Effectively 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 in particular limits the performance and algorithmic capabilities experienced by end users. Fortunately, in both the NISQ era and beyond, quantum control enables the efficient execution of quantum logic operations and quantum algorithms exhibiting robustness to errors, without the need for complex logical encoding. In this manuscript we introduce the first commercial-grade software tools for the application and integration of quantum control in quantum computing research from Q-CTRL, serving the needs of hardware R&D teams, algorithm developers, and end users. We survey quantum control and its role in combating noise and instability in near-term devices; our primary focus is on quantum firmware, the low-level software solutions designed to enhance the stability of quantum computational hardware at the physical layer. We explain the benefits of quantum firmware not only in error suppression, but also in simplifying higher-level compilation protocols and enhancing the efficiency of quantum error correction. Following this exposition, we provide an overview of Q-CTRL's classical software tools for creating and deploying optimized quantum control solutions at various layers of the quantum computing software stack. We describe our 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 technical overview of central product features including a multidimensional control-optimization engine, engineering-inspired filter functions for high-dimensional Hilbert spaces, and a new approach to noise characterization. Finally, we present a series of case studies demonstrating the utility of quantum control solutions derived from these tools in improving the performance of trapped-ion and superconducting quantum computer hardware.

Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions outperform best known solutions producing higher optical densities.

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A class of entangling gates for trapped atomic ions is studied and the use of numeric optimization techniques to create a wide range of fast, error‐robust gate constructions is demonstrated. A numeric optimization framework is introduced targeting maximally‐ and partially‐entangling operations on ion pairs, multi‐ion registers, multi‐ion subsets of large registers, and parallel operations within a single register. Ions are assumed to be individually addressed, permitting optimization over amplitude‐ and phase‐modulated controls. 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. The impact of experimental constraints such as bounds on coupling rates or modulation band‐limits on achievable performance is further demonstrated. Using a custom optimization engine based on TensorFlow, for optimizations on ion registers up to 20 ions, time‐to‐solution of order tens of minutes using a local‐instance laptop is also demonstrated, allowing computational access to system‐scales relevant to near‐term trapped‐ion devices.