Probing quantum processor performance with pyGSTi

Nielsen, Erik; Rudinger, Kenneth; Proctor, Timothy; Russo, Antonio; Young, Kevin; Blume-Kohout, Robin

doi:10.1088/2058-9565/ab8aa4

Cited by 80 publications

(84 citation statements)

References 86 publications

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“…GST comparison. The GST experiments conducted in the 'Comparison with GST' subsection were completed using the pyGSTi quantum processor performance package 38,39 . Following the procedures outlined in the documentation, with background given in refs.…”

Section: Methodsmentioning

confidence: 99%

Demonstration of non-Markovian process characterisation and control on a quantum processor

et al. 2020

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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.

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Section: Methodsmentioning

confidence: 99%

Demonstration of non-Markovian process characterisation and control on a quantum processor

et al. 2020

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“…These techniques are even applicable outside of the context of quantum computing-they could be used for time-resolved quantum sensing. We have incorporated these tools into an open-source software package 52,53 , making it easy to check any time-series QIP data for signs of instability. Because of the disastrous impact of drift on characterization protocols [16][17][18][19][20][21][22] , its largely unknown impact on QIP applications, and the minimal overhead required to implement our methods, we hope to see this analysis broadly and quickly adopted.…”

Section: Discussionmentioning

confidence: 99%

“…The code for implementing the general drift characterization methods introduced in this paper has been incorporated into the open-source Python package pyGSTi 52 , 53 . The pyGSTi-based Python scripts and notebooks used for the data analysis reported in this paper are available at 10.5281/zenodo.4033077.…”

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confidence: 99%

Detecting and tracking drift in quantum information processors

et al. 2020

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If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

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“…This inference procedure can be quite sophisticated in practice, with carefully designed experiments to tease out very slight channel imperfections. Over the past several years, GST has been demonstrated experimentally on a wide variety of platforms [15][16][17][18][19][20][21][22][23][24][25][26][27][28], predominately using the software package pyGSTi [29,30].…”

Section: Introductionmentioning

confidence: 99%

“…Button labels are abbreviated b H → H and b S → S for simplicity when specifying button sequences. − )G H (δθ H ) + Λ H , δθ H ∈ N (0, 0.0015), Λ H ∈ BCSZ(2) Eqs (30),(34)…”

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confidence: 99%

Operational, gauge-free quantum tomography

Matteo

Gamble

Granade

et al. 2020

Quantum

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As increasingly impressive quantum information processors are realized in laboratories around the world, robust and reliable characterization of these devices is now more urgent than ever. These diagnostics can take many forms, but one of the most popular categories is tomography, where an underlying parameterized model is proposed for a device and inferred by experiments. Here, we introduce and implement efficient operational tomography, which uses experimental observables as these model parameters. This addresses a problem of ambiguity in representation that arises in current tomographic approaches (the gauge problem). Solving the gauge problem enables us to efficiently implement operational tomography in a Bayesian framework computationally, and hence gives us a natural way to include prior information and discuss uncertainty in fit parameters. We demonstrate this new tomography in a variety of different experimentally-relevant scenarios, including standard process tomography, Ramsey interferometry, randomized benchmarking, and gate set tomography.

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Probing quantum processor performance with pyGSTi

Cited by 80 publications

References 86 publications

Demonstration of non-Markovian process characterisation and control on a quantum processor

Demonstration of non-Markovian process characterisation and control on a quantum processor

Detecting and tracking drift in quantum information processors

Operational, gauge-free quantum tomography

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