5th Year Master's Thesis Presentation - Samvitti Sharma
— 11:30am
Location:
In Person
-
Traffic21 Classroom, Gates Hillman 6501
Speaker:
SAMVITTI SHARMA
,
Master's Student, Computer Science Department, Carnegie Mellon University
https://www.linkedin.com/in/samvitti-sharma-7a35b3225
The Pauli channel is a fundamental model of noise in quantum systems, motivating the task of Pauli error estimation. We present an algorithm that builds on the reduction to Population Recovery introduced in [FO21]. Addressing an open question from that work, our algorithm has the key advantage of robustness against even severe state preparation and measurement (SPAM) errors. To tolerate SPAM, we must analyze Population Recovery on a combined erasure/bit-flip channel, which necessitates extending the complex analysis techniques from [PSW17, DOS17]. For n-qubit channels, our Pauli error estimation algorithm requires only exp(n1/3) unentangled state preparations and measurements, improving on previous SPAM-tolerant algorithms that had 2n-dependence even for restricted families of Pauli channels. We also give evidence that no SPAM-tolerant method can make asymptotically fewer than exp(n1/3) uses of the channel.
Thesis Committee
Ryan O'Donnell (Chair)
David Woodruff
Additional Information
For More Information:
amalloy@cs.cmu.edu