This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry. Our approach utilizes the Fourier series method to represent the hyperparameter search space, which is then optimized using quantum algorithms to find the optimal set of hyperparameters for a given machine learning model. Our study evaluates the proposed method on a benchmark of models trained to predict a forecast problem in the airline industry using a standard HyperParameter Optimizer (HPO). The results show that our approach outperforms traditional hyperparameter optimization methods in terms of accuracy and convergence speed for the given search space. Our study provides a new direction for future research in quantum-based machine learning hyperparameter optimization.
This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry. Our approach utilizes the Fourier series method to represent the hyperparameter search space, which is then optimized using quantum algorithms to find the optimal set of hyperparameters for a given machine learning model. Our study evaluates the proposed method on a benchmark of models trained to predict a forecast problem in the airline industry using a standard HyperParameter Optimizer (HPO). The results show that our approach outperforms traditional hyperparameter optimization methods in terms of accuracy and convergence speed for the given search space. Our study provides a new direction for future research in quantum-based machine learning hyperparameter optimization.
Time series forecasting is a valuable tool for many applications, such as stock price predictions, demand forecasting or logistical optimization. There are many well-established statistical and machine learning models that are used for this purpose. Recently in the field of quantum machine learning many candidate models for forecasting have been proposed, however in the absence of theoretical grounds for advantage thorough benchmarking is essential for scientific evaluation. To this end, we performed a benchmarking study using real data of various quantum models, both gate-based and annealing-based, comparing them to the state-of-the-art classical approaches, including extensive hyperparameter optimization. Overall we found that the best classical models outperformed the best quantum models. Most of the quantum models were able to achieve comparable results and for one data set two quantum models outperformed the classical ARIMA model. These results serve as a useful point of comparison for the field of forecasting with quantum machine learning.
Quantum Machine Learning (QML) offers the potential to revolutionize various computational tasks, particularly in optimization problems. This paper explores the application of QML to adaptive hyperparameter optimization, a crucial step in training machine learning models. We propose a novel QML-based algorithm that leverages quantum state dynamics to automatically adjust hyperparameters, leading to improved model performance. This approach addresses the limitations of traditional static hyperparameter optimization methods by dynamically responding to the data and model itself. The core mechanism involves the quantum state evolution driven by the learning process, enabling a more efficient and robust optimization process. This work demonstrates the potential of QML to significantly enhance model accuracy and generalization capabilities.
Quantum-Enhanced Machine Learning Model Optimization explores the potential of leveraging quantum algorithms to dramatically improve the training process of machine learning models, particularly in parameter optimization and hyperparameter selection. This paper investigates the application of quantum algorithms, specifically quantum approximate optimization algorithms (QAOA), to accelerate the training of complex models. The core claim is that utilizing quantum computing will yield substantial improvements in model performance and efficiency compared to traditional machine learning techniques. The research will detail the process of applying QAOA to a specific model type and will present results demonstrating the potential for enhanced training speed and improved model accuracy. This work offers a novel approach to optimizing machine learning models using quantum principles.
The rapid growth of machine learning (ML) has been accompanied by a significant challenge: the scaling of optimization algorithms to handle the immense parameter spaces of modern models, such as deep neural networks. Traditional optimization methods, including gradient descent and its variants, often encounter difficulties in efficiently exploring these spaces, leading to slow training times and potential stagnation. This paper investigates the application of quantum-inspired optimization techniques to address this scaling problem. We explore algorithms inspired by quantum annealing and other quantum phenomena, adapting their core principles to classical computation. Specifically, we propose a novel optimization framework utilizing a stochastic search strategy based on quantum superposition and measurement, designed to navigate high-dimensional parameter spaces more effectively. The efficacy of this approach is evaluated through theoretical analysis and conceptual simulations, demonstrating its potential to accelerate the training of large-scale machine learning models. We demonstrate how these methods can improve convergence speed and potentially escape local minima more effectively than conventional techniques. The core contribution lies in providing a theoretical foundation and a practical framework for applying quantum-inspired concepts to optimization within the context of machine learning.
This paper investigates the application of quantum annealing to the combinatorial optimization problem inherent in neural network training, specifically focusing on hyperparameter tuning. Traditional methods for hyperparameter optimization, such as grid search and random search, can be computationally expensive and often fail to efficiently explore the vast hyperparameter space. We propose a novel approach that maps the hyperparameter optimization task onto a quantum annealing landscape. The quantum annealer, leveraging quantum tunneling, is then utilized to navigate this landscape and identify optimal hyperparameter values. This approach aims to accelerate the training process and enhance the performance of neural networks. The core concept involves formulating the hyperparameter tuning problem as an Ising model, which can be directly implemented on quantum annealing hardware. We explore the theoretical underpinnings of this mapping and discuss the potential advantages and limitations of this method. The results, although not based on experimental validation due to the nature of the research, demonstrate a promising trajectory for integrating quantum computing with machine learning. Further research and development are required to fully realize the potential of this approach.
Abstract As restricted quantum computers become available, research focuses on finding meaningful applications. For example, in quantum machine learning, a special type of quantum circuit called a quantum neural network is one of the most investigated approaches. However, we know little about suitable circuit architectures or important model hyperparameters for a given task. In this work, we apply the functional ANOVA framework to the quantum neural network architectures to analyze which of the quantum machine learning hyperparameters are most influential for their predictive performance. We restrict our study to 7 open-source datasets from the OpenML-CC18 classification benchmark, which are small enough for simulations on quantum hardware with fewer than 20 qubits. Using this framework, three main levels of importance were identified, confirming expected patterns and revealing new insights. For instance, the learning rate is identified as the most important hyperparameter on all datasets, whereas the particular choice of entangling gates used is found to be the least important on all except for one dataset. In addition to identifying the relevant hyperparameters, for each of them, we also learned data-driven priors based on values that perform well on previously seen datasets, which can then be used to steer hyperparameter optimization processes. We utilize these priors in the hyperparameter optimization method hyperband and show that these improve performance against uniform sampling across all datasets by, on average, 0.53% 0.53 % , up to 6.11% 6.11 % , in cross-validation accuracy. We also demonstrate that such improvements hold on average regardless of the configuration hyperband is run with. Our work introduces new methodologies for studying quantum machine learning models toward quantum model selection in practice. All research code is made publicly available.