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 [Formula: see text] , given their state transition (ST) probability [Formula: see text]. A quantum AI (QAI) program, weighs and compares the field’s distance between entangled states as qubits from their scalar field of radius [Formula: see text]. These states distribute across [Formula: see text] with expected probability [Formula: see text] and measurement outcome [Formula: see text]. A quantum-classical hybrid model of processors via QAI, classifies and predicts states by decoding qubits into classical bits. For example, a QDF as a quantum field computation model (QFCM) in IBM-QE, performs the doubling of [Formula: see text] for a strong state prediction outcome. QFCMs are compared to achieve a universal QFCM (UQFCM). This model is novel in making strong event predictions by simulating systems on any scale using QAI. Its expected measurement fidelity is [Formula: see text] in classifying states to select 7 optimal QFCMs to predict [Formula: see text] ’s on QFTh observables. This includes QFCMs’ commonality of [Formula: see text] against QFCMs limitations in predicting system events. Common measurement results of QFCMs include their expected success probability [Formula: see text] over STs occurring in the system. Consistent results with high [Formula: see text] ’s, are averaged over STs as [Formula: see text] yielding [Formula: see text] performed by an SF or QDF of certain QFCMs. A combination of QFCMs with this fidelity level predicts error rates (uncertainties) in measurements, by which a [Formula: see text] is weighed as a QAI output to a QFCM user. The user then decides which QFCMs perform a more efficient system simulation as a reliable solution. A UQFCM is useful in predicting system states by preserving and recovering information for intelligent decision support systems in applied, physical, legal and decision sciences, including industry 4.0 systems.