Search Results for author:
Found 5 papers, 0 papers with code
On barren plateaus and cost function locality in variational quantum algorithms
Variational quantum algorithms rely on gradient based optimization to iteratively minimize a cost function evaluated by measuring output(s) of a quantum processor.
Certified variational quantum algorithms for eigenstate preparation
Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form.
Variational Quantum Eigensolver for Frustrated Quantum Systems
Hybrid quantum-classical algorithms have been proposed as a potentially viable application of quantum computers.
Machine Learning Phase Transitions with a Quantum Processor
To overcome this slowdown while still leveraging machine learning, we propose a variational quantum algorithm which merges quantum simulation and quantum machine learning to classify phases of matter.
Quantum Machine Learning Tensor Network States
Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states.
