1 citations · 2 across the 5 of their papers we have counts for
6 papers
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…
Spectral methods: crucial for machine learning, natural for quantum computers?
Vasilis Belis, Joseph Bowles, Rishabh Gupta +2
This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularis…
IQP Born Machines under Data-dependent and Agnostic Initialization Strategies
Sacha Lerch, Joseph Bowles, Ricard Puig +3
Quantum circuit Born machines based on instantaneous quantum polynomial-time (IQP) circuits are natural candidates for quantum generative modeling, both because of their probabilis…
Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits
Erik Recio-Armengol, Shahnawaz Ahmed, Joseph Bowles
We propose an approach to generative quantum machine learning that overcomes the fundamental scaling issues of variational quantum circuits. The core idea is to use a class of gene…
IQPopt: Fast optimization of instantaneous quantum polynomial circuits in JAX
Erik Armengol, Joseph Bowles
IQPopt is a software package designed to optimize large-scale instantaneous quantum polynomial circuits on classical hardware. By exploiting an efficient classical simulation algor…
Better than classical? The subtle art of benchmarking quantum machine learning models
Joseph Bowles, Shahnawaz Ahmed, Maria Schuld
Benchmarking models via classical simulations is one of the main ways to judge ideas in quantum machine learning before noise-free hardware is available. However, the huge impact o…