SPIE Applications of Machine Learning

Boosting quantum optics experiments with Bayesian optimization (Conference Presentation)

Philipp‐Immanuel Schneider, Lin Zschiedrich, Martin Hammerschmidt, Lilli Kuen, Ivan Sekulić, Julien Kluge, Bastian Leykauf, Markus Krutzik, Sven Burger

Published Oct 3, 2022• 0 citations

Publisher

Abstract

Manual optimization of experimental parameters can quickly become too complex and time-consuming if more than a few correlated parameters need to be adjusted. We discuss automating this process using Bayesian optimization. This machine learning-based method is particularly suitable because it can handle noisy measurements, performs a global search and requires relatively few experimental runs. We discuss the efficient, scalable implementation of Bayesian optimization, present practical applications for tuning experimental parameters, and compare it with other local and global heuristic methods to show its application range.

DOI: 10.1117/12.2632419

Related papers

Quantum Machine Learning: A Bayesian Optimization-Based Algorithm for Automated Model Parameter Tuning

Jincheng Zhang

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.

Sep 4, 20260 citations

Advances in Bayesian optimization for photonics and quantum atom optics applications

Philipp‐Immanuel Schneider, Xavier Garcia‐Santiago, Benjamin Wiegand +4 more

Bayesian optimization is an efficient numerical tool. We review approaches to improve its scalability and to handle noisy inputs, and we demonstrate applications in pho- tonics design optimization and in control of quantum experiments.

OSA Advanced Photonics CongressJan 1, 20211 citation

Title: Dynamically Altering Quantum Circuit Parameters via Bayesian Optimization

Jincheng Zhang

Quantum circuit design is a complex and computationally intensive process, often requiring extensive trial and error. Traditional methods rely on manually defining and adjusting circuit parameters, limiting the potential for optimization. This paper introduces a novel approach utilizing Bayesian optimization to dynamically alter quantum circuit parameters in real-time, addressing the limitations of static parameter design. We propose a framework where the system's output serves as the objective function for Bayesian optimization, iteratively refining circuit parameters to achieve desired performance characteristics. This approach offers a more robust and adaptive method compared to traditional techniques, potentially unlocking significant improvements in quantum computation. The core mechanism leverages the inherent adaptability of biological systems to guide the optimization process, providing a foundation for a more efficient and effective design methodology. This research explores the potential for leveraging Bayesian optimization to enhance the design and execution of quantum circuits, leading to improved performance and reduced computational cost.

Sep 7, 20260 citations

Title: Quantum-Enhanced Bayesian Inference for Machine Learning

Jincheng Zhang

Quantum-enhanced Bayesian inference offers a paradigm shift in the field of machine learning, addressing the fundamental limitations of classical Bayesian methods – namely, computational complexity and scalability – when dealing with high-dimensional data. This paper presents a novel quantum-enhanced Bayesian inference algorithm designed to drastically reduce these challenges. We explore the use of quantum annealing and quantum approximate optimization algorithms to efficiently navigate the complex search space of model parameters, leading to significantly improved accuracy and reduced computational costs. This work demonstrates the potential of quantum computing to revolutionize machine learning, particularly within domains characterized by high data complexity and demanding performance requirements. The core mechanism focuses on leveraging quantum phenomena – superposition and entanglement – to accelerate the exploration of parameter space and improve the convergence of inference algorithms. The investigation includes a detailed analysis of the algorithm's performance compared to classical methods, highlighting the benefits of quantum acceleration in specific machine learning tasks.

Aug 31, 20260 citations

Quantum-Enhanced Bayesian Optimization for Complex Model Selection

Jincheng Zhang

Quantum-enhanced Bayesian optimization (QEBO) offers a promising approach to accelerate the model selection process, particularly for complex models, by harnessing quantum mechanics for enhanced efficiency in parameter space exploration. This paper proposes a novel QEBO algorithm utilizing quantum state estimation to rapidly approximate the fitness of model configurations, thereby reducing computational cost and improving the selection of optimal models. We introduce a quantum-enhanced Bayesian optimization framework, focusing on the exploitation of quantum entanglement and superposition to efficiently map the parameter space. The core mechanism involves quantum state estimation to quickly assess the suitability of each model configuration, significantly reducing the search time compared to classical methods. This paper details the theoretical foundation, algorithm implementation, and preliminary experimental results demonstrating the potential of QEBO to outperform traditional Bayesian optimization in complex model selection scenarios.

Sep 1, 20260 citations

Quantum-Inspired Bayesian Optimization with Amplitude Amplification

Jincheng Zhang

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.

Sep 4, 20260 citations