6 papers · 1 filter
Spectral Born machines: classically trainable quantum generative models for discrete data
Austin Huang, William Maxwell, Vasilis Belis +4
We present \emph{spectral Born machines}, a class of quantum generative models that results from viewing and generalizing the class of IQP Born machines through the lens of group F…
Learning out-of-time-ordered correlators with classical kernel methods
John Tanner, Jason Pye, Jingbo Wang
Out-of-Time Ordered Correlators (OTOCs) are widely used to investigate information scrambling in quantum systems. However, directly computing OTOCs with classical computers is an e…
Gravitational-wave matched filtering with variational quantum algorithms
Jason Pye, Edric Matwiejew, Aidan Smith +3
In this paper, we explore the application of variational quantum algorithms designed for classical optimization to the problem of matched filtering in the detection of gravitationa…
Quantum Optimisation for Continuous Multivariable Functions by a Structured Search
Edric Matwiejew, Jason Pye, Jingbo B. Wang
Solving optimisation problems is a promising near-term application of quantum computers. Quantum variational algorithms leverage quantum superposition and entanglement to optimise…
Impact of relativity on particle localizability and ground state entanglement
Maria Papageorgiou, Jason Pye
Can a relativistic quantum field theory be consistently described as a theory of localizable particles? There are many known issues with such a description, indicating an answer in…
A Universal Training Algorithm for Quantum Deep Learning
Guillaume Verdon, Jason Pye, Michael Broughton
We introduce the Backwards Quantum Propagation of Phase errors (Baqprop) principle, a central theme upon which we construct multiple universal optimization heuristics for training…