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Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning.
The curse of random quantum data
Provided that the encoding of quantum data is sufficiently random, the performance, we find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in the number of qubits, which we call "the curse of random quantum data".
Quantum Imitation Learning
Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high computation burden.
A Comprehensive Review on Deep Supervision: Theories and Applications
Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network.
Recent Advances for Quantum Neural Networks in Generative Learning
Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
Experimental results verify our theoretical findings in the quantum simulation and quantum chemistry.
Quantum algorithm for finding the negative curvature direction
Moreover, we propose an efficient algorithm to achieve the classical read-out of the target state.
Quantum algorithm for finding the negative curvature direction in non-convex optimization
Moreover, we propose an efficient quantum algorithm to achieve the classical read-out of the target state.
