arXiv subjects

Michael J. Biercuk

Publications and source records attributed to Michael J. Biercuk.

At least 37 records · Page 2Linked to original sources

arXiv2022

Optimised Bayesian system identification in quantum devices

Identifying and calibrating quantitative dynamical models for physical quantum systems is important for a variety of applications. Here we present a closed-loop Bayesian learning algorithm for estimating multiple unknown parameters in a dynamical model, using optimised experimental "probe" controls and measurement. The estimation algorithm is based on a Bayesian particle filter, and is designed to autonomously choose informationally-optimised probe experiments with which to compare to model predictions. We demonstrate the performance of the algorithm in both simulated calibration tasks and in an experimental single-qubit ion-trap system. Experimentally, we find that with 60x fewer samples, we exceed the precision of conventional calibration methods, delivering an approximately 93x improvement in efficiency (as quantified by the reduction of measurements required to achieve a target residual uncertainty and multiplied by the increase in accuracy). In simulated and experimental demonstrations, we see that successively longer pulses are selected as the posterior uncertainty iteratively decreases, leading to an exponential improvement in the accuracy of model parameters with the number of experimental queries.

quant-ph↗

arXiv2022

Quantum computing for transport optimization

We explore the near-term intersection of quantum computing with the transport sector. To support near-term integration, we introduce a framework for assessing the suitability of transport optimization problems for obtaining potential performance enhancement using quantum algorithms. Given a suitable problem, we then present a workflow for obtaining valuable transport solutions using quantum computers, articulate the limitations on contemporary systems, and describe newly available performance-enhancing tools applicable to current commercial quantum computing systems. We make this integration process concrete by following the assessment framework and integration workflow for an exemplary vehicle routing optimization problem: the Capacitated Vehicle Routing Problem. We present novel advances to exponentially reduce the required computational resources, and experimentally demonstrate a prototype implementation exhibiting over 20X circuit performance enhancement on a real quantum device.

quant-ph↗

arXiv2021

Quantum oscillator noise spectroscopy via displaced cat states

Quantum harmonic oscillators are central to many modern quantum technologies. We introduce a method to determine the frequency noise spectrum of oscillator modes through coupling them to a qubit with continuously driven qubit-state-dependent displacements. We reconstruct the noise spectrum using a series of different drive phase and amplitude modulation patterns in conjunction with a data-fusion routine based on convex optimization. We apply the technique to the identification of intrinsic noise in the motional frequency of a single trapped ion with sensitivity to fluctuations at the sub-Hz level in a spectral range from quasi-DC up to 50 kHz.

quant-ph↗

arXiv2021

Analog quantum simulation of chemical dynamics

Ultrafast chemical reactions are difficult to simulate because they involve entangled, many-body wavefunctions whose computational complexity grows rapidly with molecular size. In photochemistry, the breakdown of the Born-Oppenheimer approximation further complicates the problem by entangling nuclear and electronic degrees of freedom. Here, we show that analog quantum simulators can efficiently simulate molecular dynamics using commonly available bosonic modes to represent molecular vibrations. Our approach can be implemented in any device with a qudit controllably coupled to bosonic oscillators and with quantum hardware resources that scale linearly with molecular size, and offers significant resource savings compared to digital quantum simulation algorithms. Advantages of our approach include a time resolution orders of magnitude better than ultrafast spectroscopy, the ability to simulate large molecules with limited hardware using a Suzuki-Trotter expansion, and the ability to implement realistic system-bath interactions with only one additional interaction per mode. Our approach can be implemented with current technology; e.g., the conical intersection in pyrazine can be simulated using a single trapped ion. Therefore, we expect our method will enable classically intractable chemical dynamics simulations in the near term.

quant-ph↗

arXiv2021

Experimental Deep Reinforcement Learning for Error-Robust Gateset Design on a Superconducting Quantum Computer

Quantum computers promise tremendous impact across applications -- and have shown great strides in hardware engineering -- but remain notoriously error prone. Careful design of low-level controls has been shown to compensate for the processes which induce hardware errors, leveraging techniques from optimal and robust control. However, these techniques rely heavily on the availability of highly accurate and detailed physical models which generally only achieve sufficient representative fidelity for the most simple operations and generic noise modes. In this work, we use deep reinforcement learning to design a universal set of error-robust quantum logic gates on a superconducting quantum computer, without requiring knowledge of a specific Hamiltonian model of the system, its controls, or its underlying error processes. We experimentally demonstrate that a fully autonomous deep reinforcement learning agent can design single qubit gates up to $3\times$ faster than default DRAG operations without additional leakage error, and exhibiting robustness against calibration drifts over weeks. We then show that $ZX(-π/2)$ operations implemented using the cross-resonance interaction can outperform hardware default gates by over $2\times$ and equivalently exhibit superior calibration-free performance up to 25 days post optimization using various metrics. We benchmark the performance of deep reinforcement learning derived gates against other black box optimization techniques, showing that deep reinforcement learning can achieve comparable or marginally superior performance, even with limited hardware access.

quant-ph↗

arXiv2020

Adaptive filtering of projective quantum measurements using discrete stochastic methods

Adaptive filtering is a powerful class of control theoretic concepts useful in extracting information from noisy data sets or performing forward prediction in time for a dynamic system. The broad utilization of the associated algorithms makes them attractive targets for similar problems in the quantum domain. To date, however, the construction of adaptive filters for quantum systems has typically been carried out in terms of stochastic differential equations for weak, continuous quantum measurements, as used in linear quantum systems such as optical cavities. Discretized measurement models are not as easily treated in this framework, but are frequently employed in quantum information systems leveraging projective measurements. This paper presents a detailed analysis of several technical innovations that enable classical filtering of discrete projective measurements, useful for adaptively learning system-dynamics, noise properties, or hardware performance variations in classically correlated measurement data from quantum devices. In previous work we studied a specific case of this framework, in which noise and calibration errors on qubit arrays could be efficiently characterized in space; here, we present a generalized analysis of filtering in quantum systems and demonstrate that the traditional convergence properties of nonlinear classical filtering hold using single-shot projective measurements. These results are important early demonstrations indicating that a range of concepts and techniques from classical nonlinear filtering theory may be applied to the characterization of quantum systems involving discretized projective measurements, paving the way for broader adoption of control theoretic techniques in quantum technology.

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arXiv2020

Error-robust quantum logic optimization using a cloud quantum computer interface

We describe an experimental effort designing and deploying error-robust single-qubit operations using a cloud-based quantum computer and analog-layer programming access. We design numerically-optimized pulses that implement target operations and exhibit robustness to various error processes including dephasing noise, instabilities in control amplitudes, and crosstalk. Pulse optimization is performed using a flexible optimization package incorporating a device model and physically-relevant constraints (e.g. bandwidth limits on the transmission lines of the dilution refrigerator housing IBM Quantum hardware). We present techniques for conversion and calibration of physical Hamiltonian definitions to pulse waveforms programmed via Qiskit Pulse and compare performance against hardware default DRAG pulses on a five-qubit device. Experimental measurements reveal default DRAG pulses exhibit coherent errors an order of magnitude larger than tabulated randomized-benchmarking measurements; solutions designed to be robust against these errors outperform hardware-default pulses for all qubits across multiple metrics. Experimental measurements demonstrate performance enhancements up to: $\sim10\times$ single-qubit gate coherent-error reduction; $\sim5\times$ average coherent-error reduction across a five qubit system; $\sim10\times$ increase in calibration window to one week of valid pulse calibration; $\sim12\times$ reduction gate-error variability across qubits and over time; and up to $\sim9\times$ reduction in single-qubit gate error (including crosstalk) in the presence of fully parallelized operations. Randomized benchmarking reveals error rates for Clifford gates constructed from optimized pulses consistent with tabulated $T_{1}$ limits, and demonstrates a narrowing of the distribution of outcomes over randomizations associated with suppression of coherent-errors.

quant-ph↗

arXiv2020

Simultaneous spectral estimation of dephasing and amplitude noise on a qubit sensor via optimally band-limited control

The fragility of quantum systems makes them ideally suited for sensing applications at the nanoscale. However, interpreting the output signal of a qubit-based sensor is generally complicated by background clutter due to out-of-band spectral leakage, as well as ambiguity in signal origin when the sensor is operated with noisy hardware. We present a sensing protocol based on optimally band-limited "Slepian functions" that can overcome these challenges, by providing narrowband sensing of ambient dephasing noise, coupling additively to the sensor along the ${z}$-axis, while permitting isolation of the target noise spectrum from other contributions coupling along a different axis. This is achieved by introducing a finite-difference control modulation, which linearizes the sensor's response and affords tunable band-limited "windowing" in frequency. Building on these techniques, we experimentally demonstrate two spectral estimation capabilities using a trapped-ion qubit sensor. We first perform efficient experimental reconstruction of a "mixed" dephasing spectrum, composed of a broadband $1/f$-type spectrum with discrete spurs. We then demonstrate the simultaneous reconstruction of overlapping dephasing and control noise spectra from a single set of measurements, in a setting where the two noise sources contribute equally to the sensor's response. Our approach provides a direct means to augment quantum-sensor performance in the presence of both complex broadband noise environments and imperfect control signals, by optimally complying with realistic time-bandwidth constraints.

quant-ph↗

arXiv2020

Software tools for quantum control: Improving quantum computer performance through noise and error suppression

Manipulating quantum computing hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise limits the performance and capabilities of noisy intermediate-scale quantum (NISQ) devices, as well as any future quantum computing technologies. Fortunately quantum control enables efficient execution of quantum logic operations and algorithms with built-in robustness to errors, without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in quantum computing research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the quantum computing software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of central product features including a flexible optimization toolkit, filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions using these tools in real experimental settings for both trapped-ion and superconducting quantum computer hardware.

quant-ph↗

arXiv2020

Numeric optimization for configurable, parallel, error-robust entangling gates in large ion registers

We study a class of entangling gates for trapped atomic ions and demonstrate the use of numeric optimization techniques to create a wide range of fast, error-robust gate constructions. Our approach introduces a framework for numeric optimization using individually addressed, amplitude and phase modulated controls targeting maximally and partially entangling operations on ion pairs, complete multi-ion registers, multi-ion subsets of large registers, and parallel operations within a single register. Our calculations and simulations demonstrate that the inclusion of modulation of the difference phase for the bichromatic drive used in the Mølmer-Sørensen gate permits approximately time-optimal control across a range of gate configurations, and when suitably combined with analytic constraints can also provide robustness against key experimental sources of error. We further demonstrate the impact of experimental constraints such as bounds on coupling rates or modulation band-limits on achievable performance. Using a custom optimization engine based on TensorFlow we also demonstrate time-to-solution for optimizations on ion registers up to 20 ions of order tens of minutes using a local-instance laptop, allowing computational access to system-scales relevant to near-term trapped-ion devices.

quant-ph↗

arXiv2020

Integration of spectator qubits into quantum computer architectures for hardware tuneup and calibration

Performing efficient quantum computer tuneup and calibration is essential for growth in system complexity. In this work we explore the link between facilitating such capabilities and the underlying architecture of the physical hardware. We focus on the specific challenge of measuring (``mapping'') spatially inhomogeneous quasi-static calibration errors using spectator qubits dedicated to the task of sensing and calibration. We introduce a novel architectural concept for such spectator qubits: arranging them spatially according to prescriptions from optimal 2D approximation theory. We show that this insight allows for efficient reconstruction of inhomogeneities in qubit calibration, focusing on the specific example of frequency errors which may arise from fabrication variances or ambient magnetic fields. Our results demonstrate that optimal interpolation techniques display near optimal error-scaling in cases where the measured characteristic (here the qubit frequency) varies smoothly, and we probe the limits of these benefits as a function of measurement uncertainty. For more complex spatial variations, we demonstrate that the NMQA formalism for adaptive measurement and noise filtering outperforms optimal interpolation techniques in isolation, and crucially, can be combined with insights from optimal interpolation theory to produce a general purpose protocol.

quant-ph↗

arXiv2020

A high-power spectral beamsplitter for closely spaced frequencies

An experimental realization of a compact, high-power spectral beamsplitter for nearly equal frequencies and identical polarization based on two-beam interference in a free-space Mach-Zehnder interferometer is presented. We demonstrate the power- and cost-efficient generation and subsequent spatial separation of two laser tones from a single sum-frequency-generation stage using double-sideband suppressed-carrier modulation in the infrared, and beam splitting in the visible at high power. The interferometer spectrally splits >98% of the incident power when accounting for bulk absorption. The beamsplitter can be constructed identically for any power or spectral range required for which suitable optics are available.

physics.atom-ph↗

arXiv2019

Autonomous adaptive noise characterization in quantum computers

New quantum computing architectures consider integrating qubits as sensors to provide actionable information useful for decoherence mitigation on neighboring data qubits, but little work has addressed how such schemes may be efficiently implemented in order to maximize information utilization. Techniques from classical estimation and dynamic control, suitably adapted to the strictures of quantum measurement, provide an opportunity to extract augmented hardware performance through automation of low-level characterization and control. In this work, we present an autonomous learning framework, Noise Mapping for Quantum Architectures (NMQA), for adaptive scheduling of sensor-qubit measurements and efficient spatial noise mapping (prior to actuation) across device architectures. Via a two-layer particle filter, NMQA receives binary measurements and determines regions within the architecture that share common noise processes; an adaptive controller then schedules future measurements to reduce map uncertainty. Numerical analysis and experiments on an array of trapped ytterbium ions demonstrate that NMQA outperforms brute-force mapping by up-to $18$x ($3$x) in simulations (experiments), calculated as a reduction in the number of measurements required to map a spatially inhomogeneous magnetic field with a target error metric. As an early adaptation of robotic control to quantum devices, this work opens up exciting new avenues in quantum computer science.

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arXiv2019

Phase-modulated entangling gates robust to static and time-varying errors

Entangling operations are among the most important primitive gates employed in quantum computing and it is crucial to ensure high-fidelity implementations as systems are scaled up. We experimentally realize and characterize a simple scheme to minimize errors in entangling operations related to the residual excitation of mediating bosonic oscillator modes that both improves gate robustness and provides scaling benefits in larger systems. The technique employs discrete phase shifts in the control field driving the gate operation, determined either analytically or numerically, to ensure all modes are de-excited at arbitrary user-defined times. We demonstrate an average gate fidelity of 99.4(2)% across a wide range of parameters in a system of $^{171}\text{Yb}^{+}$ trapped ion qubits, and observe a reduction of gate error in the presence of common experimental error sources. Our approach provides a unified framework to achieve robustness against both static and time-varying laser amplitude and frequency detuning errors. We verify these capabilities through system-identification experiments revealing improvements in error-susceptibility achieved in phase-modulated gates.

quant-ph↗

arXiv2019

Site-resolved imaging of beryllium ion crystals in a high-optical-access Penning trap with inbore optomechanics

We present the design, construction and characterization of an experimental system capable of supporting a broad class of quantum simulation experiments with hundreds of spin qubits using Be-9 ions in a Penning trap. This article provides a detailed overview of the core optical and trapping subsystems, and their integration. We begin with a description of a dual-trap design separating loading and experimental zones and associated vacuum infrastructure design. The experimental-zone trap electrodes are designed for wide-angle optical access (e.g. for lasers used to engineer spin-motional coupling across large ion crystals) while simultaneously providing a harmonic trapping potential. We describe a near-zero-loss liquid-cryogen-based superconducting magnet, employed in both trapping and establishing a quantization field for ion spin-states, and equipped with a dual-stage remote-motor LN2LHe recondenser. Experimental measurements using a nuclear magnetic resonance (NMR) probe demonstrate part-per-million homogeneity over 7 mm-diameter cylindrical volume, with no discernible effect on the measured NMR linewidth from pulse-tube operation. Next we describe a custom-engineered inbore optomechanical system which delivers ultraviolet (UV) laser light to the trap, and supports multiple aligned optical objectives for top- and sideview imaging in the experimental trap region. We describe design choices including the use of non-magnetic goniometers and translation stages for precision alignment. Further, the optomechanical system integrates UV-compatible fiber optics which decouple the system's alignment from remote light sources. Using this system we present site-resolved images of ion crystals and demonstrate the ability to realize both planar and three-dimensional ion arrays via control of rotating wall electrodes and radial laser beams. Looking to future work, we include interferometric..

physics.atom-ph↗

arXiv2018

Qiskit Backend Specifications for OpenQASM and OpenPulse Experiments

As interest in quantum computing grows, there is a pressing need for standardized API's so that algorithm designers, circuit designers, and physicists can be provided a common reference frame for designing, executing, and optimizing experiments. There is also a need for a language specification that goes beyond gates and allows users to specify the time dynamics of a quantum experiment and recover the time dynamics of the output. In this document we provide a specification for a common interface to backends (simulators and experiments) and a standarized data structure (Qobj --- quantum object) for sending experiments to those backends via Qiskit. We also introduce OpenPulse, a language for specifying pulse level control (i.e. control of the continuous time dynamics) of a general quantum device independent of the specific hardware implementation.

quant-ph↗

arXiv2018

Optimally band-limited spectroscopy of control noise using a qubit sensor

Classical control noise is ubiquitous in qubit devices, making its accurate spectral characterization essential for designing optimized error suppression strategies at the physical level. Here, we focus on multiplicative Gaussian amplitude control noise on a driven qubit sensor and show that sensing protocols using optimally band-limited Slepian modulation offer substantial benefit in realistic scenarios. Special emphasis is given to laying out the theoretical framework necessary for extending non-parametric multitaper spectral estimation to the quantum setting by highlighting key points of contact and differences with respect to the classical formulation. In particular, we introduce and analyze two approaches (adaptive vs. single-setting) to quantum multitaper estimation, and show how they provide a practical means to both identify fine spectral features not otherwise detectable by existing protocols and to obtain reliable prior estimates for use in subsequent parametric estimation, including high-resolution Bayesian techniques. We quantitatively characterize the performance of both single- and multitaper Slepian estimation protocols by numerically reconstructing representative spectral densities, and demonstrate their advantage over dynamical-decoupling noise spectroscopy approaches in reducing bias from spectral leakage as well as in compensating for aliasing effects while maintaining a desired sampling resolution.

quant-ph↗

arXiv2017

Machine learning for predictive estimation of qubit dynamics subject to dephasing

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 through feedback and feedforward control. In this work, we compare a variety of machine learning algorithms derived from diverse fields for the task of state estimation (retrodiction) and forward prediction of future qubit state evolution for a single qubit subject to classical, non-Markovian dephasing. Our approaches involve the construction of a dynamical model capturing qubit dynamics via autoregressive or Fourier-type protocols using only a historical record of projective measurements. A detailed comparison of achievable prediction horizons, model robustness, and measurement-noise-filtering capabilities for Kalman Filters (KF) and Gaussian Process Regression (GPR) algorithms is provided. We demonstrate superior performance from the autoregressive KF relative to Fourier-based KF approaches and focus on the role of filter optimization in achieving suitable performance. Finally, we examine several realizations of GPR using different kernels and discover that these approaches are generally not suitable for forward prediction. We highlight the underlying failure mechanism in this application and identify ways in which the output of the algorithm may be misidentified numerical artefacts.

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