Convolutional neural networks (CNNs) have recently proven successful for many complex applications ranging from image recognition to precision medicine. Motivated by recent advances in realizing quantum information processors, I introduce and analyze a quantum circuit-based algorithm inspired by CNNs. Our quantum convolutional neural network (QCNN) uses only O(log(N)) variational parameters for input sizes of N qubits, allowing for its efficient training and implementation on realistic, near-term quantum devices. To explicitly illustrate its capabilities, I show that QCNN can accurately recognize quantum states associated with a one-dimensional symmetry-protected topological phase, with performance surpassing existing approaches. I further demonstrate that QCNN can be used to devise a quantum error correction (QEC) scheme optimized for a given, unknown error model that substantially outperforms known quantum codes of comparable complexity. The design of such error correction codes is particularly important for near-term experiments, whose error models may be different from those addressed by general-purpose QEC schemes.

Date: Friday, 6 December 2019

Time: 8:00pm – 10:00pm (BKK Time)

Venue: Online event


Programme Details:

8:00pm – 8:20pm: Networking

8:20pm – 8:30pm: Introduction to QTFT

8:30pm – 9:30pm: Talk

9:30pm – 10:00pm: Q&A

About the speaker :

My name is Iris, and I’m a third-year graduate student at Harvard. After spending my four undergraduate years at UCLA studying computer science, I’m now pursuing my Ph.D. in physics under the supervision of Prof. Mikhail Lukin. My research interests include theoretical quantum information, AMO physics, and condensed matter physics.