Bulletin of the American Physical Society
APS March Meeting 2018
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session L47: Optimizing the Dynamics of Quantum Measurement and Control |
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Sponsoring Units: DQI Chair: Christian Arenz, Princeton University Room: LACC 507 |
Wednesday, March 7, 2018 11:15AM - 11:27AM |
L47.00001: Direct Microwave Synthesis for Superconducting Qubit Control Guihem Ribeill, Diana Lee, Brian Donovan, Colm Ryan, Blake Johnson, Diego Ristè Readout and control of superconducting qubits in the circuit QED architecture relies on microwave pulses whose frequencies are most commonly in the 4-8 GHz range. The electronics generating these signals typically rely on quadrature modulation, where baseband tones generated by \sim 1 GS/s digital-to-analog converters are mixed up with a CW carrier. We present an alternative approach to the generation of these signals that exploits recent advances in high-speed DACs capable of placing power in higher Nyquist zones above half the sampling rate. |
Wednesday, March 7, 2018 11:27AM - 11:39AM |
L47.00002: Analytic optimal control theory for high-fidelity Rydberg-blockade entangling gates Lukas Theis, Felix Motzoi, Frank Wilhelm, Mark Saffman We show that the use of shaped pulses improves the fidelity of a Rydberg-blockade two-qubit entangling gate by several orders of magnitude compared to previous protocols based on square pulses or optimal control pulses. Using analytical derivative removal by adiabatic gate (DRAG) pulses that reduce excitation of primary leakage states and an analytical method of finding the optimal Rydberg blockade, we generate Bell states with a fidelity of F > 0.9999 in a 300 K environment for a gate time of only 50 ns, which is an order of magnitude faster than previous protocols. These results establish the potential of neutral atom qubits with Rydberg-blockade gates for scalable quantum computation. |
Wednesday, March 7, 2018 11:39AM - 11:51AM |
L47.00003: Optimization of Qubit Operations in the Presence of Spatially-Correlated Noise Vickram Premakumar, Ekmel Ercan, Joydip Ghosh, Mark Friesen, M. A. Eriksson, Susan Coppersmith, Robert Joynt A large class of semiconductor spin qubits suffer primarily from slow charge noise, which may be expected to exhibit spatial correlations in small multiqubit devices. We demonstrate strategies for the measurement of these spatial correlations using existing hardware. We then show how to use this information to optimize the operation of the device, using the idea of an approximately decoherence-free subspace. Specific circuits for various quantum information-processing tasks are presented for qubits that use the CPhase rather than the CNOT as the entangling gate. A programmable 2-qubit device with such gates has recently come into operation [T.F. Watson et al, arXiv:1708.04214] |
Wednesday, March 7, 2018 11:51AM - 12:03PM |
L47.00004: Local Gradient Optimization of Modular Entangling Sequences Arman Setser, Michael Goerz, Jason Kestner Implementation of logical entangling gates is an important step towards realizing a quantum computer. We use the L-BFGS-B method to find single-qubit rotations which can be interweaved between applications of a noisy entangling gate to dramatically suppress any unknown logical error present in the entangling gate while preserving the entangling power. This approach is completely modular, and is not specific to any particular Hamiltonian. Remarkably, this modular sequence works for 1/f time-dependent gate noise as well as for quasi-static noise. We show how sequence fidelity depends on the fidelity of the local rotations and the noise strength. The modularity of this approach allows for application to any two-qubit system, regardless of the details of the experimental implementation. |
Wednesday, March 7, 2018 12:03PM - 12:15PM |
L47.00005: Effective Evolution of a Driven Qubit Beyond the Rotating Wave Approximation Daniel Zeuch, David DiVincenzo Fast quantum gates require high-amplitude pulses. The applicability of the rotating wave approximation is thus questionable, and one needs to take into account effects of the fast-oscillating terms in the Hamiltonian. These terms generate intricate features in the state evolution of the qubit on a time scale of 1/ω, where ω is the qubit resonance frequency. These features complicate the task to track the state evolution. For a constant drive envelope, an effective Hamiltonian includes the Bloch-Siegert term [1], a shifted resonance condition, and a term which renormalizes the Rabi frequency. Using the Magnus expansion [2] we obtain an effective Hamiltonian for time-dependent pulses of amplitude comparable to the qubit frequency, and the time dependence of this Hamiltonian is solely determined by the drive. This Hamiltonian yields a family of smooth trajectories in the rotating frame, each of which agrees stroboscopically with the actual state trajectory. Our approach has the potential to cost-effectively design accurate pulse shapes for quantum gates. |
Wednesday, March 7, 2018 12:15PM - 12:27PM |
L47.00006: Machine learning approaches to predictive qubit-state estimation under dephasing Riddhi Swaroop Gupta, Michael Biercuk Decoherence remains a major challenge in quantum computing hardware and a variety of physical-layer controls provide opportunities to mitigate the impact of this phenomenon. In particular, laboratory-based systems typically suffer from the presence of non-Markovian noise processes and this opens an opportunity for using feedback and feedforward correction strategies exploiting underlying noise correlations. In this work, we use a numerical record of projective qubit measurements to investigate the performance of various machine learning algorithms in performing state estimation (retrodiction) and forward prediction of future qubit state evolution. Our approaches involve the construction of a dynamical model capturing qubit dynamics via autoregressive or Fourier-type protocols. A comparison of achievable prediction horizons, model robustness, and noise filtering capabilities for Kalman Filters (KF) and a Gaussian Process Regression (GPR) algorithm is provided. We demonstrate superior performance from the autoregressive KF relative to Fourier-based KF approaches. Further, a GPR algorithm with an infinite basis of oscillators permits only retrodiction based on the data but not forward prediction. |
Wednesday, March 7, 2018 12:27PM - 12:39PM |
L47.00007: Experimental quantum Hamiltonian learning Jianwei Wang, Stefano Paesani, Raffaele Santagati, Sebastian Knauer, Antonio Gentile, Nathan Wiebe, Maurangelo Petruzzella, Jeremy O’Brien, John Rarity, Anthony Laing, Mark Thompson The efficient characterization and validation of the underlying model of a quantum physical system is a central challenge in the development of quantum techonologies. Quantum Hamiltonian Learning (QHL) combines the capabilities of quantum information processing and classical machine learning to allow the efficient characterization of the model of quantum systems. The behavior of a quantum Hamiltonian model can be efficiently predicted by a quantum simulator, and the predictions are contrasted with the data obtained from the system to infer its Hamiltonian via Bayesian methods. |
Wednesday, March 7, 2018 12:39PM - 12:51PM |
L47.00008: Reinforcement Learning for Quantum Control Barry Sanders, Pantita Palittapongarnpim, Shakib Vedaie Quantum control steers controllable degrees of freedom of a quantum system such that its dynamics follows desired behavior with respect to chosen observables. We map quantum control problems into a reinforcement learning (RL) framework [P Palittapongarnpim, P Wittek, E Zahedinejad, S Vedaie, B C Sanders, Neurocomputing 268 (2017) 116-126], which eschews model-based optimization and instead searches for operating parameters based on trial and experience. The RL approach is especially valuable when models are not trusted, which is likely the norm for complex quantum systems arising, e.g., in scalable quantum computing impementations. |
Wednesday, March 7, 2018 12:51PM - 1:03PM |
L47.00009: Reinforcement learning for quantum memory Thomas Foesel, Petru Tighineanu, Talitha Weiss, Florian Marquardt The past few years have seen dramatic demonstrations of the power of neural networks to challenging real-world applications in many domains. In the search for optimal control sequences, where the success can only be judged with some time-delay, reinforcement learning is the method of choice. We have explored how a neural-network based agent can be trained to generate optimal control sequences for quantum feedback, where the agent interacts with a quantum system, using reinforcement learning. We apply this to the problem of stabilizing quantum memories based on few-qubit systems, where the qubit layout and available set of gates is specified by the user. |
Wednesday, March 7, 2018 1:03PM - 1:15PM |
L47.00010: Training a Recurrent Neural Network to Predict Quantum Trajectories from Raw Observation. Emmanuel Flurin, Leigh Martin, Shay Hacohen-Gourgy, Irfan Siddiqi Quantum mechanics provides us with an accurate set of rules to optimally predict the outcome of experiments, however it is also infamous for being abstract and highly counter intuitive. Neural networks are powerful tools to extract non-trivial correlation in vast datasets, they recently outperformed state-of-the-art techniques in language translation, medical diagnosis or image recognitions. It remains to be seen if they can be of aid in learning non-intuitive dynamics such as ones found in quantum systems without any prior. Here, we demonstrate that a recurrent neural network can be trained in real time to infer the quantum evolution of a superconducting qubit under non-trivial unitary evolution and continuous measurement from raw experimental observations only. These predictions are exploited to extract the system Hamiltonian, measurement operators and parameters such as quantum efficiency with a greater accuracy than usual calibration methods. Also, the quantum tomography of an unknown initial state is performed without prior calibration. This work shows that quantum mechanics can be inferred from observation based on deep learning methods and can be readily extended to larger quantum system in a model independent fashion to enhance quantum sensing or QCVV. |
Wednesday, March 7, 2018 1:15PM - 1:27PM |
L47.00011: Improving Readout Fidelity of Circuit QED Quantum Processors using Recurrent Neural Networks Bradley Mitchell, James Colless, Irfan Siddiqi Achieving high-fidelity state readout of quantum processors is vital for implementing efficient quantum algorithms. Typical projective measurement readout protocols in circuit QED use Gaussian Mixture Models with thresholding to classify qubit states, but neglect correlations in time. Prior works have trained traditional machine learning classifiers, such as support vector machines, on time-resolved data to improve state readout fidelity. Motivated by their expressive power in learning long-time correlations in sequential data, we train Long Short-Term Memory (LSTM) recurrent neural network (RNN) classifiers on time-resolved dispersive readout data from a circuit QED quantum processor. We then explore the capabilities of such LSTM RNNs for learning multi-qubit correlations. |
Wednesday, March 7, 2018 1:27PM - 1:39PM |
L47.00012: Time domain measurement of on-chip flux waveforms for superconducting qubits Brooks Foxen, Zijun Chen, Ben Chiaro, Andrew Dunsworth, Charles Neill, Jim Wenner, John Martinis Implementing high fidelity entangling gates between frequency-tunable superconducting qubits often requires ad hoc optimization of control waveforms on a per-channel basis due to signal path irregularities. To better model these irregularities and more efficiently optimize control waveforms, we have developed a method of directly measuring flux waveforms on-chip using a low Q tunable resonator. This device may be operated in two regimes--one with a high sensitivity to flux, used to characterize DC to ~500 MHz current waveforms, and another which can can provide an estimate of the worst standing wave amplitude in a microwave chain. Through A/B comparisons, this technique provides an avenue for measuring the transfer function of specific signal chain components in a cryogenic environment avoiding both the complication of measuring qubits and the limits of their coherence time. |
Wednesday, March 7, 2018 1:39PM - 1:51PM |
L47.00013: Detecting measurement correlations with graphical models Matthew Ware, Guilhem Ribeill, Marcus da Silva As quantum information with superconducting circuits scales up, detecting and quantifying correlated errors becomes crucial, as fault tolerance and error correction schemes usually assume uncorrelated errors. Measurement errors, in particular, are likely candidates for correlation, especially in superconducting circuit systems as the readout of multiple qubits is often performed through shared measurement chains to reduce resource requirements (such as number of amplifiers and other cryogenic components). A naive approach to quantify these correlations by reconstructing the distribution of outcomes conditioned on the systems' state scales exponentially, and is therefore impractical. We present an approach that avoids this pitfall by reconstructing a sparse undirected graphical model to approximate the distribution of errors, and demonstrate its use superconducting transmon qubits. |
Wednesday, March 7, 2018 1:51PM - 2:03PM |
L47.00014: Demonstrated Readout of a QFP Logic Element with an RQL Josephson Transmission Line Alexander Marakov, Mark Nowakowski, Timothy Manning, Micah Stoutimore, Aaron Lee, Moe Khalil, James Medford, Anthony Przybysz Recent interest in superconducting digital logic has increased as demonstrated by the IARPA Cryogenic Computing Complexity (C3) program with AQFP logic and RQL logic demonstrated as leading competitors with complex logic and large-scale device demonstrations [1, 2]. In a fully realized cryogenic digital computer, it may be beneficial to utilize both logic families for optimal performance. To that end, we demonstrate the first generalizable, reliable mapping of Quantum Flux Parametron (QFP) circulating states into Reciprocal Quantum Logic (RQL) single flux quantum pulse pairs. By simultaneously measuring the QFP state with both a flux tunable resonator and RQL, we verify that the RQL components of our circuit are faithfully transmitting the digital information stored in the QFP state. The individual bit error rate of this process does not exceed 2E-6 at a maximum reference clock frequency of 6 GHz, and is limited only by the length of the measurement. |
Wednesday, March 7, 2018 2:03PM - 2:15PM |
L47.00015: Energy Control of a Superconducting Flux Qubit with an Oscillating Magnetic Field Hiraku Toida, Takuya Ohrai, Yuichiro Matsuzaki, Kosuke Kakuyanagi, Hiroshi Yamaguchi, Shiro Saito We control energy of a superconducting flux qubit by an oscillating magnetic field. Our device consists of a flux qubit inductively coupled to a frequency tunable resonator. The tunable resonator is implemented with an on-chip LC circuit containing a superconducting quantum interference device (SQUID). Hamiltonian of the system is represented as follows. |
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