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Quantum continual learning of quantum data realizing knowledge backward transfer
When a machine learning model is consecutively trained on multiple tasks that come in sequence, its performance on previously learned tasks may drop dramatically during the learning process of the newly seen task.
Variational quantum compiling with double Q-learning
Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U.
Quantum Algorithm for Online Convex Optimization
(ii) We show that for strongly convex loss functions, the quantum algorithm can achieve $O(\log T)$ regret with $O(1)$ queries as well, which means that the quantum algorithm can achieve the same regret bound as the classical algorithms in the full information setting.
Quantum generative adversarial network for generating discrete distribution
Quantum machine learning has recently attracted much attention from the community of quantum computing.
Quantum Physics
