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Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics

Spectral filters in quantum mechanics: A measurement theory perspective

Amrendra Vijay, Róbert E. Wyatt

Published Sep 1, 2000• 14 citations
Publisher

Abstract

We present the time-domain theory of spectral filters, starting with the basic propositions of the theory of measurement in quantum mechanics, and develop its parameter-free implementation in the traditional correlation function as well as the filter diagonalization (FD) form. The present study unifies all the time-domain spectral filter algorithms in the literature, under a single theme which is based on the notion of selective measurements. For specific numerical purposes, we have selected Chebyshev polynomials for developing the time propagator and this permits us to carry out the relevent time integrals fully analytically and obtain FD equations in a numerically convenient form. We also argue that the FD method is a particular realization of the general spectral filter goal and it is constrained, in general, by the time-energy uncertainty regime at least as much as the correlation-function-based method. To contrast the performance of the correlation function and the FD methods, we have carried out the detailed numerical experiments on a model system, which suggest that the FD method needs almost as much time propagation as the correlation function method, in order to identify the correct spectrum. The difference lies in the procedure for the exact location of eigenvalue positions, for which the FD method employs a diagonalization step while the correlation function method involves the location of zeros.

DOI
10.1103/physreve.62.4351

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Reply to Comment on “Spectral filters in quantum mechanics: A measurement theory perspective”

Amrendra Vijay

We address the issues raised in the preceding comment by Mandelshtam and Carrington [Phys. Rev. E 65, 028701 (2002)], concerning the eigenvalue determination by the spectral filter methods. We argue that the Fourier transformation is the building block of all currently known time-domain spectral filter algorithms, and, therefore, the time-energy uncertainty principle affects them all in a similar manner. We also explain the situation when the correlation function method may be less suitable, in comparison to the filter diagonalization method, for the determination of eigenvalues, even though both share the same fundamental principles.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics•Jan 15, 2002•4 citations

Extraction, through filter-diagonalization, of general quantum eigenvalues or classical normal mode frequencies from a small number of residues or a short-time segment of a signal. I. Theory and application to a quantum-dynamics model

Michael R. Wall, Daniel Neuhauser

In a previous paper we developed a method, Filter-Diagonalization, for extracting eigenvalues and eigenstates of a given operator at any desired energy range. In essence, the method eliminates correlation between distant eigenstates through a short-time filter while correlations between closely lying states are eliminated by diagonalization. Here we extend Filter-Diagonalization. When used to extract eigenvalues for a given operator H, we show that all eigenvalue information is directly extracted from a short segment of the correlation function C(t)=(ψ(0)‖e−iHt‖ψ(0)), or alternately from a small number of residues (ψ(0)‖Rn(H)‖ψ(0)), where ψ(0) is a random initial function and Rn(H) is any desired polynomial expansion in H. The implications of this feature are twofold. First, in contrast to the previous version the wave packet needs only to be propagated once (to prepare C(t)), and eigenstates at all desired energy windows can then be extracted with negligible extra computation time (and negligible storage requirements). In a simulation presented here, accurate eigenvalues are extracted using propagation times which are only a 0.0041 fraction of the ‘‘natural’’ time, i.e., the time by which the relative phase of the two closest eigenstates reaches 2π. The second and more important feature is that the method is automatically suitable for extracting eigenvalues (or normal modes) using a short-time segment of any signal C(t) which is a sum of (unknown) Fourier components (C(t)=∑ndne−iεnt) regardless of its origin. In addition to its use for determining eigenvalues of known operators, the method may also be utilized to extract normal modes from classical-dynamics simulations, eigenstates from real-time Quantum Monte-Carlo studies, frequencies from experimental optical or electrical signals, or be utilized in any other circumstance where a correlation function or general signal is only known for short times (or expensive to generate at long times).

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A linear‐scaling time‐dependent density functional theory is developed. The equation of motion is solved for the reduced single‐electron density matrix in the real time domain. Chebyshev expansion is used for integration in time domain. Filter diagonalization is implemented to determine the excited state energies. The locality of the reduced single‐electron density matrix is utilized to ensure computational time scales linearly with system size. We summarize these methods in this brief review.

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We present the time-domain theory of spectral filters, starting with the basic propositions of the theory of measurement in quantum mechanics, and develop its parameter-free implementation in the traditional correlation function as well as the filter diagonalization (FD) form. The present study unifies all the time-domain spectral filter algorithms in the literature, under a single theme which is based on the notion of selective measurements. For specific numerical purposes, we have selected Chebyshev polynomials for developing the time propagator and this permits us to carry out the relevent time integrals fully analytically and obtain FD equations in a numerically convenient form. We also argue that the FD method is a particular realization of the general spectral filter goal and it is constrained, in general, by the time-energy uncertainty regime at least as much as the correlation-function-based method. To contrast the performance of the correlation function and the FD methods, we have carried out the detailed numerical experiments on a model system, which suggest that the FD method needs almost as much time propagation as the correlation function method, in order to identify the correct spectrum. The difference lies in the procedure for the exact location of eigenvalue positions, for which the FD method employs a diagonalization step while the correlation function method involves the location of zeros.
We address the issues raised in the preceding comment by Mandelshtam and Carrington [Phys. Rev. E 65, 028701 (2002)], concerning the eigenvalue determination by the spectral filter methods. We argue that the Fourier transformation is the building block of all currently known time-domain spectral filter algorithms, and, therefore, the time-energy uncertainty principle affects them all in a similar manner. We also explain the situation when the correlation function method may be less suitable, in comparison to the filter diagonalization method, for the determination of eigenvalues, even though both share the same fundamental principles.
In a previous paper we developed a method, Filter-Diagonalization, for extracting eigenvalues and eigenstates of a given operator at any desired energy range. In essence, the method eliminates correlation between distant eigenstates through a short-time filter while correlations between closely lying states are eliminated by diagonalization. Here we extend Filter-Diagonalization. When used to extract eigenvalues for a given operator H, we show that all eigenvalue information is directly extracted from a short segment of the correlation function C(t)=(ψ(0)‖e−iHt‖ψ(0)), or alternately from a small number of residues (ψ(0)‖Rn(H)‖ψ(0)), where ψ(0) is a random initial function and Rn(H) is any desired polynomial expansion in H. The implications of this feature are twofold. First, in contrast to the previous version the wave packet needs only to be propagated once (to prepare C(t)), and eigenstates at all desired energy windows can then be extracted with negligible extra computation time (and negligible storage requirements). In a simulation presented here, accurate eigenvalues are extracted using propagation times which are only a 0.0041 fraction of the ‘‘natural’’ time, i.e., the time by which the relative phase of the two closest eigenstates reaches 2π. The second and more important feature is that the method is automatically suitable for extracting eigenvalues (or normal modes) using a short-time segment of any signal C(t) which is a sum of (unknown) Fourier components (C(t)=∑ndne−iεnt) regardless of its origin. In addition to its use for determining eigenvalues of known operators, the method may also be utilized to extract normal modes from classical-dynamics simulations, eigenstates from real-time Quantum Monte-Carlo studies, frequencies from experimental optical or electrical signals, or be utilized in any other circumstance where a correlation function or general signal is only known for short times (or expensive to generate at long times).
Continuously monitored quantum systems are emerging as promising platforms for quantum metrology, where a central challenge is to identify measurement strategies that optimally extract information about unknown parameters encoded in the complex quantum state of emitted radiation. Different measurement strategies effectively access distinct temporal modes of the emitted field, and the resulting choice of mode can strongly impact the information available for parameter estimation. While a ubiquitous approach in quantum optics is to select frequency modes through spectral filtering, the metrological potential of this technique has not yet been systematically quantified. We develop a theoretical framework to assess this potential by modeling spectral detection as a cascaded quantum system, allowing us to reconstruct the full density matrix of frequency-filtered photonic modes and to compute their associated Fisher information. This framework provides a minimal yet general method to benchmark the performance of spectral measurements in quantum optics, allowing to identify optimal filtering strategies in terms of frequency selection, detector linewidth, and metrological gain accessible through higher-order frequency-resolved correlations and mean-field engineering. These results lay the groundwork for identifying and designing optimal sensing strategies in practical quantum-optical platforms.
Quantum mechanics and the theory of operators on Hilbert space have been deeply linked since their beginnings in the early twentieth century. States of a quantum system correspond to certain elements of the configuration space and observables correspond to certain operators on the space. This book is a brief, but self-contained, introduction to the mathematical methods of quantum mechanics, with a view towards applications to Schrodinger operators. Part 1 of the book is a concise introduction to the spectral theory of unbounded operators. Only those topics that will be needed for later applications are covered. The spectral theorem is a central topic in this approach and is introduced at an early stage. Part 2 starts with the free Schrodinger equation and computes the free resolvent and time evolution. Position, momentum, and angular momentum are discussed via algebraic methods. Various mathematical methods are developed, which are then used to compute the spectrum of the hydrogen atom. Further topics include the nondegeneracy of the ground state, spectra of atoms, and scattering theory. This book serves as a self-contained introduction to spectral theory of unbounded operators in Hilbert space with full proofs and minimal prerequisites: Only a solid knowledge of advanced calculus and a one-semester introduction to complex analysis are required. In particular, no functional analysis and no Lebesgue integration theory are assumed. It develops the mathematical tools necessary to prove some key results in nonrelativistic quantum mechanics. Mathematical Methods in Quantum Mechanics is intended for beginning graduate students in both mathematics and physics and provides a solid foundation for reading more advanced books and current research literature. This new edition has additions and improvements throughout the book to make the presentation more student friendly.
This paper provides an introduction to quantum filtering theory. An introduction to quantum probability theory is given, focusing on the spectral theorem and the conditional expectation as a least squares estimate, and culminating in the construction of Wiener and Poisson processes on the Fock space. We describe the quantum Itô calculus and its use in the modeling of physical systems. We use both reference probability and innovations methods to obtain quantum filtering equations for system-probe models from quantum optics.
A linear‐scaling time‐dependent density functional theory is developed. The equation of motion is solved for the reduced single‐electron density matrix in the real time domain. Chebyshev expansion is used for integration in time domain. Filter diagonalization is implemented to determine the excited state energies. The locality of the reduced single‐electron density matrix is utilized to ensure computational time scales linearly with system size. We summarize these methods in this brief review.