Quantum Resonators Circuit for EEG Signals Pattern Matching

Applications of quantum computing are expanding into new and crossing fields, yet there is a noticeable difference in how quantum machine learning visualization is approached, i.e., when compared with simulation conducted using any simulator. When working with a large dataset, this simulation results in a situation where, depending on the problem, either a probability or a count histogram can be visualized in a Qiskit circuit application. When dealing with large amounts of data, the quantum machine learning technique is typically used, which solves the classification problem by the selection of a suitable classifier; however, the usual simulation results plot visualization takes a backseat. Given this, a Qiskit quantum circuit-based method for pattern resonance reveals target quantum states for real-world applications where a baseline state of the input data is suitably encoded for seeing any target pattern. Since EEG data is spontaneous in the real world, in this work, this approach is further applied to mimic stream data to observe the state pattern and for comparison purposes. The results are consistent and encouraging for further application.