Harrison Ball

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.

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.

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.

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..