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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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…