Abstract
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 |ij〉, given their state transition (ST) probability P|ij〉. A quantum AI (QAI) program, weighs and compares the field's distance between entangled states as qubits from their scalar field of radius R ≥ |rij|. These states distribute across 〈R〉 with expected probability 〈Pdistribute〉 and measurement outcome 〈M(Pdistribute)〉 = P|ij〉. 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 P|ij〉 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 〈M(F)〉 ≥ 7/5 in classifying states to select 7 optimal QFCMs to predict 〈M〉's on QFTh observables. This includes QFCMs' commonality of 〈M〉 against QFCMs limitations in predicting system events. Common measurement results of QFCMs include their expected success probability 〈Psuccess〉 over STs occurring in the system. Consistent results with high F's, are averaged over STs as 〈Pdistribute〉 yielding 〈Psuccess〉 ≥ 2/3 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 P|ij〉 = 〈Psuccess〉/1 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.