Quantum Fields

Jincheng Zhang

This paper introduces a novel approach to quantum field theory, termed Dynamic Quantum Field Theory (DQFT), which posits that quantum fields are not static but evolve continuously through a process of quantum field dynamics guided by quantum field theory and topological principles. We explore the theoretical framework of DQFT, focusing on the analysis of quantum field evolution, quantum effects, and the underlying mechanisms driving this dynamic system. The core of this work lies in a method for modeling and predicting the behavior of quantum fields, moving beyond traditional static models to embrace a continuous, evolving landscape. We present a preliminary investigation into the implications of DQFT for understanding fundamental quantum phenomena, specifically exploring the potential for emergent properties and novel quantum phenomena arising from the dynamic nature of the field. This research aims to establish DQFT as a powerful tool for investigating the complexities of quantum fields and their evolution.

Philippe A. Martin, FRANÇOIS ROTHEN

Tian Yu Cao

Introduction: Conceptual issues in quantum field theory Part I. Philosophers' Interests in Quantum Field Theory: 1. Why are we philosophers interested in quantum field theory 2. Quantum field theory and the philosopher Part II. Three Approaches to the Foundations of Quantum Field Theory: 3. The usefulness of a general theory of quantized fields 4. Effective field theory in condensed matter physics 5. The triumph and limitations of quantum field theory 6. Comments Discussions Part III. Does Quantum Field Theory Need a Foundation: 7. Does quantum field theory need a foundation? Part IV. Mathematics, Statistical Mechanics and Quantum Field Theory: 8. Renormalization group theory: its basis and formulation in statistical physics 9. Where does quantum field theory fit into the big picture? 10. The unreasonable effectiveness of quantum field theory 11. Comment: the quantum field theory of physics and of mathematics Part V. Quantum Field Theory and Spacetime: Introduction 12. Quantum field theory and spacetime: formalism and reality 13. Quantum field theory of geometry 14. 'Localization' in quantum field theory: how much of QFT is compatible with what we know about spacetime 15. Comments VI. 16. What is quantum field theory and what did we think it was? 17. Comments Discussions Part VII.Renormalization Group: 18. What is fundamental physics? A renormalization group perspective 19. Renormalization group: an interesting yet puzzling idea Part VIII. Non-Abelian Gauge Theory: 20. Gauge fields, gravity and Bohm's theory 21. Is the Aharonov-Bohm effect local? Discussions Part IX. The Ontology of Particles or Fields: 22. The ineliminable classical face of quantum field theory 23. The logic of quanta 24. Do Feynman diagrams endorse a particle ontology? 25. On the ontology of QFT Part X. Panel Discussion.

Philippe A. Martin, FRANÇOIS ROTHEN

Perry R. Rice

Philip Baback Alipour, Thomas Aaron Gulliver

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.