Quantum Machine Learning

As its name states, that's a research/application topic that interconnects ideas/projects from QC and ML. This connection QC/ML can work both ways: QC <-> ML

1. QC -> ML: QC can help by speeding up the time taken in the typical ML pipeline of training/evaluating/inferencing a/on a model.
2. ML -> QC: ML can help on solving current problems/difficulties in the QC realm of research such as develop/explore new Quantum-based algorithms, solve noise/ECC (error-correcting codes) problems in current Quantum HW etc.

Libraries

• Quiskit

• Pennylane (XanaduAI) Github A cross-platform Python library for differentiable programming of QC. Works with Pytorch and TF.

• Strawberry Fields (XanaduAI) Python library for designing, simulating, and optimizing continuous-variable quantum optical circuits.

• Exaq ai

  • Poliadic QML