Qiskit Quantum Seminar

Currently Playing: Robust and Efficient Quantum Property Learning with Shallow Shadows | Qiskit Quantum Seminar

Robust and Efficient Quantum Property Learning with Shallow Shadows with Hong-Ye Hu Episode 172 Abstract: Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few measurements. While random single qubit measurements are experimentally friendly and suitable for learning low-weight Pauli observables, they perform poorly for nonlocal observables. Prepending a shallow random quantum circuit before measurements maintains this experimental friendliness, but also has favorable sample complexities for observables beyond low-weight Paulis, including high-weight Paulis and global low-rank properties such as fidelity. However, in realistic scenarios, quantum noise accumulated with each additional layer of the shallow circuit biases the results. To address these challenges, we propose the robust shallow shadows protocol. Our protocol uses Bayesian inference to learn the experimentally relevant noise model and mitigate it in postprocessing. and we prove the chosen noise model can mitigate a wide range of quantum noise, including coherent and incoherent errors. The mitigation introduces a bias-variance trade-off: correcting for noise-induced bias comes at the cost of a larger estimator variance. Despite this increased variance, as we demonstrate on a superconducting quantum processor, our protocol correctly recovers state properties such as expectation values, fidelity, and entanglement entropy, while maintaining a lower sample complexity compared to the random single qubit measurement scheme. This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol for characterizing quantum states on current quantum computing platforms. Last but not the lease, I will talk about potential applications of robust shallow shadow on learning low energy spectrum of many-body Hamiltonians. Reference: [1]. Hong-Ye Hu, Andi Gu, Swarnadeep Majumder, Hang Ren, Yipei Zhang, Derek S. Wang, Yi-Zhuang You, Zlatko Minev, Susanne F. Yelin, Alireza Seif. Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows. arXiv: 2402.17911 [2]. Yizhi Shen, Alex Buzali, Hong-Ye Hu, Katherine Klymko, Daan Camps, Susanne F. Yelin, Roel Van Beeumen. Efficient Measurement-Driven Eigenenergy Estimation with Classical Shadows. (In preparation) Bio: Hong-Ye Hu is currently a Harvard Quantum Initiative Fellow with a focus on the intersection of quantum information science and machine learning. His research interest spans several areas, including the machine learning of quantum systems, quantum simulation, quantum optimal controls, and quantum error mitigation and correction. Hong-Ye earned his PhD from the University of California, San Diego, in 2022. Before that, he completed his Bachelor of Science degree at Peking University in 2016. In addition to his academic pursuits, Hong-Ye has actively collaborated with leading industry players such as IBM Quantum, QuEra Computing, NASA Quantum AI Lab, and Nvidia, contributing his expertise to push the boundaries of quantum computing technology.