Quantum optimal control has been shown to improve the performance of quantum technology devices up to their limits in terms e.g. of system size and speed of operation. This talk will review our recent results with a variety of quantum technology platforms, and introduce our newly developed software for automatic calibration of quantum operations - the fundamental building block of next-generation quantum firmware.
Quantum optimal control has been shown to improve the performance of quantum technology devices up to their limits in terms e.g. of system size and speed of operation. This talk will review our recent results with a variety of quantum technology platforms, and introduce our newly developed software for automatic calibration of quantum operations - the fundamental building block of next-generation quantum firmware.
This study optimizes qubit control protocols using optimal control theory, machine learning, and stochastic modeling to enhance quantum computing performance. Quantum computing represents a transformative leap in computational power, with qubits serving as the fundamental building blocks of quantum information processing. However, the efficient control and manipulation of qubits remain significant challenges due to decoherence, noise, and imperfect control pulses. This study focuses on optimizing qubit control protocols using advanced applied mathematical techniques, including optimal control theory, machine learning, and stochastic modeling, to enhance quantum computing performance. By leveraging these techniques, we develop robust control strategies that minimize errors, improve gate fidelity, and extend coherence times. Our methodology integrates numerical simulations with experimental validation, demonstrating significant improvements in qubit control accuracy and reliability. The results highlight the potential of these optimized protocols to enable scalable and fault-tolerant quantum computing. This work contributes to the ongoing efforts to realize practical quantum technologies, offering insights into the interplay between mathematical optimization and quantum system dynamics.
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. The potential of quantum computing to deliver compelling performance to a wide range of demanding applications is generating tremendous excitement. This talk will review the promise and the challenges to achieving large-scale quantum computers capable of real-world applications. Intel's research to address the challenges spans the entire quantum computer system, from qubit devices to the hardware and software required to control these devices and the quantum algorithms that will harness the power of quantum technologies.
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC 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 QC 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 key features including a flexible optimization toolkit, engineering-inspired 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 derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.
Quantum optimal control, a toolbox for devising and implementing the shapes of external fields that accomplish given tasks in the operation of a quantum device in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The last few years have seen a rapid evolution and expansion of the field. We review here recent progress in our understanding of the controllability of open quantum systems and in the development and application of quantum control techniques to quantum technologies. We also address key challenges and sketch a roadmap for future developments.
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.
Abstract Quantum optimal control, a toolbox for devising and implementing the shapes of external fields that accomplish given tasks in the operation of a quantum device in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The last few years have seen a rapid evolution and expansion of the field. We review here recent progress in our understanding of the controllability of open quantum systems and in the development and application of quantum control techniques to quantum technologies. We also address key challenges and sketch a roadmap for future developments.