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Europhysics Letters (EPL)

A physical model of Fourier uncertainty principle

Rohan Singh, Parbati Biswas

Published Jun 17, 2025• 0 citations
Publisher

Abstract

Abstract Fourier transform is an integral part of harmonic analysis, information theory and signal processing with wide applications. The transformation between the Fourier pair is governed by an uncertainty relation, most generally captured by the Bandwidth theorem, . The usual approaches to the uncertainty principle employ sophisticated methods which lack physical interpretation of the origin and description of the uncertainty for real systems. In this paper, we have proposed a physically inspired model of the Fourier transform with an aim to describe its underlying dynamics and how it leads to the fundamental uncertainty principle. A signal is modeled as helical spring in 3D space, which is wrapped around a circle with controllable wrapping frequency. This process is formulated to reproduce the Fourier transform. The model is used to describe the origin of uncertainty, and to formulate a new generator-based approach to the Bandwidth theorem.

DOI
10.1209/0295-5075/ade563

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Abstract Fourier transform is an integral part of harmonic analysis, information theory and signal processing with wide applications. The transformation between the Fourier pair is governed by an uncertainty relation, most generally captured by the Bandwidth theorem, . The usual approaches to the uncertainty principle employ sophisticated methods which lack physical interpretation of the origin and description of the uncertainty for real systems. In this paper, we have proposed a physically inspired model of the Fourier transform with an aim to describe its underlying dynamics and how it leads to the fundamental uncertainty principle. A signal is modeled as helical spring in 3D space, which is wrapped around a circle with controllable wrapping frequency. This process is formulated to reproduce the Fourier transform. The model is used to describe the origin of uncertainty, and to formulate a new generator-based approach to the Bandwidth theorem.
We present a machine to produce new uncertainty principles from old ones. Traditionally an uncertainty principle is an inequality involving both a functionfand its Fourier transformf.To generate new uncertainty principles, we interpret the pair(f,f)as a time-frequency representation, replace it by a different time-frequency representation, and formulate a corresponding inequality. We discuss a few recent uncertainty principles for the short-time Fourier transform and the Wigner distribution that can be obtained in this way and suggest further problems.
The Heisenberg Uncertainty Principle is conventionally interpreted as a fundamental limit of nature's precision or as a consequence of measurement disturbance. In this paper, we propose a structural reinterpretation within the Static-Dynamic Recursive Information Space (SDRIS) framework. We demonstrate that quantum uncertainty is mathematically isomorphic to the Time-Bandwidth Product in signal processing. By modeling the vacuum as a discrete information grid, we identify the uncertainty relation not as an intrinsic indeterminism of the object, but as a Representation Limit arising from the projection of infinite information onto a finite-bandwidth substrate. Key Results: Geometric Derivation: We derive the uncertainty inequality σxσk≥1/2\sigma_x \sigma_k \ge 1/2σx​σk​≥1/2 strictly from the properties of the Fourier Transform on a lattice, identifying the Planck constant ℏ\hbarℏ as the scaling parameter converting grid bandwidth into physical momentum (p=ℏkp = \hbar kp=ℏk). Numerical Verification: Included Python simulations (FFT) confirm that Gaussian wavepackets minimize the uncertainty product to exactly 0.50.50.5. This proves that "fuzziness" is effectively the compression artifact of a finite-resolution universe. Ontological Shift: The paper recovers the standard quantum mechanical limit while providing a rigorous geometric foundation based on discrete signal theory, removing the need for an axiomatic "indeterminacy principle." Includes: Full mathematical derivation (Fourier Duality). Python Simulation Notebook (102_18116715_SDRIS_Born_Rule_Simulation_Code_v1_0.ipynb) verifying the lower bound.
Donoho and Stark have shown that a precise deterministic recovery of missing information contained in a time interval shorter than the time-frequency uncertainty limit is possible. We analyze this signal recovery mechanism from a physics point of view and show that the well-known Shannon-Nyquist sampling theorem, which is fundamental in signal processing, also uses essentially the same mechanism. The uncertainty relation in the context of information theory, which is based on Fourier analysis, provides a criterion to distinguish Shannon-Nyquist sampling from compressed sensing. A new signal recovery formula, which is analogous to Donoho-Stark formula, is given using the idea of Shannon-Nyquist sampling; in this formulation, the smearing of information below the uncertainty limit as well as the recovery of information with specified bandwidth take place. We also discuss the recovery of states from the domain below the uncertainty limit of coordinate and momentum in quantum mechanics and show that in principle the state recovery works by assuming ideal measurement procedures. The recovery of the lost information in the sub-uncertainty domain means that the loss of information in such a small domain is not fatal, which is in accord with our common understanding of the uncertainty principle, although its precise recovery is something we are not used to in quantum mechanics. The uncertainty principle provides a universal sampling criterion covering both the classical Shannon-Nyquist sampling theorem and the quantum mechanical measurement.
It is well known that the fractional Fourier transform (FrFT) is a non-trivial generalization of the classical Fourier transform. This transform has been extensively utilized in signal analysis and image processing. The uncertainty principle is one of the fundamental results related to the FrFT. In the present paper, we first propose the different method in proving a variant of Heisenberg uncertainty principles associated with the FrFT. This method combines the Heisenberg-type uncertainty principle pertaining to the FrFT and the additive property of the FrFT. We then provide an alternative proof of the logarithmic inequality for the FRFT and find that this result differs slightly that in the literature. Finally, we explicitly expand the variant of Heisenberg uncertainty principle to a more general case.
The derivation of the Heisenberg Uncertainty Principle (HUP) from the Uncertainty Theorem of Fourier Transform theory demonstrates that the HUP arises from the dependency of momentum on a wave number that exists at the quantum level. It also establishes that the HUP is purely a relationship between the effective widths of Fourier transform pairs of variables (i.e. conjugate variables). We note that the HUP is not a quantum mechanical measurement principle per se. We introduce the Quantum Mechanical equivalent of the Nyquist-Shannon Sampling Theorem of Fourier Transform theory, and show that it is a better principle to describe the measurement limitations of Quantum Mechanics. We show that Brillouin zones in Solid State Physics are a manifestation of the Nyquist-Shannon Sampling Theorem at the quantum level. By comparison with other fields where Fourier Transform theory is used, we propose that we need todiscern between measurement limitations and inherent limitations when interpreting the impact of the HUP on the nature of the quantum level. We further propose that while measurement limitations result in our perception of indeterminism at the quantum level, there is no evidence that there are any inherent limitations at the quantum level, based on the Nyquist-Shannon Sampling Theorem
This article establishes the proof of the Schrödinger equation for numerous quantum systems, utilizing Heisenberg's uncertainty principle. The Fourier transform connects functions in the time and frequency domains, resulting in the mathematical inequality that is the foundation of the uncertainty principle. In the part of Methods and Theory, the article derives the uncertainty principle through Fourier transforms by defining the mean and variance of angular frequency and time, and subsequently expanding the integral. This establishes the fundamental connection between time and frequency domains, illustrating the constraints imposed by quantum mechanics. In the part of Results and Application, the article applies the uncertainty principle to derive the Schrödinger equation under different conditions: free particle, particle in a box, harmonic oscillator, and hydrogen atom. For each case, the article assumes wave function solutions, uses the uncertainty in position and momentum to estimate kinetic and potential energies, and shows that the total energy matches the ground state energy derived from the Schrödinger equation. The results highlight the critical role of Heisenberg's uncertainty principle in understanding key aspects of quantum mechanics, providing a unified framework for these diverse systems.