Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Explore the quantum analog of the Learning Parity with Noise problem through this 30-minute conference talk that introduces the Learning Stabilizers with Noise (LSN) problem. Discover how LSN represents the task of decoding a random stabilizer code in the presence of local depolarizing noise, building upon the classical challenge of decoding random linear codes that has puzzled researchers for decades despite extensive study. Learn how random classical codes possess excellent error correcting properties yet remain notoriously difficult to decode in practice, with the fastest known algorithms still requiring exponential time. Understand how the Learning Parity with Noise problem emerged as a prominent hardness assumption with widespread applications in cryptography and learning theory. Examine concrete evidence for LSN's computational difficulty, including low degree hardness results and worst-to-average-case reductions. Gain insights into how LSN encompasses LPN as a special case, suggesting it presents at least equivalent computational challenges to its classical counterpart while extending into the quantum domain.
