Quantum vs Classical ML: A Performance Evaluation

Revealing the future: A Full performance evaluation of Quantum vs Classical Machine Learning

The goal of the research field known as quantum ML (QML) is to include quantum algorithms in machine learning initiatives. It improves computer performance and data storage by utilizing the capabilities of quantum computing and quantum physics, frequently combining both classical and quantum processing.

In the 1950s and 1960s, pattern recognition was the foundation of classical ML, which advanced with additional data. These algorithms are widely used and are based on probabilistic reasoning and statistics. Traditional machine learning relies more on human input to learn and typically needs more organized data.

An analysis of the benefits and drawbacks of using quantum computers and algorithms for machine learning tasks, including classification, regression, clustering, and generative modeling, is presented in the subject Quantum vs. Classical ML: A Performance Evaluation. The goal of the developing discipline of quantum machine learning (QML) is to improve the expressiveness and efficiency of machine learning models by making use of quantum phenomena like superposition, entanglement, etc. When compared to traditional machine learning (CML) models, QML may be faster, utilize less memory, and have better accuracy. Noise, scalability, complexity, hardware constraints, and noise are some of the major issues that QML must overcome.

The effectiveness of QML and CML models has been benchmarked in several studies using a range of datasets and applications, including natural language processing, physics, chemistry, and finance. The comparison depends on several aspects, including the amount and complexity of the data, the number of parameters, the choice of quantum gates, the optimization technique, and the evaluation metrics. As a result, the results are not definitive. While some QML models have demonstrated equivalent or worse performance than CML models on specific tasks, some have shown higher performance. It is crucial to ascertain the optimal situations and approaches for implementing QML in practice as well as to thoroughly examine the trade-offs and constraints of QML and CML models.

The following references might provide you with further information on this subject:

Machine Learning: Quantum vs Classical: In addition to discussing the technological advancements, parallels, and discrepancies between the research conducted in both fields, this paper gives an overview of QML and CML. It also examines the complexity and recent developments of various QML techniques, as well as their applicability across a range of industries.

Classical versus Quantum Models in Machine Learning: Insights from a Finance Application: The restricted Boltzmann machines (RBMs) CML model, which is frequently used, is compared in this work to the quantum circuit Born machines (QCBMs) QML model, which was recently presented. Exploiting the probabilistic aspect of quantum mechanics, QCBMs tackle the same challenging issues in unsupervised generative modeling. QCBMs outperform RBMs in most cases, as demonstrated by the study, which employs scenarios from a probabilistic variant of the banking industry's portfolio optimization issue.

Open Access Proceeds Journal of Physics: Conference Series: Using a dataset of breast cancer patients, this research evaluates the performance of three CML models and four QML models on a binary classification issue. Using common assessment metrics including accuracy, precision, recall, and F-score, the article demonstrates that the QML model with 100 epochs built on EfficientSU2 outperformed all other models.

Machine Learning: Quantum vs Classical – Academia.edu: In this work, a quantum annealer-based QML model for the traveling salesman problem (TSP) is presented. Using a dataset of ten cities, the study demonstrates that QML models such as simulated annealing and evolutionary algorithms are more rapid and accurate in locating the best solution than CML models.