Bulletin of the American Physical Society
APS March Meeting 2023
Las Vegas, Nevada (March 510)
Virtual (March 2022); Time Zone: Pacific Time
Session D72: Techniques for Learning Noise in Quantum SystemsFocus

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Sponsoring Units: DQI Chair: Daniel Hothem, Sandia National Laboratories Room: Room 406 
Monday, March 6, 2023 3:00PM  3:12PM 
D72.00001: Random Pulse Sequences for Noise Spectroscopy kaixin Huang, Demitry Farfurnik, Alireza Seif, Mohammad Hafezi, YiKai Liu Qubit noise spectroscopy is an important tool for experimental investigations of open quantum systems. However, conventional noise spectroscopy techniques are timeconsuming, because they require repeated measurements of the noise spectral density S(ω) at different frequencies ω. Here we describe an alternative method for quickly and approximately characterizing S(ω). This method uses random pulse sequences, with carefullycontrolled correlations among the pulses, to measure arbitrary linear functions of S(ω). This method has many applications. We can do parametric estimation of noise spectra and learning an effective description of the environment, such as the 1D chain representation. We also develop several compressed sensing protocals to characterizing noise spectra that consist of a few isolated peaks. We illustrate these applications by simulations on quantum dots and superconducting qubits. 
Monday, March 6, 2023 3:12PM  3:24PM 
D72.00002: Estimating quantum gate fidelities in context Dripto M Debroy, Jonathan A Gross, Elie Genois, Zhang Jiang, Vadim Smelyanskiy It is often desireable to characterize quantum gates in the context in which they are being used, both in space (by applying simultaneous operations on surrounding qubits), and in time (e.g. preceding operations on the same qubits). In this talk we will present a flexible framework for estimating the average gate fidelity of a quantum operation in any particular context of interest. We will discuss two extensions to this simple idea, one that allows us to budget the coherent and incoherent errors, and one that allows us to efficiently characterize the individual operations in a larger Clifford circuit, such as a surfacecode stabilizer circuit. 
Monday, March 6, 2023 3:24PM  3:36PM 
D72.00003: Precision gate characterization in the presence of lowfrequency noise on a superconducting processor Jonathan A Gross, Dripto M Debroy, ZePei Cian, Elie Genois, Zhang Jiang Coherent amplification, the primary tool for precision quantum estimation, remains elusive for many tasks because the necessary coherence is scrambled by noise processes. One such noise process in superconductingqubit platforms is flux noise, which causes slow fluctuations of the qubit frequency. In addition to making the qubit frequency itself ill defined for estimation purposes, these fluctuations mask deviations in other welldefined parameters in the system. We present a characterization technique that allows us to precisely target select unitary parameters for controlledZ gates in the presence of such lowfrequency noise, obtaining up to 20 times precision improvement in these parameters. The simplicity and directness of this method makes it a robust and reliable tool for precision characterization of noisy quantum devices. 
Monday, March 6, 2023 3:36PM  3:48PM 
D72.00004: Resourceefficient framebased nonGaussian quantum noise spectroscopy and optimized control Wenzheng Dong, Gerardo Paz Silva, Lorenza Viola Many stateofthe art qubit devices are exposed to noise that is temporally correlated (“nonMarkovian”) and nonGaussian. Characterizing such noise in a way that is accurate and useful for predicting arbitrary controlled dynamics is challenging given finite control resources. A recently proposed formalism [1] leverages the notion of a “frame” to generalize transfer filterfunction techniques beyond the frequency domain and overcome important limitations of existing approaches. Notably, by tying the choice of frame to the available control, a modelreduced representation of the opensystem dynamics is obtained, allowing efficient noise characterization under experimental constraints. Within this general framework, we show how to achieve digital noise spectroscopy of nonGaussian classical dephasing using a window frame. Focusing on random telegraph noise as a concrete example, we demonstrate how nonGaussian noise spectroscopy can provide highorder controladapted noise spectra for qubit dynamics prediction and noisetailored decoupling design. We find that, depending on the operating parameter regime, control that is optimized based on nonGaussian noise spectroscopy can substantially outperform standard Walsh decoupling sequences as well as control that is optimized based only on Gaussian noise spectroscopy. 
Monday, March 6, 2023 3:48PM  4:00PM 
D72.00005: Two Qubit Correlated Noise and Crosstalk Characterization Mayra Amezcua, Leigh M Norris, Tom Gilliss, James A Shackford, Timothy M Sweeney, Kevin Schultz Spatiotemporally correlated noise and crosstalk have a negative impact on error correcting schemes. We propose and validate a nonparametric quantum noise spectroscopy protocol to measure the spectra associated with spatiotemporally correlated dephasing noise and fluctuating crosstalk on two qubits. This method is also used to estimate the static components of the crosstalk and the dephasing noise processes acting on each qubit. Our scheme reconstructs the real and imaginary components of the twoqubit crossspectrum by using two fixed total time pulse sequences and performing single qubit and joint twoqubit measurements to separately resolve spatially correlated noise processes from the individual noise spectra of the participating qubits. We benchmark our protocol by reconstructing the spectra of engineered spatiotemporally correlated noise processes produced by Schrodinger Wave Autoregressive Moving Average, a technique developed by our group [1] to generate phase error on qubit control lines. Our results demonstrate the utility of our protocol in characterizing spatiotemporally correlated noise and crosstalk in a multiqubit device for potential use in noiseadapted control or error mitigation schemes. 
Monday, March 6, 2023 4:00PM  4:12PM 
D72.00006: Modeling LowFrequency Hamiltonian Noise Corey I Ostrove, Timothy J Proctor, Megan L Dahlhauser, Kevin Young, Robin J BlumeKohout The ability to precisely and completely characterize the behavior of asbuilt quantum computing hardware is essential to the development of nextgeneration quantum computers. Tomographic protocols, such as gate set tomography (GST), produce incredibly detailed descriptions of the noise in a system, but do so under the assumption that the noise is markovian. Nonmarkovian noise sources (e.g. leakage, crosstalk and temporal correlations) are common in real systems, and are pernicious in that we not only do not capture them with most existing characterization protocols, but they can in fact corrupt our estimates for the markovian noise in a device. 
Monday, March 6, 2023 4:12PM  4:24PM 
D72.00007: Full characterisation of quantum nonMarkovian processes: from tomography to noise detection. Christina Giarmatzi Every experimental realisation of a quantum process faces the possibility of noise coming from the environment. Although in small quantum devices, the noise is assumed to be uncorrelated (Markovian) this assumption fails as the size and complexity increase, and the various systemenvironment interactions become correlated (nonMarkovian). 
Monday, March 6, 2023 4:24PM  4:36PM 
D72.00008: Identifying OffResonant Errors on Fixed Frequency Transmons Seth T Merkel Quantum gate error rates have dropped steadily on IBM processors over the last years, making more sophisticated computations possible, but also increasing the challenge of characterizing the remaining errors. In particular, nonMarkovian errors have been thought likely to limit gates on IBM systems, but directly detecting these errors has been elusive, limiting our ability to mitigate them efficiently. We probe our system for offresonant excitations, a specific form of nonMarkovian coherent error, using continuous phase amplification. Since errors due to excitations of an offresonant transition are nonstationary they are hard to amplify and detect without using this technique. These errors exist in all microwave driven one and twoqubit gates on IBM devices and can bottleneck performance near collisions (e.g. driving the crossresonance interaction during single qubit gates or vice versa). Furthermore we can use this same methodology to access to the errors caused by TLS, showing evidence of coherent, offresonant interactions with subsystems that are not qubits. We explore these results and their impact on gate error for IBM deployed devices. 
Monday, March 6, 2023 4:36PM  4:48PM 
D72.00009: Noise Modeling of the IBM Quantum Experience Yasuo Oda, Omar Shehab, Gregory Quiroz The influence of noise in quantum dynamics is one of the main factors preventing Noisy IntermediateScale Quantum (NISQ) devices from performing useful quantum computations. Errors must be suppressed in order to achieve the desired levels of accuracy, which requires a thorough understanding of the nature and interplay of the different sources of noise. In this work, we propose an effective error model of singlequbit operations on the IBM Quantum Experience, that possesses considerable predictive power and takes into account spatiotemporally correlated noise. Additionally, we showcase how Quantum Noise Spectroscopy (QNS) can be used alongside other error characterization techniques, such as T1 experiments, to obtain a more complete error model of the system. We focus on a Hamiltonian description of the noise, with parameters obtained from a small set of characterization experiments. We show that simulations using this error model are capable of recovering the characterization experiments' results to a high degree of accuracy. We also successfully compare the simulations against test data consisting of experimental results of varying circuit lengths and types of implemented operations. Lastly, we characterize the fluctuation of the noise model parameters as functions of time, observing large variations in some devices. 
Monday, March 6, 2023 4:48PM  5:24PM 
D72.00010: Selfconsistent learning and control of arbitrary quantum correlated noise, and beyond Invited Speaker: Gregory A White The demands of fault tolerance mean that a wide variety of simple and exotic noise types must be tamed for quantum devices to progress. Realworld hurdles range from imperfect gates and decoherence, to spatiotemporal nonMarkovianity, crosstalk, correlated control errors, and undesirable driving of bath transitions. These effects must be treated, but how can we combine everything into a feasible and useful framework, and how can this then be learned? Fortunately, as devices improve, the presence of noise gets sparser and permits more sophisticated models for efficient and general characterisation. Combining recent advances in tensor network learning with nonMarkovian process characterisation, we develop efficient techniques to capture a nearuniversal model for quantum noise. The three fundamental categories we consider are: correlated background dynamics, correlated control imperfections, and controlenvironment interactions. Our approach to characterise these components employs no assumptions about prior calibrations, and can accommodate large numbers of timesteps and qubits from relatively few experiments. The result is a practical, scalable, and consistent procedure capable of describing arbitrary open dynamics and experimental controls. We stress that the framework is hardware agnostic, but the models are readily modular and can be adapted based on the expected physics of the device or intended purpose of the characterisation. The resulting estimate not only spells out errors, but also serves as a reliable mapping from all experimenterchosen parameters to circuit outcomes. Hence, this has ready accessibility and applicability to all applied quantum information processing. We bolster these claims with an extensive set of numerical and experimental results. In particular, we demonstrate powerful applications of our method, including bespoke dynamical decoupling sequences, pulse shaping, and optimal logical control choices for any twoqubit gate decomposition. We anticipate this approach to be useful at informing all levels of the quantum control stack: in error suppression, error mitigation, and error correction. 
Monday, March 6, 2023 5:24PM  5:36PM 
D72.00011: Probing logical error models with gate set tomography Kenneth M Rudinger, Jalan Ziyad, Mario MorfordOberst, Julie Campos, Stefan Seritan, Tzvetan S Metodi, Robin BlumeKohout Qubit technologies are becoming sufficiently advanced to support the implementation of error corrected logical qubits. These logical qubits still experience errors, just like their constituent physical qubits, but they may not experience all the same kinds of errors observed in physical qubits — e.g. coherent errors. It has been widely conjectured that logical qubit error processes will be well approximated by Paulistochastic channels, regardless of what errors afflict their component physical qubits. Testing this conjecture requires precise, reliable probes of logical error processes. We demonstrate that logical gate set tomography (GST) can probe the dynamics of a numerically simulated logical qubit precisely enough to test the nature of its error process. We use GST to fit nested error models for logical qubit behavior, and apply rigorous statistical tests to determine whether any Paulistochastic logical error process captures the logical qubit’s dynamics. We demonstrate regimes where this conjecture holds, and we examine regimes where this conjecture may be less wellfounded.

Monday, March 6, 2023 5:36PM  5:48PM 
D72.00012: ModelBased Qubit Noise Spectroscopy Kevin Schultz, Christopher Watson, Andrew J Murphy, Timothy M Sweeney, Gregory Quiroz Qubit noise spectroscopy (QNS) is a valuable tool for both the characterization of a qubit's environment and as a precursor to more effective qubit control to improve qubit fidelities. Existing approaches to QNS are what the classical spectrum estimation literature would call "nonparametric" approaches, in that a series of probe sequences are used to estimate noise power at a set of points or bands. In contrast, modelbased approaches to spectrum estimation assume additional structure of the spectrum and leverage this for improved statistical accuracy or other capabilities, such as superresolution. Here, we derive modelbased QNS approaches using inspiration from classical signal processing, primarily though the recently developed Schrodinger wave autoregressive movingaverage (SchWARMA) approach for correlated noise models. We show, using simulation and experimental data, how these modelbased QNS approaches maintain the statistical and computational benefits of their classical counterparts, resulting in powerful new estimation approaches. Beyond the direct application of these approaches to QNS, we anticipate that the underlying models will find utility in adaptive feedback control for quantum systems, in analogy with their role in classical adaptive estimation and control. 
Monday, March 6, 2023 5:48PM  6:00PM 
D72.00013: Experimental MultiTime Process Tomography on Superconducting Qubits Tyler Jones, Christina Giarmatzi, Alexei Gilchrist, Fabio Costa, Arkady Fedorov The maturation of quantum technologies in the past decade has been enabled, in no small part, by the ongoing advancement of noise characterisation techniques. For Markovian noise sources, quantum process tomography or QPT (a natural extension of quantum state tomography) is one such technique which has become ubiquitous in the experimental toolbox. However, techniques like QPT have no capacity to characterise sources of nonMarkovian noise (e.g. memory effects, system drifts and crosstalk), which we know exist nontrivially in the vast majority of experimental hardware. In this talk, we introduce a methodology to perform multitime quantum process tomography, which uses a process matrix formalism to allow for full characterisation of nonMarkovian dynamics. We discuss the experimental procedure required to perform multitime process tomography on a superconducting quantum device, and present results where we successfully detect quantum memory effects on an inhouse superconducting device and a publicly available IBM processor. 
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