Lindblad Tomography of a Superconducting Quantum Processor

Samach, Gabriel; Greene, Amy; Borregaard, Johannes; Christandl, Matthias; Kim, David K.; McNally, Christopher M.; Melville, Alexander; Niedzielski, Bethany; Sung, Youngkyu; Rosenberg, Danna; Schwartz, Maurice L.; Yoder, Jonilyn; Orlando, Terry P.; Wang, Joel I-Jan; Gustavsson, Simon; Kjærgaard, Morten; Oliver, William

doi:10.1103/physrevapplied.18.064056

Cited by 11 publications

(4 citation statements)

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“…As an improvement, gate set tomography [13], [15]- [17] takes into account of the actual SPAM noise to better characterize the noise mechanism of the gate set. The recently proposed Lindblad tomography (LT) [18] also employs the SPAM error and several assumptions (Markov channel, constant SPAM error and perfect single qubit gate) and estimates the Hamiltonian and Lindbladian operator. Ref.…”

Section: B Gate Set Tomography (Gst)mentioning

confidence: 99%

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Calibration and Performance Evaluation of a Superconducting Quantum Processor in an HPC Center

Deng,

Pogorzalek,

Vigneau

et al. 2024

ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

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As quantum computers mature, they migrate from laboratory environments to HPC centers. This movement enables large-scale deployments, greater access to the technology, and deep integration into HPC in the form of quantum acceleration. In laboratory environments, specialists directly control the systems' environments and operations at any time with handson access, while HPC centers require remote and autonomous operations with minimal physical contact. The requirement for automation of the calibration process needed by all current quantum systems relies on maximizing their coherence times and fidelities and, with that, their best performance. It is, therefore, of great significance to establish a standardized and automatic calibration process alongside unified evaluation standards for quantum computing performance to evaluate the success of the calibration and operation of the system. In this work, we characterize our in-house superconducting quantum computer, establish an automatic calibration process, and evaluate its performance through quantum volume and an application-specific algorithm. We also analyze readout errors and improve the readout fidelity, leaning on error mitigation.

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Section: B Gate Set Tomography (Gst)mentioning

confidence: 99%

“…Ref. [18] demonstrated that crosstalk resources in a quantum superconducting processor can be identified through LT.…”

Section: B Gate Set Tomography (Gst)mentioning

confidence: 99%

Calibration and Performance Evaluation of a Superconducting Quantum Processor in an HPC Center

Deng,

Pogorzalek,

Vigneau

et al. 2024

ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

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“…While these are significant contributions to the research and development of NISQ devices, there is not sufficient explanation for the deviation from a Markov model to describe the qubit dynamics. This creates a void in current research which this paper is intended to address, following the inspiration of Lindblad tomography by Samach et al 23 , by applying Markovian models to benchmarking algorithms in modern NISQ devices to evaluate the various claims about their dynamics.…”

Section: Introductionmentioning

confidence: 99%

Markovian noise modelling and parameter extraction framework for quantum devices

Brand,

Sinayskiy,

Petruccione

2024

Sci Rep

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In recent years, Noisy Intermediate Scale Quantum (NISQ) computers have been widely used as a test bed for quantum dynamics. This work provides a new hardware-agnostic framework for modelling the Markovian noise and dynamics of quantum systems in benchmark procedures used to evaluate device performance. As an accessible example, the application and performance of this framework is demonstrated on IBM Quantum computers. This framework serves to extract multiple calibration parameters simultaneously through a simplified process which is more reliable than previously studied calibration experiments and tomographic procedures. Additionally, this method allows for real-time calibration of several hardware parameters of a quantum computer within a comprehensive procedure, providing quantitative insight into the performance of each device to be accounted for in future quantum circuits. The framework proposed here has the additional benefit of highlighting the consistency among qubit pairs when extracting parameters, which leads to a less computationally expensive calibration process than evaluating the entire device at once.

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“…While in NMR, the dominant noise process is decoherence, in tunable quantum simulators such as superconducting qubits, trapped ions or cold atoms in optical lattices, state preparation and measurement (SPAM) errors, as we also demonstrate here, play a central role. Initial steps at characterizing such errors as well as the dissipative Lindblad dynamics for up to two qubits in a superconducting qubit platform have been taken recently [49,50]. Hamiltonian learning of thermal states has recently also been applied in many-body experiments as a means to characterize the entanglement of up to 20-qubit subsystems whose reduced states are parameterized by the so-called entanglement Hamiltonian [51][52][53].…”

mentioning

confidence: 99%

Robustly learning the Hamiltonian dynamics of a superconducting quantum processor

Eisert,

Hangleiter,

Roth

et al. 2024

Preprint

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The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. The key to solving these issues are highly precise ways of characterizing analog quantum simulators. Here, we robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator from measured time-series of single-mode canonical coordinates. We achieve the required levels of precision in estimating the Hamiltonian parameters by maximally exploiting the model structure, making it robust against noise and state-preparation and measurement (SPAM) errors. Importantly, we are also able to obtain tomographic information about those SPAM errors from the same data, crucial for the experimental applicability of Hamiltonian learning in dynamical quantum-quench experiments. Our learning algorithm is highly scalable both in terms of the required amounts of data and post-processing. To achieve this, we develop a new super-resolution technique coined tensorESPRIT for frequency extraction from matrix time-series. The algorithm then combines tensorESPRIT with constrained manifold optimization for the eigenspace reconstruction with pre- and post-processing stages. For up to 14 coupled superconducting qubits on two Sycamore processors, we identify the Hamiltonian parameters---verifying the implementation on one of them up to sub-MHz precision---and construct a spatial implementation error map for a grid of 27 qubits. Our results constitute a fully characterized, highly accurate implementation of an analog dynamical quantum simulation and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.

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Lindblad Tomography of a Superconducting Quantum Processor

Cited by 11 publications

References 50 publications

Calibration and Performance Evaluation of a Superconducting Quantum Processor in an HPC Center

Calibration and Performance Evaluation of a Superconducting Quantum Processor in an HPC Center

Markovian noise modelling and parameter extraction framework for quantum devices

Robustly learning the Hamiltonian dynamics of a superconducting quantum processor

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