  In this work, we argue that the implications of Pseudo and Quantum Random
Number Generators (PRNG and QRNG) inexplicably affect the performances and
behaviours of various machine learning models that require a random input.
These implications are yet to be explored in Soft Computing until this work. We
use a CPU and a QPU to generate random numbers for multiple Machine Learning
techniques. Random numbers are employed in the random initial weight
distributions of Dense and Convolutional Neural Networks, in which results show
a profound difference in learning patterns for the two. In 50 Dense Neural
Networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification
at +0.1%, and QRNG exceeded PRNG for mental state EEG classification by +2.82%.
In 50 Convolutional Neural Networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10
problems are benchmarked, in MNIST the QRNG experiences a higher starting
accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10,
the QRNG outperforms PRNG by +0.92%. The n-random split of a Random Tree is
enhanced towards and new Quantum Random Tree (QRT) model, which has differing
classification abilities to its classical counterpart, 200 trees are trained
and compared (100 PRNG/100 QRNG). Using the accent and EEG classification
datasets, a QRT seemed inferior to a RT as it performed on average worse by
-0.12%. This pattern is also seen in the EEG classification problem, where a
QRT performs worse than a RT by -0.28%. Finally, the QRT is ensembled into a
Quantum Random Forest (QRF), which also has a noticeable effect when compared
to the standard Random Forest (RF)... ABSTRACT SHORTENED DUE TO ARXIV LIMIT
