Challenges and Opportunities in Quantum Machine Learning

Quantum computing exploits entanglement, superposition, and interference to perform certain tasks with significant speedups over classical computing, The recognition that the world is quantum mechanical sometimes even exponentially faster. Indeed while such has allowed researchers to embed well-established, but speedup has already been observed for a contrived problem classical, theories into the framework of quantum Hilbert [7], reaching it for data science is still uncertain even spaces. Shannon's information theory, which is the basis at the theoretical level, but this is one of the main goals of communication technology, has been generalized for QML. to quantum Shannon theory (or quantum information theory), opening up the possibility that quantum effects In practice, QML is a broad term that encompasses all could make information transmission more efficient [1]. of the tasks shown in Figure 1. For example, one can apply The field of biology has been extended to quantum biology machine learning to quantum applications like discovering to allow for a deeper understanding of biological quantum algorithms [8] or optimizing quantum experiments processes like photosynthesis, smell, and enzyme catalysis [9, 10], or one can use a quantum neural network [2]. Turing's theory of universal computation has been to process either classical or quantum information [11].