Quantum machine learning

Thermal-Drift Sampling: Generating Thermal Ensembles for Learning Many-Body Systems
Thermal equilibrium states of many-body Hamiltonians are essential for probing quantum chaos, finite-temperature phases of matter, and …
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Thermal-Drift Sampling: Generating Thermal Ensembles for Learning Many-Body Systems
Quantifying Unextendibility via Virtual State Extension
Monogamy of entanglement, which limits how entanglement can be shared among multiple parties, is a fundamental feature underpinning the …
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Quantifying Unextendibility via Virtual State Extension
Optimizer-Dependent Generalization Bound for Quantum Neural Networks
Quantum neural networks (QNNs) play a pivotal role in addressing complex tasks within quantum machine learning, analogous to classical …
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Optimizer-Dependent Generalization Bound for Quantum Neural Networks
LCQNN: Linear Combination of Quantum Neural Networks
Quantum neural networks combine quantum computing with advanced data-driven methods, offering promising applications in quantum machine …
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LCQNN: Linear Combination of Quantum Neural Networks
Quantum Recurrent Embedding Neural Network
Quantum neural networks have emerged as promising quantum machine learning models, leveraging the properties of quantum systems and …
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Quantum Recurrent Embedding Neural Network
Provable Advantage of Parameterized Quantum Circuit in Function Approximation
Parameterized quantum circuits (PQCs) have emerged as a promising approach for quantum neural networks. However, understanding their …
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Provable Advantage of Parameterized Quantum Circuit in Function Approximation
Predicting quantum learnability from landscape fluctuation
The conflict between trainability and expressibility is a key challenge in variational quantum computing and quantum machine learning. …
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Predicting quantum learnability from landscape fluctuation
Quantum Sequential Scattering Model for Quantum State Learning
Learning probability distribution is an essential framework in classical learning theory. As a counterpart, quantum state learning has …
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Quantum Sequential Scattering Model for Quantum State Learning
Mitigating Barren Plateaus of Variational Quantum Eigensolvers
Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent …
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Mitigating Barren Plateaus of Variational Quantum Eigensolvers
Quantum self-attention neural networks for text classification
An emerging direction of quantum computing is to establish meaningful quantum applications in various fields of artificial …
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Quantum self-attention neural networks for text classification