Quantum computers and machine learning are on the forefront of research in both science and engineering. Quantum computers offer significant computational speed-ups in certain tasks. Machine learning on the other hand requires a lot of computational power and is heavily used by both governments and businesses. Thus it is no wonder that the possibility of leveraging the computational efficiency of quantum computers for machine learning tasks is attractive. This thesis focuses on decision trees models, due to their interpretability and wide use cases. This thesis tries to answer the question, how can quantum technology be leveraged to improve the performance and construction of decision trees. The thesis will explore the full implementation of decision trees in quantum circuits as quantum decision trees and their viability in modern hardware and its future relevance. Quantum algorithms for construction and retraining of classical decision trees will also be explored and compared to classical methods. The work found that quantum decision trees show comparable performance to classical decision trees when applied to simple data sets. However, due to hardware limitations quantum decision trees suffer when applied to more complex data sets. Quantum algorithms for tree construction show significant theoretical speed-ups at larger models when compared to classical methods of tree construction.
Quantum computers and machine learning are on the forefront of research in both science and engineering. Quantum computers offer significant computational speed-ups in certain tasks. Machine learning on the other hand requires a lot of computational power and is heavily used by both governments and businesses. Thus it is no wonder that the possibility of leveraging the computational efficiency of quantum computers for machine learning tasks is attractive. This thesis focuses on decision trees models, due to their interpretability and wide use cases. This thesis tries to answer the question, how can quantum technology be leveraged to improve the performance and construction of decision trees. The thesis will explore the full implementation of decision trees in quantum circuits as quantum decision trees and their viability in modern hardware and its future relevance. Quantum algorithms for construction and retraining of classical decision trees will also be explored and compared to classical methods. The work found that quantum decision trees show comparable performance to classical decision trees when applied to simple data sets. However, due to hardware limitations quantum decision trees suffer when applied to more complex data sets. Quantum algorithms for tree construction show significant theoretical speed-ups at larger models when compared to classical methods of tree construction.
Quantum Machine Learning (QML) has emerged as a promising field, offering the potential to revolutionize various computational tasks, including machine learning. This paper investigates the application of QML to decision tree algorithms, specifically focusing on the development of a novel non-linear decision tree model designed for optimized decision-solving. We explore how quantum computation can enhance the optimization process inherent in decision tree construction, leading to improved efficiency and accuracy. The core claim is that leveraging quantum mechanics allows for automatic parameter adjustment within the decision tree, significantly increasing its effectiveness. This work introduces a framework where quantum algorithms are employed to refine the decision tree structure, addressing limitations of traditional methods. The research also investigates the potential benefits of this approach in complex decision scenarios, highlighting the advantages over conventional techniques. The paper concludes with a discussion of future research directions and the expected impact of QML on the field of decision tree learning.
Currently, pattern recognition is an extremely important activity in several fields.Approaches using artificial intelligence are one of the means of detecting these patterns and, therefore, optimizations in these methods are essential for such recognition to be carried out efficiently, both in processing time and in the hit rate.The field of machine learning, one of the paradigms of artificial intelligence, dates back to the beginnings of modern computing and in recent decades has been used intensively in pattern recognition.Despite the technical challenges related to the hardware, quantum computing has also been studied for a better performance of multiple computational tasks that are often unfeasible to be performed by classical algorithms.In this context, one of the fields that gain focus is the use of quantum circuits in machine learning algorithms.Thus, this work aims to compare the performance and complexity of classical algorithms, used in machine learning, to their quantum and hybrid versions, in order to identify the most efficient implementations.Another aspect to highlight is the feasibility of applying such models at certain scales, taking into account the limitations of current hardware.This will be done through a review of current academic production, followed by the development, implementation, testing, and analysis of the performance and complexity of both approaches.It is expected with this experiment that it will be possible to verify the effective possibility of using machine learning algorithms in their hybrid versions, either in current cases or in the near future.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.
Quantum computing is revolutionizing artificial intelligence (AI) by offering exponential speed-ups for complex computations. Quantum computing differs from classical computing, which uses bits leverages qubits that enable parallelism and superposition. This advancement enhances AI activities including pattern detection, optimization, and deep learning. By integrating quantum computing into AI, researchers aim to achieve superior computational power, driving breakthroughs in fields like healthcare, finance, and cryptography. In AI applications, machine learning techniques are essential., and their efficiency can be significantly improved using quantum computing. Linear Regression predicts continuous values, Random Forest Regression enhances prediction robustness by averaging multiple decision trees, and Support Vector Machines classify data by finding optimal hyperplanes. Quantum computing accelerates these algorithms by reducing computational complexity and improving model accuracy. Key input parameters for evaluating quantum AI performance include Quantum Bit Processing Speed (GHz), which determines data processing rate, Qubit Coherence Time (µs), which affects stability, and Quantum Gate Fidelity (%), which measures the accuracy of quantum operations. These parameters directly influence AI model efficiency and reliability. The AI Performance Score serves as a comprehensive evaluation metric, assessing computational efficiency, prediction accuracy, and overall robustness of quantum AI models. Keywords: Quantum Computing, Machine Learning, Qubit Coherence Time, AI Performance Score
This paper investigates the application of quantum machine learning to dynamic decision tree models, aiming to enhance optimization processes and achieve efficient planning. Traditional decision trees often rely on fixed parameters, limiting their adaptability to complex scenarios. We propose a novel approach utilizing quantum computing to dynamically adjust decision tree parameters, fostering improved accuracy and efficiency. The core mechanism centers around leveraging quantum properties to guide the tree's growth and refinement, offering a significant advancement over conventional methods. This work explores the potential of quantum-enhanced decision tree optimization for various applications, including complex logistics and resource allocation.