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The Evolution of Quantum Machine Learning Algorithms

The recent boom in machine learning (ML) and quantum computing has brought the promise of a breakthrough in artificial intelligence (AI). Quantum ML (QML) is an attempt to utilize the potential of both fields by combining the insights from ML with the capabilities of quantum computation. However, despite significant theoretical contributions and experiments using various datasets, there are many unanswered questions that prevent QML from reaching its full potential.

The most prominent of these questions concern the performance gap between classical and quantum ML algorithms. Although several studies have reported that quantum ML algorithms outperform their classical counterparts, the results often depend on the data. In general, the greater the complexity of the dataset, the larger the gap between classical and quantum models, as simple models tend to overfit. To overcome this issue, researchers have explored hybrid approaches to address the limitations of both classical and quantum ML by incorporating both methods into one model.

A notable example is the SVM-QSVM (Classical Support Vector Machine – Quantum Support Vector Machine) classifier developed by Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost, which uses both a Pauli operator for the data representation and a SVM kernel to find the hyperplane that separates the target class from the training set. This allows it to overcome the performance gaps of both classical and quantum technology website ML while performing better than SVM in most cases.

Since then, other researchers have developed a variety of hybrid quantum-classical ML algorithms with promising results. For example, an adiabatic QML algorithm that combines the feature selection of an SVM with the quantum iterative solver of an annealing algorithm can achieve the same performance as a fully quantum SVM classifier on some benchmark datasets. Furthermore, a new approach to quantum learning, called “Quantum Feature Selection”, leverages the power of both adiabatic and tensor decomposition by integrating both processes in a single framework.

While these hybrid quantum-classical models demonstrate the promise of QML, there are still many challenges to overcoming the performance gap between classical and quantum ML, including the development of more efficient algorithms, achieving a fault-tolerant regime in real quantum hardware, and providing easy access to qRAMs. Achieving these goals will enable a broader range of applications for quantum machine learning, such as neural networks and brain-inspired algorithms that take advantage of the natural interaction and unitary dynamic of quantum systems.

Until these barriers are techogle.co overcome, the field of quantum machine learning will continue to develop and evolve. In the future, it is expected to improve even further, allowing it to tackle tasks that are impractical for classical computer architectures. We look forward to continuing our work on this exciting frontier and helping make artificial intelligence a reality. This work was supported by BlueQubit.