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Abstract
In the scale-up of quantum computers, the framework underpinning fault-tolerance generally relies on the strong assumption that environmental noise affecting qubit logic is uncorrelated (Markovian). However, as physical devices progress well into the complex multi-qubit regime, attention is turning to understanding the appearance and mitigation of correlated — or non-Markovian — noise, which poses a serious challenge to the progression of quantum technology. This error type has previously remained elusive to characterisation techniques. Here, we develop a framework for characterising non-Markovian dynamics in quantum systems and experimentally test it on multi-qubit superconducting quantum devices. Where noisy processes cannot be accounted for using standard Markovian techniques, our reconstruction predicts the behaviour of the devices with an infidelity of 10−3. Our results show this characterisation technique leads to superior quantum control and extension of coherence time by effective decoupling from the non-Markovian environment. This framework, validated by our results, is applicable to any controlled quantum device and offers a significant step towards optimal device operation and noise reduction.
As quantum computing devices become more complex, they enter the realm of correlated noise, which is difficult to characterise and mitigate. Here, the authors demonstrate, over a range of superconducting devices, a method for non-Markovian dynamics characterisation based on the process tensor framework.
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Details
; Hill, C D 2 ; Pollock, F A 3
; Hollenberg L C L 1
; Modi, K 3
1 University of Melbourne, School of Physics, Parkville, Australia (GRID:grid.1008.9) (ISNI:0000 0001 2179 088X)
2 University of Melbourne, School of Physics, Parkville, Australia (GRID:grid.1008.9) (ISNI:0000 0001 2179 088X); University of Melbourne, School of Mathematics and Statistics, Parkville, Australia (GRID:grid.1008.9) (ISNI:0000 0001 2179 088X)
3 Monash University, School of Physics and Astronomy, Clayton, Australia (GRID:grid.1002.3) (ISNI:0000 0004 1936 7857)




