Solving machine learning optimization problems using quantum computers
This addresses the problem of computational inefficiency in machine learning for researchers and practitioners, but it appears incremental as it builds on existing quantum parallelism concepts.
The paper tackles the slow computation and high resource demands of classical optimization in machine learning by proposing a generic mathematical model that leverages quantum parallelism to speed up algorithms, with an application to a 3-dimensional time-varying image.
Classical optimization algorithms in machine learning often take a long time to compute when applied to a multi-dimensional problem and require a huge amount of CPU and GPU resource. Quantum parallelism has a potential to speed up machine learning algorithms. We describe a generic mathematical model to leverage quantum parallelism to speed-up machine learning algorithms. We also apply quantum machine learning and quantum parallelism applied to a $3$-dimensional image that vary with time.