Quantum Meta-Learning: Learning to Learn on Qubits
Quantum meta-learning uses classical or quantum models to learn good starting parameters for quantum circuits. Ideas, algorithms and challenges.
Quantum meta-learning uses classical or quantum models to learn good starting parameters for quantum circuits. Ideas, algorithms and challenges.
How policy gradient methods are applied in quantum reinforcement learning, how agents are trained and where quantum circuits might help.
Quantum Markov models and hidden quantum Markov models describe processes with quantum states. Transitions, inference and applications.
Stochastic gradient descent in quantum machine learning: how gradients of quantum circuits are estimated from samples and the challenges involved.
Proposals for a quantum perceptron built from qubits and gates, how training would work, and how it compares with the classical perceptron.
Quantum generative adversarial networks pit a quantum generator against a discriminator. Architecture, training and possible applications.
Architectures for quantum reinforcement learning, how they compare with classical reinforcement learning, and where they are being tested.
Quantum versions of k-means clustering, how they compute distances between points, the claimed speedups and the data loading caveat.
Information spectrum methods handle quantum information tasks without assuming identical repeated uses of a channel. Concepts and applications.
Verifiable quantum secret sharing lets participants check that their shares are consistent. Threshold schemes, security and implementations.
Cryptographic hash functions in a quantum world: how quantum attacks affect SHA-2 and SHA-3, quantum hash proposals and hash-based signatures.
Sampling problems such as boson sampling and random circuit sampling were used to demonstrate quantum advantage. Why they are hard classically.