Why the probabilistic approach is not more prominent in QFT?

In summary, there is a lack of discussion about characteristic functionals in probabilistic issues in QFT, despite their clear connection to the partition function. The Nelson approach and Jaynes' Max Ent principle also have connections to probability, but are not widely used. The use of generating functionals and information-theoretical foundation in statistical mechanics is becoming more standard. The dynamical approach to deriving equilibrium distributions is closely related to the unitarity of the S-matrix. A reference for the discussion of characteristic functionals and measure theory for infinite dimensional systems in QFT is the quantum physics book by Glimm and Jaffe.
  • #1
jordi
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Of course, there is probability theory in QFT. The partition function can be understood as a characteristic functional.

What surprises me is there is little discussion about characteristic functionals, except for some 40 year old papers.

It seems surprising to me that physicists, being used to analogies from simple systems to complex ones, are not used to emphasize the existence of characteristic functions in probability, and how its obvious generalization is the partition function. Maybe this is explained somewhere, but it is not "typical".

I read that the Nelson approach, trying to define QM as "stochastic paths", à la Markov processes, was a dead end.

A bit similar with Jaynes' Max Ent principle: I see there is a guy who recently has written about recovering the Schroedinger equation from Max Ent. By looking at Jaynes arguments, it seems the Gibbs measure could be justified quite rationally under this framework. But in general (and apart from the recent trend on entropy with black holes) this idea is not used much, and the Gibbs measure is used without much justification.

Is there any referece where the concept of characteristic functionals, and measure theory for infinite dimensional systems in QFT, is discussed? Or in general, other probabilistic issues?
 
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  • #2
I don't know, what you mean.

All you evaluate in (vacuum) QFT is probabilistic (S-matrix elements of particle reactions or equivalently cross sections, decay widths). Also among the most convenient formulations is using generating functionals to evaluate these transition probabilities, often using path-integral and other functional methods (like the heat-kernel, Schwinger proper time, or worldline formalism).

Also the information-theoretical foundation of statistical mechanics (and in fact statistical mechanics is most coherently formulated in terms of quantum statistics and that's again most elegantly evaluated in terms of relativistic or non-relativistic QFT) becomes more and more standard.

Another approach to derive the equilibrium distributions is as old as statistical mechanics itself, i.e., the dynamical approach a la Boltzmann, leading to the H theorem, which is also closely related to the fundamental properties of the quantum-field theoretically defined S-matrix, namely its unitarity leading to the principle of detailed balance (it's not necessary to relate it to time-reversal and parity invariance as suggested by some textbooks; just unitarity of the S-matrix is sufficient; see, e.g., Landau-Lifhitz vol. 10).
 
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  • #3
jordi said:
Is there any referece where the concept of characteristic functionals, and measure theory for infinite dimensional systems in QFT, is discussed?
The quanum physics book by Glimm and Jaffe should satisfy your interest.
 
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1. Why is the probabilistic approach not the standard in QFT?

The probabilistic approach is not the standard in QFT because it is based on the assumption that physical quantities, such as position and momentum, are inherently uncertain and can only be described in terms of probabilities. This is in contrast to the traditional approach in QFT, which assumes that these quantities have definite values at all times.

2. What are the main challenges of implementing the probabilistic approach in QFT?

One of the main challenges of implementing the probabilistic approach in QFT is that it requires a fundamental shift in the way we think about physical quantities. It also requires the development of new mathematical tools and techniques to accurately describe and analyze the probabilistic nature of quantum systems.

3. How does the probabilistic approach differ from the traditional approach in QFT?

The traditional approach in QFT is based on the principles of causality and determinism, which assume that the behavior of physical systems can be predicted with absolute certainty. The probabilistic approach, on the other hand, acknowledges the inherent uncertainty of quantum systems and describes their behavior in terms of probabilities.

4. Are there any advantages to using the probabilistic approach in QFT?

Yes, there are several advantages to using the probabilistic approach in QFT. For example, it allows for a more accurate description of quantum systems, particularly at the microscopic level. It also provides a more intuitive understanding of certain phenomena, such as quantum entanglement.

5. What are the potential implications of adopting the probabilistic approach in QFT?

If the probabilistic approach were to become more prominent in QFT, it could have significant implications for our understanding of the fundamental laws of nature. It could also lead to the development of new technologies and applications, such as quantum computing, that rely on the probabilistic nature of quantum systems.

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