Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Y41: Quantum Control: Optimal ControlFocus Recordings Available

Hide Abstracts 
Sponsoring Units: DQI Chair: Dan Campbell, AFRL Room: McCormick Place W196C 
Friday, March 18, 2022 8:00AM  8:12AM 
Y41.00001: Optimal Control of Disordered Quantum Systems Steve Campbell We investigate several control strategies for the transport of an excitation along a spin chain. We demonstrate that high fidelity transport at the quantum speed limit can be achieved using protocols designed with differentiable programming. Building on this, show that this approach can be effectively adapted to control a disordered quantum system. We consider two settings: optimal control for a known, unwanted disorder pattern (i.e. a specific disorder realisation) and optimal control where only the disorder structure is known (i.e. optimising for high average fidelities). In the former, the disorder effects can be effectively countered for an appropriately chosen control protocol and extremely high fidelity control. However, in the latter setting the average fidelity can only be marginally improved. 
Friday, March 18, 2022 8:12AM  8:24AM 
Y41.00002: Quantum Noise Spectroscopy Informed Optimized Gates Andrew J Murphy, Helena G Yoest, Yasuo Oda, Leigh M Norris, Kevin Schultz, Gregory Quiroz, Timothy M Sweeney In recent years, a number of quantum noise spectroscopy (QNS) protocols have been developed to characterize spatiotemporally correlated noise processes. Estimates of the noise power spectral density from QNS protocols are meant to inform optimized control protocols designed to mitigate noise while simultaneously implementing a particular quantum operation. While it is widely accepted that QNS should yield an added advantage to optimized control, there has yet to be an experimental demonstration of QNSinformed optimized control on a nontrivial gate. In this talk, we experimentally demonstrate the advantage of Gradient Ascent in Function Space (Filter GrAFS) optimized control, using injected noise as a probe. Injected noise is generated using the Schrodinger Wave Autoregressive Moving Average (SchWARMA) model, phasemodulating an ideal control signal to mimic the noise spectrum of puretone dephasing noise. By subjecting optimized and nonoptimized controls to different noise environments, we experimentally demonstrate the advantage of Filter GrAFS optimized control. 
Friday, March 18, 2022 8:24AM  8:36AM 
Y41.00003: Filter Function Tailored NoiseOptimized Single Qubit Gates Yasuo Oda, Dennis Lucarelli, Kevin Schultz, David Clader, Gregory Quiroz Noise suppression protocols represent a class of techniques that are meant to reduce gate error rates via specially designed control sequences. Here, we discuss a protocol that optimizes smooth control sequences to combat temporallycorrelated noise, a class of noise that can be detrimental to quantum error correction. The control ansatz is specifically chosen to be a functional expansion of Slepians, a discrete time basis known to be optimally concentrated in time and frequency, and quite attractive when faced with experimental control hardware constraints. We leverage the filter function formalism to transform the control problem into a filter design problem, and show that the frequency response of a quantum system can be carefully tailored to avoid the most relevant dynamical contributions of the noise processes. Using gradient ascent, we obtain optimized filter functions and investigate the relationship between filter function design, control bandwidth, and noise characteristics. Even in the presence of multiaxis correlated noise, we show that as long as the noise spectral density is sufficiently weak over a range of frequencies, it is possible to generate control sequences that yield highpass or bandpass filter functions, and simultaneously produce high fidelity, arbitrary single qubit gate operations. 
Friday, March 18, 2022 8:36AM  9:12AM 
Y41.00004: Optimal control of open quantum systems Invited Speaker: Christiane Koch Optimal control theory (OCT) is a versatile tool that can be used to identify control strategies in the presence of decoherence, either avoiding or exploiting the environment. In this talk, I will discuss an example for each. First, for qubits encoded in the infinitedimensional Hilbert space of bosonic modes, I will show how to employ OCT to systematically enhance performance in the implementation of strong and ondemand interactions between the qubits [1]. Second, I will discuss OCT in driven dissipative evolutions. For example, in trapped ion qubits, a protocol derived with OCT allows for the resourceefficient dissipative generation of an entangled state [2]. Finally, I will assess prospects for quantum control in reservoir engineering where the desired dissipation can be realized via quantum nondemolition measurements. 
Friday, March 18, 2022 9:12AM  9:24AM 
Y41.00005: Improving the performance of algorithms on NISQDevices through automated control design and implementation Anurag Mishra, Thomas Merkh, Anthony Santana, Dominik Schmid, Harry Slayter, Pranav S Mundada, Mirko Amigo, Aaron Barbosa, Yuval Baum Current NISQ era quantum computing devices suffer from various sources of noise, such as leakage, dephasing and crosstalks which makes running useful quantum algorithms especially challenging on such devices. Our team recently demonstrated that quantum control can be used to improve the performance of individual gates, through automatedclosed loop optimization of microwave pulses. These controldesign techniques consistently nullify coherent errors on at least half of the CNOT gates on a quantum device, with others limited by T1. Previously, it was not clear how these improvements would impact the ultimate performance of high level quantum algorithms. We investigate how dropin replacement of optimized control pulses can improve the performance of algorithms with potential NISQ advantage, namely, quantum fourier transform (QFT), BernsteinVazirani and QAOA in both simulation and experiment. We execute a fully autonomous parallel gate optimization across multiqubit systems, and with the resulting optimized gates see consistent improvements in algorithm performance. In IBM systems with no further change to compilation strategy, dropin gate replacement delivers at least a 30% improvement to the success probability of an accurate outcome. We observe that deterministic algorithms benefit most from reduction of coherent gate errors. 
Friday, March 18, 2022 9:24AM  9:36AM 
Y41.00006: Limitations of Average Hamiltonian Theory for Quantum Control Wynter Alford, Chandrasekhar Ramanathan, Linta Joseph One path to Hamiltonian Engineering in solidstate spin systems is to use periodic pulse trains to make the system evolve under a desired effective Hamiltonian. These pulse trains are designed using Average Hamiltonian Theory, which uses the Magnus series expansion to obtain a timeindependent description of a periodic timedependent Hamiltonian at stroboscopic intervals. The most common application of these pulse trains has been to decouple internuclear magnetic dipolar interactions in solid state NMR. These sequences have been improved by increasing their length and complexity in order to cancel out higher order terms in this expansion. It has generally been believed that the Magnus expansion converges rapidly in the parameter regime in which these sequences are used, and that therefore the largest source of error in implementing these sequences comes from the lowest terms in the expansion that the pulse sequence does not account for. In an effort to explore the limits of control, we numerically explore if the maximum attainable fidelity of conventional AHTdesigned dipolar decoupling sequences is due to their sensitivity to experimental error or due to poor convergence of the Magnus series. 
Friday, March 18, 2022 9:36AM  9:48AM 
Y41.00007: Supercharging Quantum Optimal Control with Efficient Automatic Differentiation Michael H Goerz, Sebastian C Carrasco, Alastair Marshall, Vladimir S Malinovsky Numerical optimal control theory has proven an essential tool for achieving a core requirement of both quantum information and quantum metrology: the creation of particular nonclassical states, respectively of quantum processes that produce such states. Finding the most general solutions to the relevant control problems would require the direct optimization of, e.g., measures for entanglement, spin squeezing, or the quantum Fisher information. To date, such measures have generally not been considered suitable for optimal control due to the lack of an analytic derivative. We show how the use of automatic differentiation (AD) allows to directly optimize these nonclassical measures, and virtually any other functional. We further show how AD can be \emph{combined} with existing numerical techniques of quantum control to allow the formulation of a ``semiautomatic differentiation'' approach that eliminates the often exponential overhead associated with previous attempts to use AD in quantum control. Thus, our methods scale to large Hilbert space dimensions and open quantum systems. We illustrate the use of the technique for the optimization of entangling quantum gates and the creation of spinsqueezed states. 
Friday, March 18, 2022 9:48AM  10:00AM 
Y41.00008: Pushpull optimization of quantum controls (PPOQC) Priya Batra Optimization of quantum controls to achieve a target process is centered around an objective function comparing the realized process with the target. We propose an objective function that incorporates not only the target operator but also a set of its orthogonal operators whose combined influence leads to an efficient exploration of the parameter space, faster convergence, and extraction of superior solutions. The pushpull optimization, as we call it, can be adopted in various quantum control scenarios. We describe adopting it for gradientbased and variationalprinciplebased approaches. Numerical analysis of quantum registers with up to seven qubits reveals significant benefits of pushpull optimization. We describe applying the pushpull optimization to prepare a longlived singlet order in a twoqubit system using NMR techniques. 
Friday, March 18, 2022 10:00AM  10:12AM 
Y41.00009: Optimizing quantum control pulses with complex constraints and few variables through Tensorflow XiuHao Deng Applying optimal control algorithms on realistic quantum systems confronts two key challenges: to efficiently adopt physical constraints in the optimization and to minimize the variables for the convenience of experimental tuneups. In order to resolve these issues, we propose a novel algorithm by incorporating multiple constraints into the gradient optimization over piecewise pulse constant values, which are transformed to contained numbers of the finite Fourier basis for bandwidth control. Such complex constraints and variable transformation involved in the optimization introduce extreme difficulty in calculating gradients. We resolve this issue efficiently utilizing autodifferentiation on Tensorflow. We test our algorithm by finding smooth control pulses to implement singlequbit and twoqubit gates for superconducting transmon qubits with alwayson interaction, which remains a challenge of quantum control in various qubit systems. Our algorithm provides a promising optimal quantum control approach that is friendly to complex and optional physical constraints. 
Friday, March 18, 2022 10:12AM  10:24AM 
Y41.00010: Increasing memory and runtime performance of GRAPE for control in large quantum systems yunwei Lu, Vinh San Dinh, Jens Koch Gradient Ascent Pulse Engineering (GRAPE) is a popular technique in quantum optimal control. Recent implementations of the GRAPE algorithm are based on automatic differentiation (AD). AD helps avoid the need for coding of analytical gradients but incurs a large memory cost. Specifically, AD stores intermediate states and propagators at all time steps, thus posing a severe bottleneck for quantum systems with large Hilbert space dimension. To address this issue, we implement hardcoded analytical gradients in a scheme that avoids propagator storage and significantly reduces the storage of states. We further succeed in enhancing runtime performance with an improved algorithm for state propagation and propagator derivatives. We benchmark the performance and memory cost of our code against ADbased implementations for largedimensional state transfer and gate optimization problems. Results confirm the expected improvements that will allow us to tackle optimal control of much larger quantum systems in the future. 
Friday, March 18, 2022 10:24AM  10:36AM 
Y41.00011: Robust quantum computing on qubit arrays with fixed coupling Nguyen H Le, Max Cykiert, Eran Ginossar We show how robust and scalable quantum computing can be realized on an arbitrarily large two dimensional arrays of qubits with fixed longitudinal couplings despite significant uncertainty in all the qubitqubit and drivequbit coupling strengths. This opens the possibility for bypassing the fabrication complexity associated with tunable couplers required in conventional quantum computing hardware. Our approach is based on driving a chosen subarray of qubits such that the total multiqubit Hamiltonian can be decomposed into a sum of commuting fewqubit blocks and on efficient optimization of the unitary evolution within each block. Robust optimal control is then employed to implement a universal set of quantum gates with fidelities around 99.99% despite 1% uncertainty in the Hamiltonian's parameters. This robust feature is crucial for scaling up as uncertainty in the properties of qubits is substantial in large devices. 
Friday, March 18, 2022 10:36AM  10:48AM 
Y41.00012: Optimizing cross resonance gates using recurrent neural networks Sai Vinjanampathy, Aakash V, Sumeru Hazra, Rajamani Vijayaraghavan, Chaithanya Mude Quantum control is a powerful technique for implementing quantum circuits and designing highfidelity quantum gates. Besides analytical and gradientbased control protocols, recently machine learning has evolved as a tool to generate control pulses for implementing high fidelity quantum gates. In this work, we demonstrate a new deep learning model based on encoderdecoder architecture using Long Short Term Memory (LSTM) units combined with convolution layers to implement high fidelity fast quantum gates. The model architectures are trained to generate optimized pulse sequences for single and twoqubit gates with infidelities in the order of 105 and 104 respectively. Furthermore, we incorporate realworld hardware limitations by incorporating pulse constraints like amplitude and bandwidth limitations. We apply these techniques to coupled transmon qubits and study the optimal sequences with attention to leakage out of computational subspace and the effect of decoherence. 
Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit membership organization working to advance the knowledge of physics. 
© 2023 American Physical Society
 All rights reserved  Terms of Use
 Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 207403844
(301) 2093200
Editorial Office
1 Research Road, Ridge, NY 119612701
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700