Motivated by the potential performance gains offered by quantum computing (QC), a recent research focus — known as quantum-assisted machine learning (QAML) — aims to develop machine learning algorithms tailored for execution on quantum computers while processing data that originate from a classical (i.e. non-quantum) phenomenon. QAML has been studied in various quantum computing (QC) paradigms. This paper, in particular, offers a brief overview of QAML within the context of adiabatic quantum computing (AQC). The major challenge in this case is to reformulate the machine learning task as a quadratic unconstrained binary optimization (QUBO) problem, which lies at the core of existing AQC physical implementations. We provide examples of machine learning techniques tailored to AQC, along with applications in remote sensing.
Motivated by the potential performance gains offered by quantum computing (QC), a recent research focus — known as quantum-assisted machine learning (QAML) — aims to develop machine learning algorithms tailored for execution on quantum computers while processing data that originate from a classical (i.e. non-quantum) phenomenon. QAML has been studied in various quantum computing (QC) paradigms. This paper, in particular, offers a brief overview of QAML within the context of adiabatic quantum computing (AQC). The major challenge in this case is to reformulate the machine learning task as a quadratic unconstrained binary optimization (QUBO) problem, which lies at the core of existing AQC physical implementations. We provide examples of machine learning techniques tailored to AQC, along with applications in remote sensing.
This paper introduces a novel quantum-enhanced machine learning framework, termed "Quantum Adaptive Machine Learning" (QAML), leveraging quantum self-adaptation to enhance the performance of various machine learning algorithms. Traditional machine learning often struggles with complex, high-dimensional data, requiring extensive parameter tuning. QAML addresses this challenge by employing quantum self-adaptation, a quantum mechanical phenomenon allowing the system to dynamically adjust its parameters based on the data itself. We detail the framework's architecture, explain the core mechanisms driving its enhanced performance, and provide a comparative analysis with existing techniques. The study demonstrates significant improvements in accuracy and efficiency across several benchmark datasets, highlighting the potential of this approach for tackling complex machine learning tasks.
A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to [Formula: see text] speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric. The quantum approach used the D-Wave 2000Q adiabatic quantum computer, whereas the classical approach used a desktop workstation with an 8-core Intel i9 processor. As such, the results obtained in this work must be interpreted within the context of the specific hardware and software implementations of these machines.
Quantum Adiabatic Machine Learning (QAML) leverages principles of adiabatic quantum computation to optimize machine learning tasks such as plant disease classification. By encoding the problem into quantum Hamiltonians and using adiabatic evolution, QAML explores a vast solution space efficiently. This method uses quantum concepts such as entanglement to navigate complex energy landscapes that classical approaches find challenging. The framework applies both theoretical and practical insights to address high-dimensional data issues, essential for identifying plant diseases accurately and efficiently. We show that QAML optimizes classification tasks by encoding plant disease detection into an Ising-model-inspired Hamiltonian. The adiabatic process retains optimal configurations via multi-qubit entanglement while mitigating decoherence effects. Furthermore, we mathematically demonstrate QAML’s resilience to noise in open quantum systems and its potential quantum advantage over classical methods. This approach promises advancements in agricultural diagnostics and quantum-enhanced learning.
Quantum Adaptive Machine Learning (QAML) presents a novel approach to machine learning by integrating quantum computing's inherent adaptability with traditional machine learning paradigms. This paper explores the development of a quantum-enhanced self-adaptive learning algorithm designed to dynamically adjust model parameters based on both data characteristics and the learning process itself. We propose a mechanism leveraging quantum computation to optimize model performance through continuous parameter refinement. The core claim is that this algorithm significantly enhances the model's generalization ability by responding to evolving data and learning dynamics.
Machine learning is a branch of Artificial Intelligence that seeks to make machines learn from data. It is being applied for solving real world problems with huge amount of data. Though, Machine Learning is receiving wide acceptance, however, execution time is one of the major concerns in practical implementations of Machine Learning techniques. It largely comprises of a set of techniques that trains a model by reducing the error between the desired or actual outcome and an estimated or predicted outcome, which is often called as loss function. Thus, training in machine learning techniques often requires solving a difficult optimization problem, which is the most expensive step in the entire model-building process and its applications. One of the possible solutions in near future for reducing execution time of training process in Machine learning techniques is to implement them on quantum computers instead of classical computers. It is conjectured that quantum computers may be exponentially faster than classical computers for solving problems which involve matrix operations. Some of the machine learning techniques like support vector machines make extensive use of matrices, which can be made faster by implementing them on quantum computers. However, their efficient implementation is non-trivial and requires existence of quantum memories. Thus, another possible solution in near term is to use a hybrid of Classical Quantum approach, where a machine learning model is implemented in classical computer but the optimization of loss function during training is performed on quantum computer instead of classical computer. Several Quantum optimization algorithms have been proposed in recent years, which can be classified as gradient based and gradient free optimization techniques. Gradient based techniques require the nature of optimization problem being solved to be convex, continuous and differentiable otherwise if the problem is non-convex then they can find local optima only whereas gradient free optimization techniques work well even with non-continuous, non-linear and nonconvex optimization problems. This chapter discusses a global optimization technique based on Adiabatic Quantum Computation (AQC) to solve minimization of loss function without any restriction on its structure and the underlying model, which is being learned. Further, it is also shown that in the proposed framework, AQC based approach would be superior to circuit-based approach in solving global optimization problems.
Machine Learning (ML) models are trained using historical data to classify new, unseen data. However, traditional computing resources often struggle to handle the immense amount of data, commonly known as Big Data, within a reasonable time frame. Quantum Computing (QC) provides a novel approach to information processing, offering the potential to process classical data exponentially faster than classical computing through quantum algorithms. By mapping Quantum Machine Learning (QML) algorithms into the quantum mechanical domain, we can potentially achieve exponential improvements in data processing speed, reduced resource requirements, and enhanced accuracy and efficiency. In this article, we delve into both the QC and ML fields, exploring the interplay of ideas between them, as well as the current capabilities and limitations of hardware. We investigate the history of quantum computing, examine existing QML algorithms, and present a simplified procedure for setting up simulations of QML algorithms, making it accessible and understandable for readers. Furthermore, we conduct simulations on a dataset using both traditional machine learning and quantum machine learning approaches. We then compare their respective performances by utilizing a quantum simulator.