Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that investigates how QC can enhance ML, making it one of the most exciting areas of research due to the potential of QC in solving complex problems. In this paper, we demonstrate the Quantum Annealing (QA) approach to improving ML in binary classification tasks. We implemented the QBoost algorithm on a few datasets using D-Wave’s quantum computers, specifically the Advantage 1 and Advantage 2 prototypes, incorporating the new feature Fast Anneal.
Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that investigates how QC can enhance ML, making it one of the most exciting areas of research due to the potential of QC in solving complex problems. In this paper, we demonstrate the Quantum Annealing (QA) approach to improving ML in binary classification tasks. We implemented the QBoost algorithm on a few datasets using D-Wave’s quantum computers, specifically the Advantage 1 and Advantage 2 prototypes, incorporating the new feature Fast Anneal.
The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called “Pegasus.” This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers has spurred experimental demonstrations and intensified research interest, particularly in quantum machine learning. However, the application of quantum annealing in machine learning remains limited due to the lack of conclusive performance evaluations on real-world datasets. There is still no clear consensus on the efficacy of these models in practical scenarios. This work focuses on experimentally evaluating quantum annealing machine learning (QAML) classification methods, specifically Quantum Support Vector Machines (QSVM) and QBoost, on ADMET datasets—one of the most important datasets in the drug discovery domain. We compare QAML with classical machine learning to evaluate their relative performance. This study seeks to address this gap by rigorously comparing the performance of QBoost and QSVM models using ADMET datasets, employing D-Wave’s Quantum Annealer. This work provides a comprehensive analysis of the potential and limitations of quantum annealers in quantum machine learning, with a focus on their application to real-world data in the ADMET domain. The findings offer critical insights into the nuanced advantages and challenges of quantum annealers in advancing machine learning methodologies.
Machine learning applications are limited by computational power. In this paper, we gain novel insights into the application of quantum annealing (QA) to machine learning (ML) through experiments in natural language processing (NLP), seizure prediction, and linear separability testing. These experiments are performed on QA simulators and early-stage commercial QA hardware and compared to an unprecedented number of traditional ML techniques. We extend QBoost, an early implementation of a binary classifier that utilizes a quantum annealer, via resampling and ensembling of predicted probabilities to produce a more robust class estimator. To determine the strengths and weaknesses of this approach, resampled QBoost (RQBoost) is tested across several datasets and compared to QBoost and traditional ML. We show and explain how QBoost in combination with a commercial QA device are unable to perfectly separate binary class data which is linearly separable via logistic regression with shrinkage. We further explore the performance of RQBoost in the space of NLP and seizure prediction and find QA-enabled ML using QBoost and RQBoost is outperformed by traditional techniques. Additionally, we provide a detailed discussion of algorithmic constraints and trade-offs imposed by the use of this QA hardware. Through these experiments, we provide unique insights into the state of quantum ML via boosting and the use of quantum annealing hardware that are valuable to institutions interested in applying QA to problems in ML and beyond.
Quantum computing offers new paradigms in machine learning, notably through Quantum Annealing (QA), which offers promising approaches to optimization problems. This study introduces an application of Multi-tasking Quantum Annealing (MTQA) to enhance multi-class Support Vector Machines (SVMs). Traditional QA methods require multiple cycles to classify multi-class datasets, significantly increasing computational demands. In contrast, MTQA embeds multiple SVM classifiers in parallel on a quantum annealer, thereby reducing the number of required annealing cycles to a single cycle. Utilizing the D-Wave Advantage 6.4 system, we evaluated our method across three benchmark datasets: a synthetic dataset (Blob), subsets of handwritten digits, and the Iris dataset with different feature focuses. Our findings demonstrate that MTQA achieves accuracy equivalent to that of classical Sequential Minimal Optimization (SMO), Simulated Annealing (SA), and standard QA methods while significantly improving computational efficiency. Despite the current limitations in quantum technology and the empirical selection of model parameters, these results highlight the emergent potential of quantum computing in machine learning. Further advancements in quantum algorithm optimization and parameter tuning are anticipated to enhance the efficacy of QA and MTQA, solidifying their practical application roles.
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.
At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades. With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.
Over the last couple of decades, there has been a significant evolution in machine learning (ML) and it has gained much popularity, as the volume of data grows exponentially and the adoption of ML algorithms brings new perspectives, trends, and patterns on the data which help in the process of decision making in businesses. Machine learning has transformed the way technical experts work; on the other hand, quantum computing is also becoming prevalent: a fast-growing technology that takes advantage of the laws of quantum mechanics to solve large problems which might be too complex for a classical computer to solve. Tech giants like IBM and Google have already built the first wave of quantum computers. Many research organizations, governments and universities are investing funds and researching in quantum computing, as quantum computers are expected to bring revolutionary changes and accelerate outcomes in various fields such as drug discovery, electronic materials, fertilizers, artificial intelligence (AI), and so on. A new initiative for research into the potential of quantum computers to expedite the learning process of ML algorithms has been inspired by different applications of machine learning and the possibilities of quantum computing. Multiple factors exist in the environment which can affect the performance and accuracy of quantum computers. The Earth’s magnetic field can have an impact on quantum computers since they are susceptible to noise, such as Wi-Fi or mobile phone radiation. Likewise, weak machine learning algorithms can produce inaccurate results; their predictions similar to mere human guessing. Applying machine learning algorithms in quantum computers achieves speedup in the learning process. A weak inaccurate algorithm can be improved by an accurate machine learning algorithm accompanied by boosting. The interplay or trade-off between noisy quantum computers and quantum machine learning algorithms is an interesting research field to explore. For example, assume A is a quantum machine learning (QML) algorithm with a theoretically excellent performance. However, A’s performance is poor when used with a noisy quantum computer; this is because only slightly more than half of the inputs result in A’s output being correct. Can we make A perform better, so that its output is correct on two-thirds of the inputs? Boosting algorithms work on this aspect.