551 citations · 728 across the 6 of their papers we have counts for
16 papers · 1 filter
Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data
David Wakeham, Maria Schuld
How can we take inspiration from a typical quantum algorithm to design heuristics for machine learning? A common blueprint, used from Deutsch-Josza to Shor's algorithm, is to place…
Quantum computing with differentiable quantum transforms
Olivia Di Matteo, Josh Izaac, Tom Bromley +6
We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiabil…
Quantum circuits with many photons on a programmable nanophotonic chip
J. M. Arrazola, V. Bergholm, K. Brádler +36
Growing interest in quantum computing for practical applications has led to a surge in the availability of programmable machines for executing quantum algorithms. Present day photo…
Supervised quantum machine learning models are kernel methods
Maria Schuld
With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replac…
The effect of data encoding on the expressive power of variational quantum machine learning models
Maria Schuld, Ryan Sweke, Johannes Jakob Meyer
Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions. While a lot of work has been done to…
Quantum Machine Learning in High Energy Physics
Wen Guan, Gabriel Perdue, Arthur Pesah +4
Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early…