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Found 5 papers, 1 papers with code
Scalably learning quantum many-body Hamiltonians from dynamical data
The physics of a closed quantum mechanical system is governed by its Hamiltonian.
A single $T$-gate makes distribution learning hard
We first show that the generative modelling problem associated with depth $d=n^{\Omega(1)}$ local quantum circuits is hard for any learning algorithm, classical or quantum.
Learnability of the output distributions of local quantum circuits
As many practical generative modelling algorithms use statistical queries -- including those for training quantum circuit Born machines -- our result is broadly applicable and strongly limits the possibility of a meaningful quantum advantage for learning the output distributions of local quantum circuits.
Quantum Computational Advantage via High-Dimensional Gaussian Boson Sampling
Theoretically, there is a comparative lack of rigorous evidence for the classical hardness of GBS.
Quantum Physics
On the Quantum versus Classical Learnability of Discrete Distributions
Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework.
