🙌🏼MEGAscaling with quantum ML
Theoretically, quantum computers could be more powerful than any conventional computer, especially at finding prime factors of numbers, the mathematical basis of modern encryption that protects banking and other sensitive data. The more components known as qubits are connected to each other in a quantum computer, where multiple particles can instantly influence each other no matter how far apart they are, the more its processing power can grow exponentially.
One potential application of quantum ML is the simulation of quantum systems, such as chemical reactions, to create new drugs. But the average performance of an ML algorithm depends on how much data it has. The amount of data ultimately limits the performance of machine learning. Therefore, to simulate a quantum system, the amount of training data that a quantum computer might need will grow exponentially as the system being modeled gets larger. This potentially eliminates the advantage of quantum computing over classical computing.
The scientists proposed to link additional qubits to the quantum system that the quantum computer should model. This additional set of "auxiliary" qubits can help the quantum ML circuit to simultaneously interact with many quantum states in the training data. So the quantum ML scheme can work even with a relatively small number of auxiliary devices. In practice, it is still quite difficult to implement this idea, but it can be tested within the framework of the experiments of CERN, the largest particle physics laboratory in the world.
https://spectrum.ieee.org/quantum-machine-learning