed a new quantum incremental learning framework designed to address limitations in the Noisy Intermediate-Scale Quantum (NISQ) era. This framework utilizes trainable mixed-state prototypes to seq…
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New quantum graph learning architecture designed for NISQ era
Researchers have developed a novel quantum graph convolutional architecture specifically designed for unsupervised learning within the noisy intermediate-scale quantum (NISQ) era. This approach utilizes a variational qu…
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Quantum-Classical Hybrid Models Tackle Time-Series Forecasting on NISQ Hardware
Researchers have developed new quantum-classical hybrid frameworks for multivariate time-series forecasting, designed to operate on near-term noisy intermediate-scale quantum (NISQ) hardware. These frameworks, including…
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Optimal FALQON enhances quantum optimization on NISQ devices
Researchers have introduced Optimal FALQON, an enhanced version of the Feedback-based Adaptive Quantum Optimization (FALQON) method designed to improve performance on noisy intermediate-scale quantum (NISQ) devices. Thi…
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New framework aids discovery of quantum-ready battery datasets
Researchers have introduced IonSense-QKG, a new metadata framework designed to help discover and evaluate lithium-ion battery datasets for use in quantum machine learning workflows. This framework assigns a Quantum Read…
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Quantum Kernel Bandit Optimization Balances Expressivity and Learnability
Researchers have developed new methods for Gaussian process bandit optimization using quantum kernels, specifically addressing challenges in the noisy intermediate-scale quantum (NISQ) era. The study focuses on balancin…
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Reinforcement learning disentangles multiqubit quantum states
Researchers have developed a novel deep reinforcement learning approach to create efficient disentangling circuits for quantum states. This method utilizes partial observations, specifically two-qubit reduced density ma…
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New framework assesses quantum neural network robustness to noise
Researchers have introduced JGRA, a new framework designed to assess the robustness of quantum neural networks (QNNs) in the presence of noise. This method utilizes Jacobian geometry to analyze how sensitive QNNs are to…
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Quantum ML framework QADR enhances scalability and performance
Researchers have developed a new hybrid quantum-classical machine learning framework called QADR to address limitations in training quantum circuits. QADR decomposes large quantum circuits into smaller, localized sub-ci…
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Quantum Reservoir Computing outperforms QPINNs for chaotic dynamics prediction
Researchers have benchmarked two quantum machine learning architectures, Quantum Reservoir Computing (QRC) and Quantum Physics-Informed Neural Networks (QPINNs), for predicting chaotic time-series data. On the Lorenz sy…