Quantum Simulation, Artificial Intelligence, and Vision
Somnath Bhattacharyya · Evolution · 2023
Quantum noise minimization is particularly important for artificial intelligence, which requires a significant amount of improvement. Many concepts of machine learning were derived from the fundamental laws of interactions that were thoroughly described in SP through vision and image processing in the brain. Artificial vision based on quantum image processing appears to be a very complex problem that will include (1) clear vision (noise-free) like seeing the flame of fire and disregarding the smoke created by the fire. (2) an extra depth or dimension using any depth of field or aperture, (3) superposed images (image reconstruction), (4) vision beyond duality (duality shows wave–particle at the same time instead of one at a time), (5) ground state, or singularity, or the end of a vortex, (6) ground and the excited state simultaneously representing the Purusha and Prakriti. So an artificial object is created from an image or dividing one into two parts. The second one will possess a very similar or complementary nature to the original one. So the fundamental thing is to divide the object, allow interactions, make many parts, and finally assemble all parts together. The mind can do this job efficiently. Intelligence can be achieved through an attractive force that can break and make things. This has to be added to a machine that can possibly think of a process since some complexity in nature cannot be understood by machines. (1) We can simulate the WL process by a quantum computer based on superconducting qubits. (2) Using two qubits, we can create a four-level system that can be extended by adding a third qubit. A set of three qubits delivering an eight-level system can create a geometric phase. The holonomic gate operation can run these qubits, which can also be implemented in NV centers. This can be used for color images. (3) Multichannel quantum information (MCQI) architecture can be produced from three color qubits.