Quantum machine learning is expected to be the principle application of quantum computing. Exact expectation values, pure quantum states, and efficient differentiation methods allow for quantum algorithm implementations into existing Healthcare ML workflows. 1, 2, 3, 4, 5.
The resources in this repository represent a collection of medical and quantum machine learning algorithm prototypings, parameter studies, and efficiency tests using GPUs and CPUs.
The goal is to implement a new variational quantum algorithm architecture with more trainable parameters into models for better performance, analogous to previous classical deep learning advancements.
References:
- Wikipedia contributors. (2023c). Quantum machine learning. Wikipedia. https://en.m.wikipedia.org/wiki/Quantum_machine_learning
- Virtue Market Research. (n.d.). Quantum Machine Learning Market | Size, share, growth | 2023 – 2030. Virtue Market Research. https://virtuemarketresearch.com/report/quantum-machine-learning-market
- Stanwyck, S. (2023c, September 12). Quantum Boost: CuQuantum, PennyLane Let simulations ride supercomputers | NVIDIA Blogs. NVIDIA Blog. https://blogs.nvidia.com/blog/2023/09/12/quantum-supercomputers-pennylane/
- Pires, F. (2023b, May 3). Single-GPU systems will beat quantum computers for a while: research. Tom’s Hardware. https://www.tomshardware.com/news/research-single-gpu-systems-will-continue-to-beat-quantum-computers-for-a-while
- Huynh, L. (2023b, August 22). Quantum-Inspired Machine Learning: a Survey. arXiv.org. https://arxiv.org/abs/2308.11269