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Ph.D. Dissertation Defense - Swamit Tannu

Event Details

Tuesday, September 15, 2020

3:00pm - 5:00pm

Location: 
https://bluejeans.com/548529289

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Event Details

TitleSoftware Techniques to Mitigate Errors in Noisy Quantum Computers

Committee:

Dr. Moinuddin Qurreshi, ECE, Chair , Advisor

Dr. Tushar Krishna, ECE

Dr. Vivek Sarkar, CoC

Dr. Asif Khan, ECE

Dr. Kenneth Brown, Duke

Abstract: Quantum computers are domain-specific accelerators that can provide a large speedup for important problems. Quantum computers with few tens of qubits have already been demonstrated, and machines with 100+ qubits are expected soon. These machines face significant reliability and scalability challenges. Due to limited and unreliable qubits, these machines are operated in the Noisy Intermediate Scale Quantum (NISQ) mode of computing. The computation on a NISQ machine can produce incorrect output. Therefore, in the NISQ mode, a program is run thousands of times, and the output log is analyzed to infer the correct output. However, the error rates on current quantum hardware are such that the likelihood of obtaining the right answer is still quite small for NISQ machines, and this problem only becomes worse for programs with a large number of instructions. This dissertation shows how the reliability of near-term quantum computers can be improved by developing software techniques. Our first work (ASPLOS 2019) exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid error-prone qubits. Our second work (MICRO 2019a) looks at executing different versions of the programs tuned to cause diverse mistakes so that the machine is less vulnerable to correlated errors, thereby making it easier to infer the correct answer. Our third work (MICRO 2019b) looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to measure the stronger state. We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability.

Last revised September 10, 2020