Bristol team’s latest paper explores Quantum Speed-ups for numerical optimisation

The key to using quantum computers is their purported ability to compute certain algorithms faster than classical computers. However there are only a hand-full of algorithms which appear to show speed-up: For example, Grover or Shor’s algorithms. Researchers are not resting there and are actively looking to see whether other algorithms are amenable to Quantum Speed-up.

The fundamental component of deep-learning: Gradient Descent. Could Gradient Descent work effectively on Quantum Computers?

The paper from Bristol title “Quantum speedups of some general-purpose numerical optimisation algorithms” explores other (other than Shor, Grover etc.) algorithms such as Quantum versions of the Gradient Descent algorithm which might offer advantages for many processes especially deep learning. The quest is to find more uses for Quantum computing and more algorithms. Read the paper for more details.