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 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.
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.
Precise manipulation of quantum effects at the atomic and nanoscale has become an essential task in ongoing scientific and technological endeavors. Quantum control methods are thus routinely exploited for research in areas such as quantum materials, quantum chemistry, and atomic and molecular physics, as well as in the development of quantum technologies like computing, simulation, and sensing. Here, we present a pedagogical introduction to the basics of quantum control methods in tutorial form, with the aim of providing newcomers to the field with the core concepts and practical tools to use these methods in their research. We focus on three areas: shortcuts to adiabaticity, quantum optimal control, and machine-learning-based control. We lay out the basic theoretical elements of each area in a pedagogical way and describe their application to a series of example cases. For these, we include detailed analytical derivations as well as extensive numerical results. As an outlook, we discuss quantum control methods in the broader context of quantum technologies development and complex quantum systems research, outlining potential connections and synergies between them.
Precise manipulation of quantum effects at the atomic and nanoscale has become an essential task in ongoing scientific and technological endeavours. Quantum control methods are thus routinely exploited for research in areas such as quantum materials, quantum chemistry, and atomic and molecular physics, as well as in the development of quantum technologies like computing, simulation, and sensing. Here, we present a pedagogical introduction to the basics of quantum control methods in tutorial form, with the aim of providing newcomers to the field with the core concepts and practical tools to use these methods in their research. We focus on three areas: shortcuts to adiabaticity, quantum optimal control, and machine-learning-based control. We lay out the basic theoretical elements of each area in a pedagogical way and describe their application to a series of example cases. For these, we include detailed analytical derivations as well as extensive numerical results. As an outlook, we discuss quantum control methods in the broader context of quantum technologies development and complex quantum systems research, outlining potential connections and synergies between them.
Precise manipulation of quantum effects at the atomic and nanoscale has become an essential task in ongoing scientific and technological endeavors. Quantum control methods are thus routinely exploited for research in areas such as quantum materials, quantum chemistry, and atomic and molecular physics, as well as in the development of quantum technologies like computing, simulation, and sensing. Here, we present a pedagogical introduction to the basics of quantum control methods in tutorial form, with the aim of providing newcomers to the field with the core concepts and practical tools to use these methods in their research. We focus on three areas: shortcuts to adiabaticity, quantum optimal control, and machine-learning-based control. We lay out the basic theoretical elements of each area in a pedagogical way and describe their application to a series of example cases. For these, we include detailed analytical derivations as well as extensive numerical results. As an outlook, we discuss quantum control methods in the broader context of quantum technologies development and complex quantum systems research, outlining potential connections and synergies between them.
Implementing fast and high-fidelity quantum operations using open-loop quantum optimal control relies on having an accurate model of the quantum dynamics. Any deviations between this model and the complete dynamics of the device, such as the presence of spurious modes or pulse distortions, can degrade the performance of optimal controls in practice. Here, we propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment while specifically characterizing this quantum system. Concretely, we use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data and then optimize our experimental controls on this trained model. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations on a superconducting transmon qubit, using detailed numerical simulations. We demonstrate that this framework produces an accurate description of the device dynamics under arbitrary controls, together with the precise pulses achieving arbitrary single-qubit gates with a high fidelity of about 99.99%.
As ultracold atom experiments become highly controlled and scalable quantum simulators, they require sophisticated control over high-dimensional parameter spaces and generate increasingly complex measurement data that need to be analyzed and interpreted efficiently. Machine learning (ML) techniques have been established as versatile tools for addressing these challenges, offering strategies for data interpretation, experimental control, and theoretical modeling. In this review, we provide a perspective on how machine learning is being applied across various aspects of quantum simulation, with a focus on cold atomic systems. Emphasis is placed on practical use cases -- from classifying many-body phases to optimizing experimental protocols and representing quantum states -- highlighting the specific contexts in which different ML approaches prove effective. Rather than presenting algorithmic details, we focus on the physical insights enabled by ML and the kinds of problems in quantum simulation where these methods offer tangible benefits.