Monte Carlo insight
The description of Monte Carlo methods given in the article was revelatory.
I have devised algorithms based on Monte Carlo and permutation methods to meet my specific needs in context of statistical analysis relating to biomedical data.The technique holds no mysteries but the idea that one is drawing samples from 'answer space' places it in different perspective. In other words, Monte Carlo methods are the poor man's quantum computing. One does not need to explore and catalogue the entire 'solution space' to get reliable answers.
Linear computation though advances in conventional electronics is becoming increasingly powerful with respect to speed and capacity for handling data. One such machine can run a sequence of Monte Carlo simulations with rapidity. An array of physical parallel processors offers prospect of increasing the number of 'solution space' computations in a given time period in linear proportion to the number of processors added to the array. Distributing a Monte Carlo task across processors in a single location or a set of networked devices is becoming established procedure.
Perhaps there are problems only QC can satisfactorily elucidate. Yet, meanwhile samples drawn from the huge 'solution spaces' open to QC enable useful work to be done. Practical QC rests as tantalising prospect on a horizon that may not be reachable.
Biting the hand that feeds IT
