Recent Progress on Classical Shadow Tomography | Qiskit Quantum Seminar with Hong-Ye Hu

Recent Progress on Classical Shadow Tomography | Qiskit Quantum Seminar with Hong-Ye Hu
Abstract:
Classical shadow tomography (CST) provides an efficient method for predicting many properties of unknown quantum states with a few measurements. It has many applications ranging from quantum chemistry to quantum machine learning. The success of CST relies on a unitary channel that efficiently scrambles quantum information of the states to the measurement basis. However, realizing deep unitary circuits with near-term quantum devices is very challenging. In this talk, I will solve these important questions by combining classical shadow tomography with locally scrambled unitary ensembles. The new protocol is very flexible that is compatible with digital quantum computers with shallow circuits or analog quantum computers. I will demonstrate the advantages of shadow tomography in the shallow circuit region and introduce a tensor network method that enables us to post-process data efficiently. In the end, I will also discuss the possibility of applying CST in the early fault-tolerant region and CST with hybrid quantum circuits.
Refs:
[1] H.-Y. Hu, S. Choi, Y.-Z. You. Phys. Rev. Research 5, 023027 (2023)
[2] A. Akhtar, H.-Y. Hu, Y.-Z. You. Quantum 7, 1026 (2023)
[3] H.-Y. Hu, R. LaRose, Y.-Z. You, E. Rieffel, Z. Wang. arXiv:2203.07263 (2022)
[4] A. Akhtar, H.-Y. Hu, E. Altman, Y.-Z. You, arXiv:2308.01653 (2023)
BIO:
Hong-Ye Hu received his Ph.D. from the University of California, San Diego in 2022 and his B.S. from Peking University in 2016. He is the Harvard Quantum Initiative (HQI) Postdoctoral Fellow in the Department of Physics. His research interests include machine learning for quantum physics, quantum tomography, variational quantum algorithms, and quantum error correction. Besides academic positions, he has interned with USRA&NASA Quantum AI Lab, QuEra Computing, and Salk Institute for Biological Studies.

[@jorgemuroarbulu3267]

Lima, Peru

[@MichaelZibulevsky]

Are the slides available? Thanks!