Quantum computers have the potential to be exponentially faster than traditional computers, revolutionising the way we currently work. While we are still years away from general-purpose Quantum Computing, Bayesian Optimization can help to stabilise quantum circuits for certain applications. This blog will summarise how Mind Foundry Optimize did just that.
Black box optimization in machine learning is a pretty common scenario. More often than not, the process or model we are trying to optimize does not have an algebraic model that can be solved analytically.
Hyper-parameter tuning can be observed in many everyday processes, but few people are aware of its existence. This blog aims to change that, and demonstrate its true value in data science.
