Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session M33: Quantum Characterization, Verification, and Validation IIILive
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Sponsoring Units: DQI Chair: Megan Lilly, University of Tennessee |
Wednesday, March 17, 2021 11:30AM - 11:42AM Live |
M33.00001: Pauli gate error amplification for sophisticated quantum gate calibration Heya Kentaro, Naoki Kanazawa Improving quantum gate fidelity is one of the most important challenges for the development of quantum computers. Sophisticated calibration technique can eliminate coherent errors due to imperfection of quantum gates. To calibrate parameters of underlying pulses, we need to evaluate an error from the target unitary and optimize the parameters to minimize the error. To further improve the gate fidelity, we need to investigate smaller error terms with higher accuracy. Hamiltonian error amplifying tomography is one of techniques to investigate such smaller errors with the aid of echo sequence that selectively amplifies error terms of interest. However, the target quantum gate is restricted to specific form. In this work we demonstrate a novel echo sequence generation technique that enables us to amplify any small Pauli error term in any target Pauli gate. The presented technique was implemented using open-source software toolkits in Qiskit and we experimentally investigated small interaction terms in the cross-resonance gate of IBM Quantum devices. |
Wednesday, March 17, 2021 11:42AM - 11:54AM Live |
M33.00002: Characterizing Control of Superconducting Qutrits by Randomized Benchmarking Michal Kononenko, Muhammet Ali Yurtalan, Jiahao Shi, Sahel Shafiq Ashhab, Adrian Lupascu We present experimental results for control of a qutrit implemented in the lowest three levels of a capacitively-shunted flux-biased superconducting circuit. Using randomized benchmarking over the qutrit Clifford group, we measure an average fidelity of 99.0 +/- 0.2% over the elements of the qutrit Clifford group. For a subset of this group, we characterize the fidelity using quantum process tomography and by observing the behaviour of repeated gate sequences. The gate decomposition used in this experiment can generate any unitary gate, given resonant control of two-level subspaces in the qutrit. Analysis of the results shows that errors are dominated by driving-induced level shifts, with leakage and decoherence playing a role as well. This work demonstrates a benchmark for high-fidelity control of qutrits, and outlines interesting avenues for future work on controlling single and multiple superconducting qudits. |
Wednesday, March 17, 2021 11:54AM - 12:06PM Live |
M33.00003: High speed calibration and characterization of superconducting qubits without qubit reset Max Werninghaus, Daniel Egger, Stefan Filipp To Characterize and calibrate quantum processing devices a large amount of measurement data has to be collected. Active qubit reset increases the speed at which data can be gathered but requires additional hardware and/or calibration. |
Wednesday, March 17, 2021 12:06PM - 12:18PM Live |
M33.00004: Characterizing mid-circuit measurements with a new form of gate set tomography part 1: Theory Kenneth Rudinger, Timothy Proctor, Erik Nielsen, Guilhem Ribeill, Matthew Ware, Luke Govia, Thomas A Ohki, Kevin Young, Robin Blume-Kohout While quantum circuits end with measurements, they can also include measurements in the middle. Such mid-circuit measurements are required for real-time feedforward control applications, such as quantum error correction. Understanding error processes in these mid-circuit measurements will be critical for building next-generation quantum processors. To that end, we extend gate set tomography (GST), a highly accurate and self-consistent protocol for diagnosing quantum gate errors, to be able to also characterize mid-circuit measurements. We will describe this extension and demonstrate its success in simulations. |
Wednesday, March 17, 2021 12:18PM - 12:30PM Live |
M33.00005: Characterizing mid-circuit measurements with a new form of gate set tomography part 2: Experiment Guilhem Ribeill, Matthew Ware, Luke Govia, Kenneth Rudinger, Timothy Proctor, Thomas A Ohki Quantum computers rely on classical electronics for qubit control and readout. An important capability for the implementation of complex algorithms such as quantum error correction is the ability to operate the classical hardware in a feedback loop with the quantum processor. Mid-circuit measurements are the key operation in this type of control scheme, and their efficient and precise characterization will be critical to understanding the performance of algorithms on near-term quantum devices. To that end, we demonstrate the use of an extension to gate set tomography (GST), a highly accurate protocol for diagnosing quantum processes, to characterize intermediate measurements on a superconducting transmon qubit. |
Wednesday, March 17, 2021 12:30PM - 12:42PM Live |
M33.00006: Demonstration of non-Markovian process characterisation and control on a quantum processor Gregory White, Charles Hill, Felix Alexander Pollock, Lloyd C. L. Hollenberg, Kavan Modi As experimentally available quantum devices increase in precision and accessibility, attention is turning to understanding and eliminating correlated -- or non-Markovian -- noise. Here, we develop and experimentally test a generalisation of quantum process tomography specifically for this problem. Based on the process tensor, this framework is designed to characterise non-Markovian dynamics in quantum systems by measuring the system’s response to a basis of control operations. The method was demonstrated on an array IBM Quantum devices where the effects of any control operation in the span were discernible to an infidelity of 10-3. We discuss the subtleties and best practices for the approach, and also demonstrate several applications: a statistically significant lower-bound on memory size for quantum devices; high fidelity dynamical characterisation in situations where conventional Markov models appreciably break down; and finally, demonstrate how the characterisation can be used for optimal local control of a system and its non-Markovian bath. 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. |
Wednesday, March 17, 2021 12:42PM - 12:54PM Live |
M33.00007: Quantum-tailored machine-learning characterization of quantum processors Elie Genois, Agustin Di Paolo, Noah Stevenson, Gerwin Koolstra, Akel Hashim, Irfan Siddiqi, Alexandre Blais, Jonathan Gross Standard methods for characterizing quantum devices require resources that grow exponentially with system size. As an alternative to these unscalable techniques, one can use readily available knowledge about the device in order to acquire information about a quantum state more efficiently. Here, we introduce a machine-learning architecture for inferring and interpreting the dynamics of a quantum device from time-series measurement data. Our recurrent architecture leverages quantum-mechanical structure in its design to interpret limited measurement data from quantum devices more accurately. We treat both numerically generated and experimental data for the case of a driven superconducting transmon qubit probed by a weak microwave tone implementing heterodyne measurement. This work shows how a physics-inspired machine-learning model can improve the learning of device-specific quantum dynamics and parameters. These can then be used to characterize and calibrate superconducting quantum processors. |
Wednesday, March 17, 2021 12:54PM - 1:06PM Live |
M33.00008: Two-qubit noise cross-correlation spectroscopy of electronic spins in diamond Won Kyu Calvin Sun, Paola Cappellaro Every quantum device is subject to noise due to entangling with its environment, which limits the lifetime and thus usefulness of the device. Characterizing the noise affecting quantum devices is thus of both practical interest (e.g., to find low-noise operating conditions) and fundamental interest (e.g., to probe the physical nature of the quantum environment). An established method based on dynamical decoupling control sequences has achieved single-qubit noise spectroscopy (1QNS) in many dephasing quantum systems. However, for a multi-qubit information processor, 1QNS alone is not sufficient to characterize the overall device noise, as it fails to capture the spatio-temporal correlations shared across the noise on each qubit. As the first step to address this, the theory of two-qubit noise spectroscopy (2QNS) was proposed to characterize such cross-correlations in the dephasing noise across spatially separated qubits. Here we demonstrate initial measurements of 1QNS and 2QNS of two weakly interacting electronic spins in diamond, which can yield a more complete understanding of the overall noise affecting the two-qubit register, and in turn inform practical considerations for improved operating conditions for the device. |
Wednesday, March 17, 2021 1:06PM - 1:18PM Live |
M33.00009: Model-based Qubit Noise Spectroscopy Christopher Watson, Kevin Schultz, Andrew J Murphy, Gregory Quiroz, Timothy M Sweeney Model-based approaches to classical spectral density estimation have a number of potential benefits over nonparametric approaches, including reduced estimation error and super-resolution, the ability to resolve spectra below the nominal frequency resolution. These benefits are achieved by exploiting assumptions about the spectral content, such as a particular parametric form or the number of signal components. Classically, model-based techniques are well studied, but they have not yet been employed in qubit noise spectroscopy (QNS). In this work, we show how such approaches can be adapted to standard gate-based QNS procedures; moreover, we demonstrate that the recently introduced Schrödinger Wave Autoregressive Moving Average (SchWARMA) models can be used in a spectrum estimation technique that can resolve an injected noise tone to numerical precision in frequency. These results continue to expand the role of classical statistical signal processing in advancing quantum characterization, verification, and validation, and furthermore will have applications in quantum sensing of classical signals from radar to magnetic resonance imaging. |
Wednesday, March 17, 2021 1:18PM - 1:30PM Live |
M33.00010: Random Pulse Sequences for Qubit Noise Spectroscopy Kaixin Huang, Seyed Alireza Seif Tabrizi, Mohammad Hafezi, Yi-Kai Liu Qubit noise spectroscopy is an important tool in experimental investigations of quantum information processors and open quantum systems. However, conventional noise spectroscopy techniques are time-consuming, because they require repeated measurements of the noise spectral density S(w) at different frequencies w. Here we describe an alternative method for quickly and approximately characterizing S(w). This method uses random pulse sequences, with carefully-controlled correlations among the pulses, in order to measure arbitrary linear functionals of S(w). This method has several applications, such as efficient characterization of noise spectra that consist of a few isolated peaks (via compressed sensing), parametric estimation of Lorentzian noise spectra, and learning effective descriptions of the environment (such as 1-D chain representations). |
Wednesday, March 17, 2021 1:30PM - 1:42PM Live |
M33.00011: Constructing U(1) gauge symmetry in electronic circuits Hannes Riechert, Landry Bretheau, Fred Jendrzejewski Classical electronic circuits have proven powerful to study several topological lattice structures (Ningyuan et al., PRX 5, 2015; Imhof et al., Nat. Phys. 14, 2019). Here, we present an electronic circuit described by a lattice Hamiltonian with local U(1) symmetry and explore the extent to which a classical physical simulator in the form of an electronic circuit might be useful as a stepping stone for lattice gauge theories like SU(2). |
Wednesday, March 17, 2021 1:42PM - 1:54PM Live |
M33.00012: Maximal entropy approach for quantum state tomography Rishabh Gupta, Rongxin Xia, Raphael Levine, Sabre Kais Quantum computation has been growing rapidly in both theory and experiments. The current quantum computing devices are noisy intermediate-scale quantum (NISQ) devices, and so approaches to validate quantum processing on these quantum devices are needed. One of the most common ways of validation for an n-qubit quantum system is quantum tomography, which tries to reconstruct a quantum system's density matrix by a complete set of observables. However, the inherent noise in the quantum systems and the intrinsic limitations poses a critical challenge to precisely know the actual measurement operators which make quantum tomography impractical in experiments. Here, we propose an alternative approach to quantum tomography, based on the maximal information entropy, that can predict the values of unknown observables based on the available mean measurement data. This can then be used to reconstruct the density matrix with high fidelity even though the results for some observables are missing. Of additional contexts, a practical approach to the inference of the quantum mechanical state using only partial information is also needed. |
Wednesday, March 17, 2021 1:54PM - 2:06PM Live |
M33.00013: Robust phase estimation for two-qubit gates Benjamin Marinoff, Joshua Combes, Nicholas Rubin, Kyle Gulshen Fast and accurate gate calibration is necessary for quantum computing. In this talk we present protocols that allow you to robustly tune up arbitrary two qubit gates with high accuracy. We generalize iterative phase and Hamiltonian estimation procedures developed for single qubits to two qubit gates. The resulting protocol has Heisenberg limited scaling when estimating any of the gate parameters, and inherits the robustness guarantees of RPE on single qubit gates. |
Wednesday, March 17, 2021 2:06PM - 2:18PM Live |
M33.00014: Tracking Non-Markovian Quantum Trajectories of a Superconducting Qubit from a Finite-Memory Bath Noah Stevenson, Gerwin Koolstra, Karthik Siva, Ravi K. Naik, William Livingston, Shiva Lotfallahzadeh Barzili, Justin G. Dressel, Irfan Siddiqi High-fidelity quantum control of superconducting qubits requires determining the time-dependent Hamiltonian of the applied control and environmental coupling. While continuous state tracking has proven effective for determining Markovian qubit time-evolution, coupling to non-computational subspaces or a finite-memory bath can result in non-Markovian dynamics. We use quantum state tracking with continuous weak measurement to experimentally investigate non-Markovianity in a transmon qubit strongly coupled to an unmonitored bath qubit via a cross-resonance interaction. We measure state decay and revival, a signature of non-Markovianity, and train a recurrent neural network to reconstruct quantum trajectories for dynamics that are difficult to describe with single-qubit trajectory theory. |
Wednesday, March 17, 2021 2:18PM - 2:30PM Live |
M33.00015: Reconstructing Transmon State Trajectories Outside the Bad-Cavity Regime using a Neural Network Filter Shiva Lotfallahzadeh Barzili, Gerwin Koolstra, Noah Stevenson, Sacha Greenfield, Lucas Burns, Karthik Siva, William Livingston, Akel Hashim, Irfan Siddiqi, Justin G. Dressel Superconducting transmon qubits are measured by coupling them to a resonator and monitoring the leaked microwave field continuously in time. In the bad-cavity regime, we can treat the resonator as a steady-state bath that produces Markovian quantum trajectories of the qubit. However, outside the bad-cavity regime, the entangled qubit-resonator state can nontrivially evolve in time. The resulting time-varying measurement strength significantly complicates theoretical models for tracking reduced qubit dynamics, which can become non-Markovian due to information back-flow from the resonator. Modern quantum processors regularly operate outside the bad-cavity regime, motivating the need for more sophisticated state tracking that handles these time-dependent effects. In this work, we experimentally investigate driven qubit dynamics outside the steady-state and bad-cavity regime and demonstrate that a recurrent neural network (RNN) accurately reconstruct the reduced qubit-state trajectories with time-dependent parameters. We reconstruct and verify trajectories involving Rabi frequencies comparable to the resonator linewidth, and find that the RNN correctly identifies drive-hybridization corrections to the measurement axis and strength. |
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