Quantum computing, with its quantum parallelism, excels in tasks like factorization and unordered search, offering exponential speed-ups. However, integrating classical and quantum computing for broader computational acceleration poses challenges. This paper explores the current state of quantum machine learning (QML), benchmarking the performance of classical and quantum algorithms on various tasks. QML employs hybrid methods, combining classical algorithms with quantum techniques such as tree tensor networks and neural network-based quantum tomography to analyze quantum states and enhance classical data science algorithms. These novel approaches, demonstrated on quantum processors, show promise in predicting quantum states while mitigating noise. Despite significant hardware and software challenges, QML holds substantial potential, particularly in areas such as unsupervised learning and generative models. Future research aims to develop new quantum learning models that leverage quantum mechanics to overcome the limitations of classical machine learning.
Additional resources: https://arxiv.org/abs/1906.10175