The Shift Towards a Healthy Quantum Inspired Machine Learning Industry PDF 10/27/23.
The shift towards further developing existing quantum inspired machine learning workflows must be a top
priority for the field of quantum computing when compared to the exploratory phase of solving extra large
problems with quantum hardware. The practicality of QiML comes from leveraging ‘principles of quantum
mechanics within classical computational frameworks’.
The primary reasons for advancing QiML developments are A) There have been working models for several years, and B) Additional growth and compatibility has been experienced since the first iterations. (1) The key requirements for QiML technologies are a Python compatible quantum computing library, and high classical RAM considerations to process different pure quantum states correctly. (2) Other ML libraries can also be incorporated into notebooks for full classical and quantum ‘differentiability’ with PyTorch or TensorFlow interfaces.