no code implementations • 20 Nov 2020 • Alexey Uvarov, Jacob Biamonte
Variational quantum algorithms rely on gradient based optimization to iteratively minimize a cost function evaluated by measuring output(s) of a quantum processor.
no code implementations • 23 Jun 2020 • Andrey Kardashin, Alexey Uvarov, Dmitry Yudin, Jacob Biamonte
Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form.
no code implementations • 1 May 2020 • Alexey Uvarov, Jacob Biamonte, Dmitry Yudin
Hybrid quantum-classical algorithms have been proposed as a potentially viable application of quantum computers.
no code implementations • 24 Jun 2019 • Alexey Uvarov, Andrey Kardashin, Jacob Biamonte
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.
no code implementations • 6 Apr 2018 • Andrey Kardashin, Alexey Uvarov, Jacob Biamonte
Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states.