Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session R68: Control and Calibration Tools for Scalable Quantum ComputingFocus

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Sponsoring Units: DQI Chair: Robin BlumeKohout, Sandia National Laboratories Room: Four Seasons 4 
Thursday, March 5, 2020 8:00AM  8:12AM 
R68.00001: Estimation of statistical significance in the quantum supremacy experiment with the Sycamore processor Ping Yeh, Adam Zalcman, Sergio Boixo Google's quantum supremacy experiment is based on sampling of output bitstrings of random quantum circuits [1]. To demonstrate quantum supremacy, it is critical to establish that the hardware fidelity at sampling time is not degraded to zero due to errors and the sampled bitstrings correspond to the expected noisy distribution. In addition, since the run time of classical approximate simulations is proportional to fidelity, it is important to verify that the fidelity is above a threshold at which classical simulation is estimated to be hard. In this talk, I will describe the methodologies for the statistical analyses and show that the bitstring distributions are extremely unlikely to be explained by noise alone and the fidelity is significantly above the threshold. 
Thursday, March 5, 2020 8:12AM  8:24AM 
R68.00002: How OpenSuperQ is planning to fully calibrate and characterize a 100 qubit superconducting QPU over the weekend Shai Machnes, Nicolas Wittler, federico Roy, Anurag Saha Roy, Kevin Pack, Frank Wilhelm The current methodology of designing control pulses for superconducting circuits often results in an absurd situation: simplified analytic models which do not predict gate fidelities to high accuracy, and calibrated pulse shapes which achieve good fidelities, but do not correspond to the model. For large number of qubits the QPU calibration is long and difficult, and determining a detailed error budget is nearly impossible. 
Thursday, March 5, 2020 8:24AM  8:36AM 
R68.00003: Advanced Control Calibration for NISQ QPUs and Quantum Devices  Theory and Experiments Kevin Pack, Shai Machnes, Nicolas Wittler, federico Roy, Anurag Saha Roy, Frank Wilhelm As the era of NISQ is dawning, calibration and characterization of QPUs is becoming an increasingly complex task due to the growing amount of qubits and high fidelity requirements. To tackle this problem, we developed a machine learning driven approach for Combined Calibration and Characterization procedure, C^3. This talk focuses on the gate calibration task, where optimization algorithms are used to find the best pulse parameter values in a multidimensional space. 
Thursday, March 5, 2020 8:36AM  8:48AM 
R68.00004: Automatic Calibration and Characterization of Quantum Devices  Experimental Results on NISQ QPUs and Quantum Memory devices Shai Machnes, Nicolas Wittler, federico Roy, Anurag Saha Roy, Kevin Pack, Frank Wilhelm The practical application of optimal control techniques, especially in superconducting settings, requires extensive calibration to reach high fidelities. By controlling both experiment and a high performance numerical simulation, the C3 procedure provides a framework to systematically design and apply even intricate open loop optimal control pulses. 
Thursday, March 5, 2020 8:48AM  9:00AM 
R68.00005: Active Learning of Hamiltonians Arkopal Dutt, Edwin Pednault, Chai Wu, Sarah Sheldon, Lev S Bishop, John Smolin, Isaac Chuang Querying a quantum system produces measurement outcomes which originate from the Hamiltonian dynamics of the system and its environment. Learning a Hamiltonian from a class that best fits these observations is the Hamiltonian tomography problem. Prior work has focused on estimation and offline optimal experiment design. Here, we consider an active learner that is given an initial set of training examples and the ability to interactively query the quantum system to generate new training data. The goal is to then minimize the training data required for Hamiltonian learning. To this end, we present an efficient active learning algorithm based on Fisher information and assess its performance on recalibrating superconducting qubit systems based on the crossresonance (CR) gate. The CR Hamiltonian has up to nine parameters and admits queries involving input state, measurement operator and interaction time. Practical challenges include exponential growth of number of parameters with number of qubits, and modeling different noise sources such as readout, and imperfect pulse shapes. We show that we can achieve a constant 30% reduction in queries compared to a uniformly random approach. We also describe a regime where we achieve Heisenberg limited convergence rate during learning. 
Thursday, March 5, 2020 9:00AM  9:12AM 
R68.00006: Optimizing Quantum Gate Frequencies for Google’s Quantum Processors Paul Klimov, Julian Kelly, Kevin Satzinger, Zijun Chen, Hartmut Neven, John M Martinis A crucial component of operating a quantum processor is mitigating computational errors from energyrelaxation, dephasing, leakage, and control imperfections. In superconducting qubits, these sources of error can arise from controlelectronics noise, controlpulse distortions, and the parasitic coupling of qubits to other qubits, twolevel system defects, spurious microwave modes, and the control and readout circuitry. In frequencytunable qubit architectures, it is possible to mitigate these sources of error by choreographing qubit gate frequencies over the course of quantum algorithms. This choreography maps to constructing and optimizing a highdimensional, highconstraint, nonconvex, and timedependent objective over a search space that significantly exceeds the Hilbertspace dimension of the processor. In this talk, I will introduce the frequency optimization problem and the Snake optimizer that we developed to solve it for Google’s flagship quantum processors [1]. 
Thursday, March 5, 2020 9:12AM  9:24AM 
R68.00007: Quantum Orchestration Platform
Integrated hardware and software for design and execution of complex quantum control protocols Yonatan Cohen, Itamar Sivan, Nissim Ofek, Lior Ella, Niv Drucker, Tal Shani, Ori Weber, Hanan Grinberg, Michael Greenbaum The incredible progress in designing quantum systems, engineering their environment, and controlling them effectively, has led to significant improvements in coherence times, gate fidelities and the ability to integrate more qubits into a single quantum processor. While development of quantum processors remains the number one challenge, many bottlenecks exist in the classical control hardware layer as well as the software layer, where optimizations can play a critical role for near term quantum computing. Some examples include (1) feedback for error correction and repeat until success protocols, (2) complex calibrations, and (3) hybrid quantum classical algorithms. 
Thursday, March 5, 2020 9:24AM  9:36AM 
R68.00008: Quantum Orchestration Platform
Integrated hardware and software for design and execution of complex quantum control protocols Itamar Sivan, Yonatan Cohen, Nissim Ofek, Tal Shani, Ori Weber, Lior Ella, Michael Greenbaum, Hanan Grinberg, Niv Drucker Quantum computing holds a great promise for immense computational power. In last years, it was suggested that noisy intermediatescale quantum (NISQ) processors may already demonstrate an advantage over classical hardware for particular computational tasks which may be of commercial use. Nonetheless, significant additional technological progress, at every layer of the quantum computer, is required in order to advance from NISQ processors to ones capable of running faulttolerantquantumcomputation (FTCC). In particular, for FTCC, multiqubit realtime feedback capabilities have to be developed far beyond those we know today. 
Thursday, March 5, 2020 9:36AM  9:48AM 
R68.00009: HighFidelity, Scalable QuantumClassical Control Interface using Photonics Jacky Chan, Apurva S. Gowda, Peter T. S. DeVore, Brandon W. Buckley, Jonathan L DuBois, Jason Chou Quantum computing (QC) has been hailed as the next big leap for the digital age; however, stateoftheart QC devices have yet to surpass classical computers. One bottleneck is in the number of quantum operations that can be done within a qubit lifetime, also known as the "circuit depth". The circuit depth on current hardware is limited to 10100 operations due to uncontrolled coupling to the classical environment, infidelities in the qubit operations and the gate operation time. 
Thursday, March 5, 2020 9:48AM  10:00AM 
R68.00010: Automatic single qubit characterization with QubiC Yilun Xu, Gang Huang, Ravi Kaushik Naik, Bradley Mitchell, David Santiago, Irfan Siddiqi Initial setup a physical qubit is a complex procedure which involve multiple instruments and multiple experiments and optimization. After the initial setup, they still need to be carefully calibrated routinely because the qubit is sensitive to the environment and the qubit itself can have slow drift so as to impact the gate fidelity. 
Thursday, March 5, 2020 10:00AM  10:12AM 
R68.00011: Optimal Control of Superconducting Qubits Max Werninghaus, Daniel Egger, federico Roy, Shai Machnes, Frank Wilhelm, Stefan Filipp Fast and accurate twoqubit gates are a key requirement to perform complex algorithms on current quantum computers. Ideally, the duration of the gate should be much shorter than the coherence time of the system. However, shorter gates can result in unwanted loss of states from the computational subspace. Optimal control theory aims to design fast control pulses suppressing such side effects of the driving field. Even with an accurately calibrated system model, control pulses require a tuneup to accommodate for parameterdrifts and modelinaccuracies. Here we present our work on optimal control algorithms, using a closed loop approach with direct experimental feedback to design complex pulses. This approach avoids errors from an inaccurate initial system model and uses information gained during the pulse optimization to update the model. We improve the interplay of control instruments and multidimensional optimization algorithms to speed up the tuneup of feedbackloops, reducing evaluation times from several minutes to a few seconds . With these measures, we achieve a reduction of gate errors by more than a factor of five for short pulses. 
Thursday, March 5, 2020 10:12AM  10:48AM 
R68.00012: Quantum Supremacy: computational complexity and applications Invited Speaker: Pedram Roushan

Thursday, March 5, 2020 10:48AM  11:00AM 
R68.00013: Calibration of flux crosstalk in flux qubit based quantum annealers with persistent current readout devices Xi Dai, Antonio Javier Martinez, Daniel M Tennant, Denis Melanson, Ali Yurtalan, Salil Bedkihal, Yongchao Tang, Alexander Melville, Bethany Niedzielski, Rabindra Das, David K Kim, Jonilyn Yoder, Steven Weber, Andrew James Kerman, Sergey Novikov, Steven M Disseler, James I Basham, Jeffrey Grover, Evgeny Mozgunov, Daniel A Lidar, Adrian Lupascu Quantum annealing with superconducting flux qubits requires reliable control of externally applied magnetic fluxes. We present results on the calibration of flux crosstalk in a device consisting of two flux qubits coupled by a chain of seven couplers. The device has 27 superconducting loops and 27 bias lines. The method relies on the use of flux detectors formed of RFSQUID terminated waveguide resonators, attached to each qubit and coupler. An iterative version of the method is demonstrated, which is more tolerant to errors arising from persistent currents in the complete circuits. The ability to perform reliable crosstalk calibration and compensation allows maintaining high coherence qubit designs and eliminates overhead in designs that are robust against crosstalk. We discuss future extensions of this work, relevant for large scale annealing control. 
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