Abstracts

Learning quantum annealing

Presenting Author: Elizabeth Behrman, Wichita State University
Contributing Author(s): James E. Steck

We apply machine learning to "program" a quantum annealing computer, originally designed to solve binary optimization problems. As a proof of concept, it is programmed to anneal to an entangled state; investigating a basic building block of general quantum computing. This has been done in simulation for 2-7 qubits, working toward a goal of demonstrating this in hardware on a super conducting flux qubit quantum annealing machine. Targeted entangled states are the relatively easy GHZ states, the EPR as well as the more difficult W and other states. We also show that the method is robust to noise and decoherence. Quantum annealing machines are, by far, the largest quantum computers currently built and operational. This research targets expanding the use of quantum annealing machines, beyond solving binary optimization problems, to general quantum computing tasks. These machines, via quantum machine learning, can open up new avenues for exploring the many possibilities of this new type of computing hardware. The results of applying machine learning to the task of programming quantum computers give us systematic ways of designing many new algorithms for these machines.

(Session 5 : Thursday from 5:00 - 7:00 pm)

 

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