Quantum computers do not have such separation. They can contain some quantum memory, but the data is generally housed directly in qubits, but calculations involve directly performing an operation called a gate. In fact, there was a demonstration that for monitored machine learning, systems can learn to classify items after training with pre-classified data. Better than the classicsEven when the data to be processed is housed in classic hardware.
This form of machine learning relies on what is called mutated quantum circuits. This is a two quit gate operation that takes on additional elements that are retained in the classic aspect of the hardware and is given to the qubit via a control signal that triggers the gate operation. This can be thought of as similar to communications involved in neural networks. There is a 2 quit gate operation that corresponds to passing information between two artificial neurons and passing information between factors similar to weight given to the signal.
It’s a system from the team Honda Research Institute We worked on a collaboration called Quantum Software Company. Blue qubit.
Kits to pixels
The focus of the new work was primarily on how to obtain data from the classical world to Quantum System. However, the researchers ended up testing the results with two different quantum processors.
The problem they were testing was one of the image classifications. The raw materials were from a Honda Scene data set, where images were taken from approximately 80 hours of operation in Northern California. The image is tagged with information about what is in the scene. And the question the researchers wanted to work with machines was simple: is it snowing in the scene?
