Latent Quantum-Like Structure in the Kolmogorov Model

Andrei Yu. Khrennikov

We compare the classical Kolmogorov and quantum probability models. We show that the gap between these model is not so huge as it was commonly believed. The main structures of quantum theory (interference of probabilities, Born's rule, complex probabilistic amplitudes, Hilbert state space, representation of observables by operators) are present in a latent form in the Kolmogorov model. In particular, we obtain ``interference of probabilities'' without to appeal to the Hilbert space formalism. We interpret ``interference of probabilities'' as a perturbation (by a $\cos$-term) of the conventional formula of total probability. Our classical derivation of quantum probabilistic formalism can stimulate applications of quantum methods outside of microworld : in psychology, biology, economy,...

André Berthiaume, Wim van Dam, Sophie Laplante

Andrei Yu. Khrennikov

We compare the classical Kolmogorov and quantum probability models. We show that the gap between these models is not so huge as was commonly believed. The main structures of quantum theory (interference of probabilities, Born's rule, complex probabilistic amplitudes, Hilbert state space, representation of observables by operators) are present in a latent form in the Kolmogorov model. In particular, we obtain “interference of probabilities” without appealing to the Hilbert space formalism. We interpret “interference of probabilities” as a perturbation (by a cos-term) of the conventional formula of total probability. Our classical derivation of quantum probabilistic formalism can stimulate applications of quantum methods outside of the microworld, for instance, in psychology, biology, economy, and other domains of science.

Chon-Fai Kam, Xavier Cadet, Miloud Bessafi +1 more

While world models learn compact representations of complex environments, they lack a physics-grounded metric to assess the structural fidelity of their latent spaces. We identify the wavelet scaling exponent $α$ as a critical diagnostic, proposing optimal representations satisfy variance equipartition ($α\approx 1/2$) -- mirroring Kolmogorov's inertial range. We establish $α= 1/2$ as a sharp transition boundary for the classical simulability of amplitude-encoded quantum kernels. Using tensor-network theory, we prove latents with $α> 1/2$ reside in an area-law phase admitting efficient classical emulation, while $α< 1/2$ triggers a volume-law phase where the Matrix Product State bond dimension $χ$ grows exponentially with qubit count $n$. Analyzing pre-trained VideoMAE latents reveals a dichotomy: spatial tokens approach the equipartition limit ($α\approx 0.423$), but permutation-invariant feature channels exhibit unstructured disorder ($α\approx -0.123$). This forces real-world latents deep into the volume-law phase, providing a data-driven necessary condition for simulation hardness. Finally, we apply Weingarten calculus to derive the exact variance of the scrambled transition probability under a 2-design ensemble. We prove this variance scales strictly as $\Var[X] = Θ(d^{-2})$. We confirm this numerically with a log-log slope of $-1.881$ ($R^2 = 0.999$), identifying a formidable shot-noise wall demanding a measurement budget of $M = Ω(d^2)$ that constrains quantum machine learning scalability.

Luigi Accardi

Oleg Georgievich Smolyanov, Andrei Yu. Khrennikov