Abstract
In this work, we argue that the implications of pseudorandom 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, and 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 data sets, 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). Ten to 100 ensembles of trees are benchmarked for the accent and EEG classification problems. In accent classification, the best RF (100 RT) outperforms the best QRF (100 QRF) by 0.14% accuracy. In EEG classification, the best RF (100 RT) outperforms the best QRF (100 QRT) by 0.08% but is extremely more complex, requiring twice the amount of trees in committee. All differences are observed to be situationally positive or negative and thus are likely data dependent in their observed functional behaviour.
Highlights
Quantum and classical hypotheses of our reality are individually definitive and yet are independently paradoxical, in that they are both scientifically verified though contradictoryCommunicated by V
Though the lack of a Standard Model of the Universe continues to provide a problem for physicists, the field of Computer Science thrives by making use of both in classical and quantum computing paradigms since they are independently observable in nature
Please note that in neural network training, lines do not correlate on a one-to-one basis
Summary
Quantum and classical hypotheses of our reality are individually definitive and yet are independently paradoxical, in that they are both scientifically verified though contradictoryCommunicated by V. Entanglement means that the value of the bit, even before measurement, can be assumed to be parallel or antiparallel to another bit of which it is entangled to Bell (1964) These extended laws allow for the solving of problems far more efficiently than computers. A 64-bit system (263 −1) has approximately 9.22 quintillion values with its individual bits at values 1 or 0, whereas unlike a three-state ternary system which QPUs are often mistaken for, the laws of superposition and the degrees of state would allow a small array of qubits to represent all of these values at once—theoretically allowing quantum computers to solve problems that classical computers will never be able to possibly solve. Quantum Processing Units (QPUs) made available for use by Rigetti, Google and IBM have up to 16 available qubits for computing via their cloud platforms
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