Abstract Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings.
Abstract Robust quantum control is crucial for realizing practical quantum technologies. Energy landscape shaping offers an alternative to conventional dynamic control, providing theoretically enhanced robustness and simplifying implementation for certain applications. This work demonstrates the feasibility of robust energy landscape control in a practical implementation with ultracold atoms. We leverage a digital mirror device (DMD) to shape optical potentials, creating complex energy landscapes. To achieve a desired objective, such as efficient quantum state transfer, we formulate a novel hybrid optimization approach that effectively handles both continuous (laser power) and discrete (DMD pixel activation) control parameters. This approach combines constrained quasi-Newton methods with surrogate models for efficient exploration of the vast parameter space. Furthermore, we introduce a framework for analyzing the robustness of the resulting control schemes against experimental uncertainties. By modeling uncertainties as structured perturbations, we systematically assess controller performance and identify robust solutions. We apply these techniques to maximize spin transfer in a chain of trapped atoms, achieving high-fidelity control while maintaining robustness. Our findings provide insights into the experimental viability of controlled spin transfer in cold atom systems. More broadly, the presented optimization and robustness analysis methods apply to a wide range of quantum control problems, offering a toolkit for designing and evaluating robust controllers in complex experimental settings.
Quantum technologies based on cold atom platforms, while benefiting from highly stable and scalable atomic systems, still rely on the precise characterization, calibration, and control of complex experimental apparatuses. In this thesis, we develop automated approaches to address these critical tasks, integrating physical insight with optimization and learning methods, while complying with operational constraints. The bulk of the work presented here consists of three research papers. In the first one, we introduce physics-inspired machine-learning models to optimize optical dipole potentials for shaping ultracold gasses, achieving order-of-magnitude improvements in speed. In the second paper, we investigate atom transport between optical tweezers, a key auxiliary operation for scalable quantum processors, and demonstrate how shortcuts to adiabaticity and quantum optimal control can minimize heating and excitations during fast transport. The third and last paper analyzes a class of optimization landscapes relevant for quantum control, deriving from first principles a set of classical surrogates. We show how time and energy constraints translate into limited bandwidth and derivatives for the landscape, with consequences for the design of regression models and for optimization, which we relate to bounds in associated metrics. Together, these results provide concrete examples of how automated optimization, learning, and control can expand the toolset available for building quantum firmware for cold atom platforms. The methods presented not only improve experimental performance but keep implementation overhead at a minimum, thereby simplifying operations and facilitating scale-up efforts.
Quantum spin networks form a generic system to describe a range of quantum devices for quantum information processing and sensing applications. Understanding how to control them is essential to achieve devices with practical functionalities. Energy landscape shaping is a novel control paradigm to achieve selective transfer of excitations in a spin network with surprisingly strong robustness towards uncertainties in the Hamiltonians. Here we study the effect of decoherence, specifically generic pure dephasing, on the robustness of these controllers. Results indicate that while the effectiveness of the controllers is reduced by decoherence, certain controllers remain sufficiently effective, indicating potential to find highly effective controllers without exact knowledge of the decoherence processes.
Neutral atoms, trapped in optical tweezers and lattices, have gained significant attention owing to advances in laser technologies, control hardware, and innovative experimental techniques. These systems offer unique advantages such as long coherence times, natural homogeneity, and scalability, while also presenting challenges related to gate speed limitations, atom loss, slow measurements, and other imperfections. Quantum control theory has emerged as a crucial tool for advancing these technologies by enabling precise manipulation of atoms. In this thesis, we review the foundational concepts in quantum control theory, outlining both analytical approaches and numerical optimization techniques, including methods to account for experimental imperfections. A concise overview of neutral atom platforms follows, covering two distinct operational regimes: Rydberg atoms in optical tweezers and fermionic atoms in optical superlattices. The core of the thesis is dedicated to our work addressing practical control problems in neutral atom systems. First, we develop a platform-independent method for estimating and compensating nonlinear pulse distortions in experimental setups and demonstrate its use numerically for Rydberg atom excitations. Next, we tackle the challenge of implementing fast and robust gates in fermionic atom systems trapped in superlattices by optimizing lattice depths. Finally, we explore the application of Rydberg-based analog quantum simulators for variational quantum eigensolvers, using Lie-algebraic tools to analyze symmetry restrictions. This work highlights the essential role of quantum control methods in enhancing the fidelity, robustness, and applicability of neutral atom quantum technologies. It also demonstrates how combining theoretical tools, numerical optimization, and experimental considerations leads to practical solutions for scalable and high-performance quantum systems. The methods and insights developed here also offer transferable strategies for other quantum hardware architectures.
Quantum spin networks form a generic system to describe a range of quantum devices for quantum information processing and sensing applications. Understanding how to con- trol them is essential to achieve devices with practical function- alities. Energy landscape shaping is a novel control paradigm to achieve selective transfer of excitations in a spin network with surprisingly strong robustness towards uncertainties in the Hamiltonians. Here we study the effect of decoherence, specifically generic pure dephasing, on the robustness of these controllers. Results indicate that while the effectiveness of the controllers is reduced by decoherence, certain controllers remain sufficiently effective, indicating potential to find highly effective controllers without exact knowledge of the decoherence processes.
Ultracold quantum gases are usually prepared in conservative traps for quantum simulation experiments. The atomic density inhomogeneity, together with the consequent position-dependent energy and time scales of cold atoms in traditional harmonic traps, makes it difficult to manipulate and detect the sample at a higher level. These problems are partially solved by optical box traps made of blue-detuned hollow beams. However, generating a high-quality hollow beam with high light efficiency for the box trap is challenging. Here, we present a scheme that combines the fixed optics, including axicons and prisms, to pre-shape a Gaussian beam into a hollow beam with a digital micromirror device (DMD) to improve the quality of the hollow beam further, providing a nearly ideal optical potential of various shapes for preparing highly homogeneous cold atoms. The highest power-law exponent of potential walls can reach a value over 100, and the light efficiency from a Gaussian to a hollow beam is also improved compared to direct optical shaping by a mask or a DMD. Combined with a one-dimensional optical lattice, a nearly ideal two-dimensional uniform quantum gas with different geometrical boundaries can be prepared for exploring quantum many-body physics to an unprecedented level.
The implementation of spatial light modulators (SLMs) in quantum gas experiments has allowed the realization of ever more complex trapping geometries. As ultracold atoms may be sensitive to perturbations of the trapping potential at the 1% level, the high contrast ratios of digital micromirror devices are proving advantageous for use in optical trapping. Our laboratory pursues configurable traps based on the direct (nearly diffraction limited) imaging of a digital micromirror device (DMD). We achieve highly flexible potentials using commercially available microscope objectives external to our vacuum chamber that directly project the DMD to the atom plane, producing optical traps over an area of 130 μm × 200 μm, with a resolution of 630(10) nm full width at half maximum (FWHM) at 532 nm illumination. We combine these potentials with a horizontally propagating TEM00 or TEM01 Hermite-Gaussian optical sheet that provides vertical confinement. With the dynamic control enabled by the maximum full-frame rate of 20 KHz and on-board storage of <13,000 frames of the DMD, we study the transport of atoms and superfluid dynamics in configurable trapping geometries. Using the fast frame rate of the DMD we also produce intermediate grey levels that complement half-toning techniques for producing optimized grayscale patterns.