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Theoretical and Mathematical Physics

Quantum mechanics as the quadratic Taylor approximation of classical mechanics: The finite-dimensional case

Andrei Yu. Khrennikov

Published Aug 1, 2007• 7 citations
Publisher
DOI
10.1007/s11232-007-0095-z

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In Section 3.1, we showed that QM can be represented as a classical field model in which physical quantities are given by quadratic functionals of fields. In this chapter we shall show that in complete accordance with Einstein’s views QM is an approximation of CSM of fields, i.e., classical statistical mechanics with the infinite- dimensional phase space H = L2(R3;R) × L2(R3;R), where L2(R3;R) is the space of real-valued square-integrable functions. (In the previous considerations the symbol L2(R3) was used to denote the space of complex-valued square-integrable functions.) QM can be represented as the quadratic Taylor approximation of CSM of fields. By expanding classical quantities, functionals of the prequantum field, φ → f (φ), into the Taylor series up to the second term, we obtain quantum quantities. By averaging these expansions we establish a coupling between classical average (so to say, prequantum) and quantum average. The latter appears as the main term in the expansion of the classical phase space average with respect to a small parameter α, the dispersion of the prequantum fluctuations, α = σ 2(μ). Here μ is the probability distribution of a prequantum random field. In the limit α → 0 the classical prequantum and quantum averages coincide. Thus QM can be interpreted as the limit (as α → 0) of the theory of classical random fields, cf. Section 3.11 for an experimentwhichmay show a deviation from the prediction of QM, namely, violation of Born’s rule.

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In Section 3.1, we showed that QM can be represented as a classical field model in which physical quantities are given by quadratic functionals of fields. In this chapter we shall show that in complete accordance with Einstein’s views QM is an approximation of CSM of fields, i.e., classical statistical mechanics with the infinite- dimensional phase space H = L2(R3;R) × L2(R3;R), where L2(R3;R) is the space of real-valued square-integrable functions. (In the previous considerations the symbol L2(R3) was used to denote the space of complex-valued square-integrable functions.) QM can be represented as the quadratic Taylor approximation of CSM of fields. By expanding classical quantities, functionals of the prequantum field, φ → f (φ), into the Taylor series up to the second term, we obtain quantum quantities. By averaging these expansions we establish a coupling between classical average (so to say, prequantum) and quantum average. The latter appears as the main term in the expansion of the classical phase space average with respect to a small parameter α, the dispersion of the prequantum fluctuations, α = σ 2(μ). Here μ is the probability distribution of a prequantum random field. In the limit α → 0 the classical prequantum and quantum averages coincide. Thus QM can be interpreted as the limit (as α → 0) of the theory of classical random fields, cf. Section 3.11 for an experimentwhichmay show a deviation from the prediction of QM, namely, violation of Born’s rule.
The quantization of Hamilton dynamics (QHD) [J. Chem. Phys. 113, 6557 (2000)] that efficiently generalizes classical mechanics to include quantum tunneling and zero-point energy effects is extended to a general position dependent potential. A Taylor series expansion of the potential is considered both around a fixed point and around the moving instantaneous value of the position variable. The equations-of-motion obtained for the moving frame are significantly simpler than for the fixed frame, while still satisfying the classical limit. The number of the QHD variables and the order of the Taylor expansion of the potential constitute two independent approximation parameters. Conservation of the total energy and the Heisenberg commutator relationship is established for the second-order QHD that includes linear and quadratic variables. The formal results are illustrated by examples, including the harmonic oscillator, tunneling in a doublewell potential, and energy exchange between coupled Morse oscillators representing the stretching vibrations of the SPC-F water molecule commonly used in molecular dynamics simulations. QHD provides an improvement over classical mechanics in all cases. The QHD data agree with the exact data in the large ℏ quantum limit and in the ℏ=0 classical limit, with deviations observed for the intermediate ℏ values, depending on the system and initial conditions. QHD provides a quantitative short time approximation. The second-order QHD becomes inappropriate when quantum wave packets split. Higher-order QHD approximations are required in such cases. The second-order QHD is particularly suitable for the description of zero-point energy effects and properly treats moderate tunneling events.
The mutual information is bounded from above by a decreasing affine function of the square of the distance between the input distribution and the set of all capacity-achieving input distributions ΠA, on small enough neighborhoods of ΠA, using an identity due to Topsøe and the Pinsker’s inequality, assuming that the input set of the channel is finite and the constraint set A is polyhedral, i.e., can be described by (possibly multiple but) finitely many linear constraints. Counterexamples demonstrating nonexistence of such a quadratic bound are provided for the case of infinitely many linear constraints and the case of infinite input sets. Using Taylor’s theorem with the remainder term, rather than the Pinsker’s inequality and invoking Moreau’s decomposition theorem the exact characterization of the slowest decrease of the mutual information with the distance to ΠAis determined on small neighborhoods of ΠA. Corresponding results for classical-quantum channels are established under separable output Hilbert space assumption for the quadratic bound and under finite-dimensional output Hilbert space assumption for the exact characterization. Implications of these observations for the channel coding problem and applications of the proof techniques to related problems are discussed.