Effective Quantum ML Algorithm Implementations for Medical R&D PDF + Discussion 1/25/24.
Achieving Quantum-inspired Algorithm performance improvements over traditional methods has served as the foundation for QiML applications today. In specific, Moore, M., Narayanan, A.; and Han, K-H., Kim, J-H. iterated on Quantum-inspired genetic algorithms or Parallel quantum-inspired genetic algorithms for combinatorial optimization problems as early as 1995.
Quantum evolutionary algorithms, Quantum delta Particle swarm optimization algorithms, and Quantum ant colony optimization algorithms were also developed, with several cases of progression over traditional algorithms. Also, in 2020, Ross, O. provided a review of "Quantum-Inspired Metaheuristics" which are a more abstract class of quantum algorithms that can be used in multiple scenarios.
From 2006 to current, Quantum-inspired Machine Learning has been building on the quantum algorithm advancements, but now with larger models and trainable parameters. The 2014 Wittek, et al. quantum data mining paper and lecture series provided an overview of where the technology is headed. Subsequent works have focused on areas such as Quantum deep learning, QML Classifiers, and Trainability subtleties.
In 2024 QiML discussions continue to focus on techniques to help run larger circuits on classical hardware - which include Circuit cutting, Tensor networks, and Quantum circuit neural networks. Lastly, leading organizations such as Terra Quantum, SandboxAQ, and BASF have committed to relying GPUs to further develop quantum circuit applications.