I love this part of the conclusion,
Finally, by using equity trading as a medium, we have
shown by training four weeks of stock market data and then producing two groups of 6 possible forecast
outcomes for the next week on the preceding weekend, the quantum-like evolutionary algorithm can produce
a forecast with odds of 80%.

Really! Four weeks, lol. Quite a large sample size hahaha.
This paper does nothing but highlight the importance of data which everyone already knows. There is no specific use case for the 'superposition principle' outlined explicitly in the paper, this should be a philosophical paper

.
Of course, four weeks of data may not seem like much, but let’s be blunt, what does the price of Bitcoin or some stock from 10 years ago have to do with predicting the future price of Bitcoin for the following week or for the next 10 years? Looking back that long in the past or forward towards the future doesn’t really have much of an effect for forecasting what’s happening at hand now, it doesn’t mean the larger the dataset the more accurate the prediction you’ll get – quality not quantity. Yes, for some mathematical or statistical analysis it might be of some use, but if we’re just looking at the forecast horizon of the foreseeable future, studying the more recent past is more efficient, well at least in our opinion we believe that you don’t need a tremendously large dataset to forecast a short horizon, so we only used four weeks of data in our case study. As to the superposition principle, we clearly stated in the paper it is utilized to model the challenge of dual uncertainty; the uncertain market and the traders’ actions, where at any given time the market could go up or down and the traders’ can either buy or sell, and this is, to the best of our knowledge, an approach that has not yet been attempted.