Quantum machine learning

Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many …
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Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Optimal Quantum Dataset for Learning a Unitary Transformation
Unitary transformations formulate the time evolution of quantum states. How to learn a unitary transformation efficiently is a …
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Concentration of Data Encoding in Parameterized Quantum Circuits
Variational quantum algorithms have been acknowledged as the leading strategy to realize near-term quantum advantages in meaningful …
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Power and limitations of single-qubit native quantum neural networks
Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and …
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Practical distributed quantum information processing with LOCCNet
Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. …
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Variational Quantum Singular Value Decomposition
Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been …
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Noise-Assisted Quantum Autoencoder
Quantum autoencoder is an efficient variational quantum algorithm for quantum data compression. However, previous quantum autoencoders …
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Quantum algorithms for hedging and the learning of Ising models
A paradigmatic algorithm for online learning is the Hedge algorithm by Freund and Schapire. An allocation into different strategies is …
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More Practical and Adaptive Algorithms for Online Quantum State Learning
Online quantum state learning is a recently proposed problem by Aaronson et al. (2018), where the learner sequentially predicts …
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