QF-LCA dataset: Quantum Field Lens Coding Algorithm for system state simulation and strong predictions.

Quantum field lens coding algorithm (QF-LCA) dataset is useful for simulating systems and predict system events with high probability. This is achieved by computing QF lens distance-based variables associated to event probabilities from the dataset produced by field lenses that encode system states on a quantum level. The probability of a state transition (ST), doubles in prediction values at the decoding step, e.

Quantum AI and hybrid simulators for a Universal Quantum Field Computation Model.

Quantum field theory (QFTh) simulators simulate physical systems using quantum circuits that process quantum information (qubits) via single field (SF) and/or quantum double field (QDF) transformation. This review presents models that classify states against pairwise particle states , given their state transition (ST) probability . A quantum AI (QAI) program, weighs and compares the field's distance between entangled states as qubits from their scalar field of radius .

Quantum field lens coding and classification algorithm to predict measurement outcomes.

This study develops a method to implement a quantum field lens coding and classification algorithm for two quantum double-field (QDF) system models: 1- a QDF model, and 2- a QDF lens coding model by a DF computation (DFC). This method determines entanglement entropy (EE) by implementing QDF operators in a quantum circuit. The physical link between the two system models is a quantum field lens coding algorithm (QF-LCA), which is a QF lens distance-based, implemented on real -qubit machines.