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arXiv:2609.24269

On the Born rule in a new quantum approach

Inge S. Helland

quant-phSubmitted Sep 21, 2026• Announced Sep 22, 2026• 0 citations• Revised Sep 22, 2026 (v2)
v1 · Sep 21, 2026v2 · Sep 22, 2026v3 · Sep 26, 2026v4 · Oct 5, 2026v5 · Oct 7, 2026v6 · Oct 16, 2026
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Abstract

In the context of a new approach towards quantum foundation, the Born rule is proved under a weak condition through several steps. The sole weak condition behind the Born rule is called the transition probability principle, which is closely related to the likelihood principle of statistics. This condition is discussed from several points of view. The whole approach leads to a simpler state concept than the traditional one in finite-dimensional quantum mechanics.

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14 pages

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In the context of a new approach towards quantum foundation, the Born rule is proved under a weak condition through several steps. The sole weak condition behind the Born rule is called the transition probability principle, which is closely related to the likelihood principle of statistics. This condition is discussed from several points of view. The whole approach leads to a simpler state concept than the traditional one in finite-dimensional quantum mechanics.
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Quantum decision theory is introduced here, and a new basis for this theory is proposed. It is first based upon the author’s general arguments for the Hilbert space formalism in quantum theory, and next on arguments for the Born rule, which is the basis for calculating quantum probabilities. A basic notion behind the quantum theory foundation is that of theoretical variables, that are divided into accessible and inaccessible ones. This is here specialized to decision variables. It is assumed that each accessible variable can be seen as a function of a specific inaccessible variable. Another assumption is that there exist two maximal accessible decision processes in the given situation. Two basic assumptions behind the Born rule are 1) the likelihood principle, and 2) the actor in question has motivations that can be modeled by a hypothetical perfectly rational higher being. The theory is illustrated by a medical example.
Quantum decision theory is introduced here, and new basis for this theory is proposed. It is first based upon the author's general arguments for the Hilbert space formalism in quantum theory, next on arguments for the Born rule, that is the basis for calculating quantum probabilities. A basic notion behind the quantum theory foundation is that of theoretical variables, that are divided into accessible and inaccessible ones. This is here specialized to decision variables. It is assumed that each accessible variable can be seen as a function of a specific inaccessible variable. Another assumption is that there exist two maximal accessible decision processes in the given situation. Two basic assumptions behind the Born rule are 1) the likelihood principle, 2) the actor in question has motivations that can be modeled by a hypothetical perfectly rational higher being. The theory is illustrated by a medical example.
It is argued from several points of view that quantum probabilities might play a role in statistical settings. New approaches toward quantum foundations have postulates that appear to be equally valid in macroscopic settings. One such approach is described here in detail, while one other is briefly sketched. In particular, arguments behind the Born rule, which gives the basis for quantum probabilities, are given. A list of ideas for possible statistical applications of quantum probabilities is provided and discussed. A particular area is machine learning, where there exists substantial literature on links to quantum probability. Here, an idea about model reduction is sketched and is motivated from a quantum probability model. Quantum models can play a role in model reduction, where the partial least squares regression model is a special case. It is shown that for certain experiments, a Bayesian prior given by a quantum probability can be motivated. Quantum decision theory is an emerging discipline that can be motivated by this author's theory of quantum foundations.
It is argued from several points of view that quantum probabilities might play a role in statistical settings. New approaches toward quantum foundations have postulates that appear to be equally valid in macroscopic settings. One such approach is described here in detail, while two others are briefly sketched. In particular, arguments behind the Born rule, which gives the basis for quantum probabilities, are given. A list of ideas for possible statistical applications of quantum probabilities is provided and discussed. A particular area is machine learning, where there exists substantial literature on links to quantum probability. Here, an idea about model reduction is sketched and is motivated from a quantum probability model. Quantum models can play a role in model reduction, where the partial least squares regression model is a special case. It is shown that for certain experiments, a Bayesian prior given by a quantum probability can be motivated. Quantum decision theory is an emerging discipline that can be motivated by this author’s theory of quantum foundations.
An alternative formulation of quantum mechanics is proposed based on a principle that can be considered a generalization of the Born rule. The principle is composed of a mathematical expression and an associated interpretation, and establishes a correlation between the positions of a particle at two different times. Under reasonable conditions for the wave function, this correlation implies that the particles follow quasiclassical trajectories. It is also shown that the Born rule is equivalent to a particular case of the evolved principle. These features allow the principle to provide a unified explanation of the results of the statistical experiments and of the quasiclassical macroscopic evolution. There is a strong analogy between the quantum principle and a probabilistic principle which is necessary to derive empirical predictions from the mathematical formalism of probability theory. This principle is referred to by some authors as Cournot's principle, while other authors use the equivalent notion of typicality. In this paper probability theory and quantum mechanics are formulated in such a way as to explicitly include the two principles and to emphasize the very similar conceptual structure of the two theories.