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 | i j 〉 , given their state transition (ST) probability P | i j 〉 . A quantum AI (QAI) program, weighs and compares the field's distance between entangled states as qubits from their scalar field of radius R ≥ | r i j | . These states distribute across 〈 R 〉 with expected probability 〈 P distribute 〉 and measurement outcome 〈 M ( P distribute ) 〉 = P | i j 〉 . 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 | i j 〉 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 〈 P success 〉 over STs occurring in the system. Consistent results with high F 's, are averaged over STs as 〈 P distribute 〉 yielding 〈 P success 〉 ≥ 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 | i j 〉 = 〈 P success 〉 < ∼ 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.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 The Author(s).)