Collaborative learning of high-precision quantum control and tomography
Collaborative learning of high-precision quantum control and tomography
High-precision control of quantum states and gate operations is essential to the hardware implementation of quantum computation. Recently, online calibration has become an important tool for correcting errors induced by parameter shifts or environmental noises in the underlying quantum control systems. However, the experimental cost for acquiring information through quantum tomography (for state or gate reconstruction) is very high, especially when many iterations are to be done. In this paper, we propose a novel scheme that integrates the gradient-descent optimization of quantum control pulses with the adaptive learning of quantum tomography as two interactive processes, which updates the control iteratively with the progressively refined state tomography. This scheme, which we call c-GRAPE, can greatly improve the calibration efficiency by substantial reduction the experimental cost for tomography without sacrificing the control precision.
128 - 133
Ding, H.-J.
54d8df5a-573c-49a7-aead-cf651a0479aa
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Qi, B.
43a96f47-38c3-4fb0-971f-5bc374ee3c46
Wu, R.-B.
0a6da656-1956-448b-b3fe-a340a2993f4b
Ding, H.-J.
54d8df5a-573c-49a7-aead-cf651a0479aa
Chu, B.
555a86a5-0198-4242-8525-3492349d4f0f
Qi, B.
43a96f47-38c3-4fb0-971f-5bc374ee3c46
Wu, R.-B.
0a6da656-1956-448b-b3fe-a340a2993f4b
Ding, H.-J., Chu, B., Qi, B. and Wu, R.-B.
(2020)
Collaborative learning of high-precision quantum control and tomography.
Physical Review Applied, 52 (29), .
(doi:10.1103/PhysRevApplied.16.014056).
Abstract
High-precision control of quantum states and gate operations is essential to the hardware implementation of quantum computation. Recently, online calibration has become an important tool for correcting errors induced by parameter shifts or environmental noises in the underlying quantum control systems. However, the experimental cost for acquiring information through quantum tomography (for state or gate reconstruction) is very high, especially when many iterations are to be done. In this paper, we propose a novel scheme that integrates the gradient-descent optimization of quantum control pulses with the adaptive learning of quantum tomography as two interactive processes, which updates the control iteratively with the progressively refined state tomography. This scheme, which we call c-GRAPE, can greatly improve the calibration efficiency by substantial reduction the experimental cost for tomography without sacrificing the control precision.
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e-pub ahead of print date: 15 January 2020
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Local EPrints ID: 471581
URI: http://eprints.soton.ac.uk/id/eprint/471581
ISSN: 2331-7019
PURE UUID: 83639003-fca0-4b7c-88bf-74d15890f31e
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Date deposited: 14 Nov 2022 17:37
Last modified: 17 Mar 2024 03:28
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Author:
H.-J. Ding
Author:
B. Chu
Author:
B. Qi
Author:
R.-B. Wu
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