Learning Algorithm for Quantum Error Correction

Using quantum mechanics to improve information technology has been an interdisciplinary exercise. The challenge in implementing quantum information technology arises primarily from the fragile nature of quantum systems under various noises. We focus on the problem of correcting such errors that occur in the superconducting quantum circuits, which is a promising candidate for realizing a scalable quantum computer. Although there exist a few proposals for quantum error correction in superconducting circuits, a scalable high-fidelity implementation of these algorithms require precise control over the superconducting circuit parameters. We propose to use a learning algorithm, which is a tool from computer science, to generate fast, high-fidelity implementation of quantum error correction under realistic constraints on the superconducting control electronics. This problem will be investigated using computer simulation in order to access the feasibility of the method.

Faculty Supervisor:

Barry Sanders

Student:

Pantita Palittapongarnpim

Partner:

Discipline:

Physics / Astronomy

Sector:

University:

University of Calgary

Program:

Globalink Research Award