Quantum computing is one of the most advanced technologies which surpasses other conventional approaches in solving complex computations. Evaluation of the system based on the Qubit Count, Gate Fidelity, Decoherence Time, and Computation Efficiency is important. Machine learning algorithms have been used effectively to model these variables. In this research, the importance of computation efficiency modeling within the context of quantum computing is emphasized. Besides, this research has shown some important considerations that influence the performance of the system. Moreover, it has demonstrated the possible application of machine learning algorithms in improving this variable. Support Vector Regression and Random Forest regression models were used in this study to examine the relationship between computation efficiency and other parameters of quantum computing. The performances of the models were evaluated statistically. From the results obtained, the Support Vector Regression model was much more effective than the Random Forest regression model. This is because it provided accurate predictions with negligible prediction errors. This study underscores the usefulness of Support Vector Regression in predicting computation efficiency within the context of quantum computing.
Quantum computing is one of the most advanced technologies which surpasses other conventional approaches in solving complex computations. Evaluation of the system based on the Qubit Count, Gate Fidelity, Decoherence Time, and Computation Efficiency is important. Machine learning algorithms have been used effectively to model these variables. In this research, the importance of computation efficiency modeling within the context of quantum computing is emphasized. Besides, this research has shown some important considerations that influence the performance of the system. Moreover, it has demonstrated the possible application of machine learning algorithms in improving this variable. Support Vector Regression and Random Forest regression models were used in this study to examine the relationship between computation efficiency and other parameters of quantum computing. The performances of the models were evaluated statistically. From the results obtained, the Support Vector Regression model was much more effective than the Random Forest regression model. This is because it provided accurate predictions with negligible prediction errors. This study underscores the usefulness of Support Vector Regression in predicting computation efficiency within the context of quantum computing.
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 study investigates the application of machine learning techniques for predicting gate depth in quantum computational systems, focusing on the relationship between quantum circuit parameters and computational performance. Using a dataset of 50 observations encompassing qubit count, gate depth, entanglement rate, and logical error rate, we developed and compared Linear Regression (LR) and Random Forest Regression (RFR) models for gate depth prediction. The research addresses critical challenges in quantum fault tolerance and circuit optimization, which are essential for achieving scalable quantum computation. Our analysis revealed strong negative correlation (-0.65) between qubit count and logical error rate, while gate depth showed moderate positive correlation (0.62) with error rates. The Random Forest model demonstrated superior performance with R² values of 0.96068 on training data and 0.87262 on testing data, significantly outperforming Linear Regression (R²: 0.87552 training, 0.60257 testing). Error metrics further confirmed RFR's superiority, achieving RMSE of 2.33 (training) and 2.97 (testing) compared to LR's 4.15 and 5.25 respectively. The study highlights the importance of quantum error correction mechanisms and the inherent trade-offs between circuit complexity and computational reliability. Both models showed expected performance degradation from training to testing, with RFR maintaining better generalization capabilities. These findings contribute to the development of typed models for quantum mechanics in computational applications, offering insights into quantum circuit optimization and fault-tolerant design. The research demonstrates that ensemble methods like Random Forest provide more robust predictions for quantum system parameters, supporting the advancement of practical quantum computing implementations and algorithm development. KeyWords: Quantum Computation, Gate Depth Prediction, Random Forest Regression, Quantum Error Correction, Machine Learning, Fault Tolerance.
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.
The paper presents an influence of machine learning techniques that can be efficiently solved using quantum computer. Machine learning is considered as a core concept of modeling and mining large dimensionality data and visualizes data instances as a vector. Support vector is one of the trending methods that use supervised learning for classifying the data efficiently irrespective of linearity or non-linearity in data. As the quantum computations already proved to be inherently parallel and can become one of the key factors in order to reduce the complexities while dealing with the classical machine learning model. The quantum machine learning takes advantage of parallelism and can explore the number of vectors and their dimensions in logarithmic time, and hence it can achieve the computational speedup. Now, the main contribution of the paper is to discuss quantum fundamental, quantum algorithm and then to review over classical equivalent quantum support vector machine along with their training algorithm and the comparative analysis between algorithmic growths. At last, the paper justifies the quantum support vector as a highly efficient model that gains the computational speedup and also shows the possibility of further improvement.
Quantum computing has the capability to efficiently solve complex problems that would require significantly longer processing times on classical computing systems. This study investigates the practical applications of quantum machine learning by comparing the performance of classical Support Vector Machines (SVM) and Quantum Support Vector Machines (QSVM) across three simulation-based quantum computing platforms: Qiskit, Cirq, and Pennylane. Through experiments conducted on various datasets, the study examines the influence of different feature mapping techniques—ZFeatureMap, ZZFeatureMap, and Pauli-FeatureMap—on the encoding of data into quantum states within QSVM models. The results demonstrate that both the choice of computational environment and the feature mapping strategy play a crucial role in determining the performance of quantum machine learning applications. The findings underscore the potential advantages of quantum-based approaches for tackling complex computational problems.