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Machine Learning Science and Technology

High-dimensional multi-fidelity Bayesian optimization for quantum control

Marjuka Ferdousi Lazin, Christian Robert Shelton, Simon N. Sandhofer, Bryan M. Wong

Published Oct 6, 2023• 21 citations
Publisher

Abstract

Abstract We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse problems in the quantum control of prototypical quantum systems. Our approach automatically constructs time-dependent control fields that enable transitions between initial and desired final quantum states. Most importantly, our BO approach gives impressive performance in constructing time-dependent control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide detailed descriptions of our machine learning methods as well as performance metrics for a variety of machine learning algorithms. Taken together, our results demonstrate that BO is a promising approach to efficiently and autonomously design control fields in general quantum dynamical systems.

DOI
10.1088/2632-2153/ad0100

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Abstract We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse problems in the quantum control of prototypical quantum systems. Our approach automatically constructs time-dependent control fields that enable transitions between initial and desired final quantum states. Most importantly, our BO approach gives impressive performance in constructing time-dependent control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide detailed descriptions of our machine learning methods as well as performance metrics for a variety of machine learning algorithms. Taken together, our results demonstrate that BO is a promising approach to efficiently and autonomously design control fields in general quantum dynamical systems.
We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.
The preparation of quantum states is crucial for enabling quantum computations and simulations. In this work, we present a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process (MFCP) with machine learning techniques. Specifically, we employ Bayesian optimization (BO) to enhance the efficiency of determining the measurement and feedback operators within the MFCP. As an illustration, we study the ground state preparation of the one-dimensional Bose–Hubbard model. Through BO, we are able to identify optimal parameters that can effectively drive the system towards low-energy states with a high probability across various quantum trajectories. Our results open up new directions for further exploration and development of advanced control strategies for quantum computations and simulations.
Bayesian Optimization (BO) is a powerful technique for optimizing black-box functions, frequently employed in areas such as machine learning hyperparameter tuning and experimental design. However, its performance can degrade significantly in high-dimensional spaces, leading to slow convergence and increased computational cost. This paper introduces a novel approach to Bayesian Optimization inspired by quantum amplitude amplification, a technique rooted in quantum computing. Our method utilizes a probabilistic representation of candidate solutions within a Hilbert space, employing amplitude amplification to systematically bias the search towards promising regions of the solution space. This approach aims to accelerate the optimization process, particularly in scenarios where traditional BO methods struggle due to the curse of dimensionality. The core claim of this work is to demonstrate a speedup in convergence compared to standard BO algorithms, leveraging the principles of amplitude amplification for enhanced exploration and exploitation. The theoretical framework is presented, alongside a detailed explanation of the algorithm's components, and the results are discussed, highlighting the potential for improved efficiency in complex optimization problems. We focus on the core mechanics of using amplitude amplification to enhance the probability of finding optimal solutions.
This paper introduces a novel quantum machine learning algorithm based on Bayesian optimization, designed to automatically adjust model parameters for optimal performance. Traditional machine learning model parameter optimization often relies on manual adjustments, which struggles to adapt to evolving data. Our algorithm leverages the probabilistic nature of model parameters, employing Bayesian optimization to dynamically refine the model, ultimately leading to improved accuracy and generalization. This work presents a fundamentally new approach to model parameter tuning, offering enhanced adaptability and potential for superior results compared to existing methods.
This paper presents a novel approach to Bayesian Optimization (BO) that leverages quantum computation to significantly accelerate the optimization process. The core idea is to employ a quantum circuit to estimate the gradient of the objective function, providing a richer and potentially more accurate signal for the classical Bayesian optimizer. Crucially, the algorithm incorporates adaptive query strategies, dynamically adjusting its exploration based on the observed interference effects within the quantum circuit. This synergy between classical Bayesian inference and quantum gradient estimation promises enhanced exploration and faster convergence compared to traditional BO methods. We detail the architecture of the proposed algorithm, outlining the quantum circuit design, the Bayesian optimization framework, and the adaptive query strategy. The theoretical analysis suggests a potential improvement in the exploration efficiency, particularly in high-dimensional search spaces. The algorithm's adaptability allows it to overcome challenges often encountered in BO, such as the "curse of dimensionality," leading to improved performance across a range of optimization problems. The method's effectiveness is demonstrated through a conceptual framework and analytical discussion, highlighting the key components and their interactions.
Bayesian Optimization (BO) has emerged as a powerful technique for optimizing black-box functions, particularly in scenarios with expensive evaluations. However, its performance often degrades in high-dimensional spaces due to the curse of dimensionality, leading to inefficient exploration of the search landscape. This work proposes a novel approach to BO that leverages the principles of quantum mechanics, specifically entanglement, to accelerate the optimization process. We introduce a framework where candidate solutions are represented as entangled quantum states. This representation inherently captures correlations between different parts of the search space, enabling a more focused and efficient exploration strategy. The core mechanism involves evaluating the objective function for these entangled states, allowing the algorithm to exploit the natural correlations. We demonstrate the efficacy of this quantum-inspired BO method through theoretical analysis and a conceptual illustration, showcasing its potential to outperform traditional BO methods in high-dimensional problems. The key innovation lies in translating the concept of quantum entanglement into a classical optimization framework, offering a fundamentally different approach to exploration and potentially unlocking significant performance gains.