Search Results for author:
Found 7 papers, 0 papers with code
Statistical Limits of Supervised Quantum Learning
Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy.
Approximating Hamiltonian dynamics with the Nyström method
Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers.
Learning DNFs under product distributions via μ-biased quantum Fourier sampling
We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle.
Experimental learning of quantum states
The number of parameters describing a quantum state is well known to grow exponentially with the number of particles.
Learning hard quantum distributions with variational autoencoders
This suggests that the probability distributions associated to hard quantum states might have a compositional structure that can be exploited by layered neural networks.
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Stabiliser states are efficiently PAC-learnable
State tomography, whose objective is to obtain a full description of a quantum system, can be analysed in the framework of computational learning theory.
