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 direction of research is currently supported by a CQT Fellowship, a Merlion project grant, and a Tier-2 grant from the Ministry of Education, Singapore.
- A Jayashankar, AM Babu, HK Ng, and P Mandayam, Finding good codes using the Cartan form, Phys Rev A 101, 042307 (2020); arXiv:1911.02965.
- YL Len and HK Ng, Open-system quantum error correction, Phys Rev A 98, 022307 (2018); arXiv:1804:09486.
- Y Zheng and HK Ng, Digital quantum simulator in the presence of a bath, Phys Rev A 96, 042329 (2017); arXiv:1707:04407.
- 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.
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 Tier-2 grant from the Ministry of Education, Singapore, and done in collaboration with B-G Englert (CQT and NUS) and J Suzuki (U Electro-Comm, Japan).
- JY Sim, J Suzuki, B-G Englert, and HK Ng, User-specified random sampling of quantum channels and its applications, Phys Rev A 101, 022307 (2020); arXiv:1905.00696.
- Y Gazit, HK Ng, and J Suzuki, Quantum process tomography via optimal design of experiments, Phys Rev A 100, 012350 (2019); arXiv:1904.11849.
- Y Quek, S Fort, and HK Ng, Adaptive Quantum State Tomography with Neural Networks, arXiv:1812.06693 (2018).
- J Qi and HK Ng, Comparing the randomized benchmarking figure with the average infidelity of a quantum gate-set, Int J Quant Inf 17, 1950031 (2019); arXiv:1805.10622.
- 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).