Search Results for author:
Found 8 papers, 0 papers with code
Machine learning applications for noisy intermediate-scale quantum computers
We discuss and present a framework for quantum advantage for such models, propose gradient-based training methods and demonstrate these both numerically and on the Rigetti quantum computer up to 28 qubits.
Graph neural network initialisation of quantum approximate optimisation
In this work, we focus on the quantum approximate optimisation algorithm (QAOA) for solving the MaxCut problem.
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
In particular, we consider training a quantum circuit Born machine using $f$-divergences.
Variational inference with a quantum computer
One alternative is variational inference, where a candidate probability distribution is optimized to approximate the posterior distribution over unobserved variables.
Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis
In this work, we propose variational quantum cloning (VQC), a quantum machine learning based cryptanalysis algorithm which allows an adversary to obtain optimal (approximate) cloning strategies with short depth quantum circuits, trained using hybrid classical-quantum techniques.
Quantum versus Classical Generative Modelling in Finance
Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum technologies.
Robust data encodings for quantum classifiers
Data representation is crucial for the success of machine learning models.
The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
In particular, we explore quantum circuit learning using non-universal circuits derived from Ising Model Hamiltonians, which are implementable on near term quantum devices.
