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Ubc theses and dissertations, applications of classical and quantum machine learning for quantum problems dai, jun --> -->.
This thesis investigates applications of classical machine learning to quantum problems and the possibilities of combining machine learning and quantum computing to improve algorithms for solving quantum problems. In quantum physics and quantum chemistry, the high dimensionality of quantum problems poses a significant challenge. Due to the increased complexity of such problems, traditional algorithms may not solve them effectively. However, new insights and better computational methods have become possible with the development of machine learning methods. This thesis aims to develop new methods based on classical and quantum machine learning methods applied to quantum problems. The first part of the thesis shows how Bayesian machine learning can be applied to quantum research when the number of calculations is limited. To be more specific, I construct accurate global potential energy surfaces for polyatomic systems by using a small number of energy points and demonstrate methods to improve the accuracy of quantum dynamics approximations with few exact results. The second part of the thesis looks into combining machine learning and quantum computing to improve machine learning algorithms. I demonstrate the first practical application of quantum regression models and use the resulting models to produce accurate global potential energy surfaces for polyatomic molecules. Furthermore, I illustrate the effect of qubit entanglement for the resulting models. In addition, I propose a quantum-enhanced feature mapping algorithm that is proven to have a quantum advantage for specific classically unsolvable classification problems and is more computationally efficient than previous methods. Finally, I highlight the potential for combining machine learning and quantum computing to improve algorithms for solving quantum problems.
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- ubc_2023_november_dai_jun.pdf -- 8.04MB
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Permanent URL: https://dx.doi.org/10.14288/1.0433722
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A QUANTUM ALGORITHM FOR TRAINING DEEP NEURAL NETWORKS 1A. Zlokapa and S. Lloyd, A quantum algorithm for training wide and deep neuralnetworks,inpreparation. 1.1 Introduction While deep neural networks have achieved state-of-the-art results in numer-ous relevant problems, the computational requirements of deep learning are
with the large and complex datasets. In this context, Quantum Machine Learning (QML) is emerging as a field of interest in computer science with the intersection of quantum computing and machine learning. Quantum computers are fundamentally different from classical computers as principles of quantum mechanics are used for information process-ing.
This thesis aims to develop new methods based on classical and quantum machine learning methods applied to quantum problems. The first part of the thesis shows how Bayesian machine learning can be applied to quantum research when the number of calculations is limited.
The field ofquantum machine learning (QML) explores how quantum computers can be used to more efficiently solve machine learning problems. As an application of hybrid quantum-classical algorithms, it promises a potential quantum advantages in the near term. In this thesis, we use the ZXW-calculus to diagrammatically analyse
Quantum computing is widely thought to provide exponential speedups over classical algorithms for a variety of computational tasks. In classical computing, methods in artificial intelligence such as neural networks and adversarial learning have enabled drastic improvements in state-of-the-art performance for a variety of tasks.
seen a growing interest in applying machine learning techniques. With the rise of quantum computing, it is of interest to explore whether quantum machine learning can offer advantages over classical methods for time series forecasting. This thesis presents the first large-scale systematic benchmark comparing classical and quan-