It is shown that the Helmholtz wave equation follows from a new uncertainty principle: Given, as data, the position of a photon in an unknown diffraction pattern, the estimated position of the centroid of the pattern will suffer minimum precision. This implies a maximally spread out diffraction pattern, obeying a principle of minimum Fisher information. The minimum is constrained by knowledge of the refractive-index function n(x, y, z) of the medium through a requirement that the mean-square spatial phase gradient across the medium should be generally nonzero. Operationally the principle works directly with intensities and not complex amplitudes. As a practical matter the numerical use of the intensity-based principle might permit a widening of the known scope of solutions to diffraction problems.
It is shown that the Helmholtz wave equation follows from a new uncertainty principle: Given, as data, the position of a photon in an unknown diffraction pattern, the estimated position of the centroid of the pattern will suffer minimum precision. This implies a maximally spread out diffraction pattern, obeying a principle of minimum Fisher information. The minimum is constrained by knowledge of the refractive-index function n(x, y, z) of the medium through a requirement that the mean-square spatial phase gradient across the medium should be generally nonzero. Operationally the principle works directly with intensities and not complex amplitudes. As a practical matter the numerical use of the intensity-based principle might permit a widening of the known scope of solutions to diffraction problems.
An unknown probability law p(x) can be efficiently estimated using a smoothness enforcing principle I=∫dxp'(x)2/p(x) = minimum, called minimum Fisher information (MFI). An unknown diffraction pattern is an unknown probability law (on photon position x). Consider a best estimate of the centroid position in the pattern from observation of a single-photon position. It is known that the mean-square error e2 in such an estimate must exceed the Cramer-Rao bound 1II,I above. Therefore, if we demand that e2 be a maximum, we have constructed a physical rationale for the use of MFI for estimating p(x). To bring in the known refractive-index profile n(x) of the diffracting medium, demand as a constraint that the mean-square spatial phase (2π/λ)2∫dxp2(x)p(x)=ϕ2 be generally nonzero. Constructing an overall principle I — ϕ 2 = minimum, a variational problem in p(x) results. The Euler-Lagrange solution is the Helmholtz wave equation on a real amplitude function q(x), where p(x) = q(x) 2 . Thus a diffraction pattern is seen to be nature’s way of maximally smearing out spatially the probability law on position for each photon forming the pattern.
A wide class of physical problems requires the estimation of probability laws. Diffraction patterns and quantum mechanical probability laws on position are examples. Minimum Fisher information is one approach to estimating such laws; maximum entropy is another. In this paper, we show that the minimum Fisher information approach may be derived from a prior principle of maximum Cramer-Rao bound (MCRB). The MCRB-Fisher approach is then applied to some fundamental physical problems, including diffraction theory and quantum mechanics. It is found to give the correct physical solutions, that is the Helmholtz and Schrödinger wave equations respectively. By comparison, the maximum entropy approach gives incorrect solutions to these problems, except in the special (thermodynamic) case of a harmonic oscillator potential.
The principle of minimum Fisher information (MFI) provides a basis for deriving most of the laws of physics.1 The Fisher I is defined as (mean-squares error)-1 in the efficient determination of the classical position θ of a particle. For a one-component pdf p(x) = q2(x), the Fisher information is a one-term functional I = 4 ʃdx[q’(x)]2. Thus, I decreases as q(x) becomes smoother, or as disorder increases, consistent with the second law of thermodynamics. In the above, p(x) was specifically assumed to have a single component q2(x). Then MFI gives a real-only wave equation. Here we consider multicomponent cases p(x) = q12(x) + … + qN2(x), where the qn(x) are independent degrees of freedom. We seek the Fisher I for the case N = 2 of nonrelativistic quantum mechanics or of scalar optics. A benchmark statistical situation arises when the N components of qn(x) do not overlap. Then their informations add, and the resulting mean-squares error value is efficient, i.e. an absolute minimum.
It is shown that the assumption that nature acts to make optimum estimates of position maximally in error leads to the Schrödinger (energy) wave equation (SWE). In this way, the SWE follows from a simple statement of uncertainty. The approach grows out of probability estimation theory, in particular the principle of minimum Fisher information (or maximum Cramer–Rao bound). The minimized Fisher information turns out to be proportional to the mean kinetic energy of the particle. This approach is an attractive supplement to conventional ways of introducing quantum mechanics to students, since it avoids the use of imperfect physical models (such as vibrating strings) or the immediate need for complex momentum operators, and follows from a plausible assumption as to how nature works.
Optical signals may be regarded as probability laws (on photon events). The problem of optical signal recovery may therefore be viewed as a problem in estimating a probability law p(x). Maximum entropy has, in the past, been used for this purpose. Here, we discuss instead the use of minimum Fisher information I = ∫ d x [ p ′ ( x ) ] 2 / p ( x ) . Consider a physical system consisting of a random aggregate of particles or pho tons. Consider the experiment of measuring one particle's coordinate y, and from this, best estimating the mean coordinate θ of the ensemble, where y = θ + x. As time passes, the particles randomly become more spread out, so that the error in the estimate should increase. But the error goes inversely as I. Therefore, I should statistically decrease with time. With no constraint on the system, as t → ∞, I → 0, defining a maximally random law p(x) = constant. However, in the presence of a physical constraint, I should approach a finite value, obeying I = minimum. When the constraint is linear in the mean kinetic energy of the system, the solution p(x) obeys the stationary differential equation for the system. In this way, the Schrodinger (energy) wave equation, Helmholtz wave equation, diffusion equation, and Maxwell-Boltzmann law may be derived from one classical principle of disorder.
The Klein–Gordon equation for spinless particles, as well as the Weyl–Pauli and Dirac equations for particles with spin, may be derived from one information-theoretic principle. Consider a gedanken experiment whereby the mean position of a particle in a central force field is to be estimated by means of one position measurement. An efficient (optimum) estimate obeys a condition of minimum Fisher information, or minimum precision, according to the second law of thermodynamics. When the Fisher information is minimized subject to a constraint on the mean-square kinetic energy for the particle, the solution is a probability law on position whose corresponding wave amplitude obeys the Klein-Gordon equation, the Weyl–Pauli equation, or the Dirac equation, depending on whether scalar (without spin) or vector (with spin) solutions are sought. The minimized Fisher information is proportional to the mean-square kinetic energy, minus the rest energy of the particle.