Noise and its control in quantum information processing tasks
After more than two decades of working towards building a computer based on quantum mechanical principles, a basic obstacle remains: that of noise. Noise-suppression techniques have shown encouraging progress, but the noise threshold below which reliable computation can be accomplished is still far from attainable. We seek better understanding of the physical nature of the noise affecting quantum information processing (QIP) tasks beyond the naive quantum-channel description, and examine the efficacy of error correction and fault-tolerant schemes for different types of noise and different practical QIP tasks.
This project is supported by a start-up grant and a Yale-NUS Internal (MoE Tier-1) grant.
Relevant publications and manuscripts:
- J Dai, YL Len, and HK Ng, Initial system-bath state via the maximum-entropy principle, Phys Rev A 94, 052112 (2016); arXiv:1508.06736 [quant-ph].
- Y Zheng and HK Ng, Digital quantum simulator in the presence of a bath, Phys Rev A 96, 042329 (2017); arXiv:1707:04407 [quant-ph].
- YL Len and HK Ng, Open-system quantum error correction, Phys Rev A 98, 022307 (2018); arXiv:1804:09486 [quant-ph].
- JH Chai and HK Ng, Comparison of different fault-tolerance schemes (2018). (In preparation.)
Selected past works of relevance:
- HK Ng, DA Lidar, and J Preskill, Combining dynamical decoupling with fault-tolerant quantum computation, Phys Rev A 84, 012305 (2011).
- HK Ng and P Mandayam, Simple approach to approximate quantum error correction based on the transpose channel, Phys Rev A 81, 062342 (2010).
- HK Ng and J Preskill, Fault-tolerant quantum computation versus Gaussian noise, Phys Rev A 79, 032318 (2009).
Estimating relevant noise parameters
An essential input to effective noise-suppression strategies is the nature of the noise that afflicts the quantum system. Here, we seek to advance methods for the practical characterization of noise in quantum systems, through better use of limited data from resource-expensive standard process tomography, as well as direct measurements of noise parameters relevant for high-fidelity QIP tasks.
This project is supported by a new Tier-2 grant (PI: HK Ng) from the Ministry of Education (MoE), Singapore, and done in collaboration with B-G Englert (co-PI; CQT and Dept of Physics, NUS) and J Suzuki (collaborator, U Electro-Comm, Japan).
The work builds upon past work on quantum state estimation based on Bayesian methods, done in collaboration with B-G Englert (CQT and Dept of Physics, NUS) and DJ Nott (Dept of Statistics & Applied Probability, NUS).
- JY Sim, J Suzuki, B-G Englert, and HK Ng, User-specified random sampling of quantum channels and its applications, arXiv:1905.00696 [quant-ph] (2019).
- Y Gazit, HK Ng, and J Suzuki, Quantum process tomography via optimal design of experiments, arXiv:1904.11849 [quant-ph] (2019).
- Y Quek, S Fort, and HK Ng, Adaptive Quantum State Tomography with Neural Networks, arXiv:1812.06693 [quant-ph] (2018).
- J Qi and HK Ng, Comparing the randomized benchmarking figure with the average infidelity of a quantum gate-set, arXiv:1805.10622 [quant-ph] (2018) (submitted to the International Journal of Quantum Information).
- J Shang, HK Ng, A Sehrawat, X Li, and B-G Englert, Optimal error regions for quantum state estimation, New J Phys 15, 123026 (2013).