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arXiv:1904.05067

Generalized Independent Component Analysis for Extracting Eigen-Modes of a Quantum System

Yadong Wu, Hui Zhai

Published Apr 10, 2019• 1 citation
arXivHTMLPDF

Abstract

In many dynamical probes of a quantum system, quite often multiple eigenmodes are excited. Therefore, the experimental data can be quite messy due to the mixing of different modes, as well as the background noise, despite that each mode manifests itself as a single frequency oscillation. Here we develop an unsupervised machine learning algorithm to extract the frequencies of these oscillations from such measurement data, that is, the eigenenergies of these modes. This method is particularly useful when the measurement time is not long enough to perform the Fourier transformation. Our method is inspired by the independent component analysis method and its application to the cocktail party problem. In that problem, the goal is to recover each voice from detectors that detect signals of many mixed voices, and the principle is to find out signals that possess features and are away from a Gaussian distribution. Instead, our generalization is to find out signals that are close to a single frequency oscillation. We demonstrate the advantage of our method by an example of analyzing the collective mode of cold atoms. We believe this method can find broad applications in analyzing data from dynamical experiments in quantum systems.

DOI
10.48550/arxiv.1904.05067

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In many dynamical probes of a quantum system, quite often multiple eigenmodes are excited. Therefore, the experimental data can be quite messy due to the mixing of different modes, as well as the background noise, despite that each mode manifests itself as a single frequency oscillation. Here we develop an unsupervised machine learning algorithm to extract the frequencies of these oscillations from such measurement data, that is, the eigenenergies of these modes. This method is particularly useful when the measurement time is not long enough to perform the Fourier transformation. Our method is inspired by the independent component analysis method and its application to the cocktail party problem. In that problem, the goal is to recover each voice from detectors that detect signals of many mixed voices, and the principle is to find out signals that possess features and are away from a Gaussian distribution. Instead, our generalization is to find out signals that are close to a single frequency oscillation. We demonstrate the advantage of our method by an example of analyzing the collective mode of cold atoms. We believe this method can find broad applications in analyzing data from dynamical experiments in quantum systems.
Abstract Dynamical probes are popular experimental tools for studying quantum systems nowadays. Though a well-defined eigen-mode will manifest itself as a single frequency oscillation in the dynamical probe, the real experimental data can be quite messy because a probe usually excites multiple modes which are also mixed together with random white noise. Moreover, the oscillations usually quickly damp out due to finite decoherence time. These make it difficult to extract the frequencies of these oscillation measurement data, that is, the eigen-energies of these eigen-modes. Here we develop an unsupervised machine learning algorithm to solve this problem. Our method is inspired by the independent component analysis method and its application to the ‘cocktail party problem’, where the goal is to recover each voice from detectors that detect signals of many mixed voices. We demonstrate the advantage of our method by an example of analyzing the collective mode of a Bose–Einstein condensate of atomic gases. We believe that this method can find broad applications in analyzing data of dynamical experiments in quantum systems of different fields.
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).
Advanced data analysis techniques have proved to be crucial for extracting information from noisy images. Here we show that principal component analysis can be successfully applied to ultracold gases to unveil their collective excitations. By analyzing the correlations in a series of images we are able to identify the collective modes which are excited, determine their population, image their eigenfunction, and measure their frequency. Our method allows us to discriminate the relevant modes from other noise components and is robust with respect to the data sampling procedure. It can be extended to other dynamical systems, including cavity polariton quantum gases and trapped ions.
We study non-equilibrium quantum dynamics by performing principal component analysis on the data sets of wavefunction snapshots. We show that a specific transformation of the data sets maximizes the information content in the largest principal component and further enables its connection to certain observables. This connection enables us to explain the dynamical features revealed by such a dimensionality-reduction scheme. We demonstrate this using quantum dynamics of the Heisenberg spin chain, starting from different initial states, and further extend the approach to extract higher-order correlations. Our framework should also be applicable to other unsupervised machine-learning methods based on dimensionality-reduction schemes and is highly relevant to experiments with quantum simulators, including those in higher dimensions.
For a linear system, the response to a stimulus is often superposed by its responses to other decomposed stimuli. In quantum mechanics, a state is the superposition of multiple eigenstates. Here, by taking advantage of the phase difference, a common feature as we identified in data sets, we propose eigen component analysis (ECA), an interpretable linear learning model that incorporates the principle of quantum mechanics into the design of algorithm design for feature extraction, classification, dictionary and deep learning, and adversarial generation, etc. The simulation of ECA, possessing a measurable class-labelclass\text{-}labelclass-label H\mathcal{H}H, on a classical computer outperforms the existing classical linear models. Eigen component analysis network (ECAN), a network of concatenated ECA models, enhances ECA and gains the potential to be not only integrated with nonlinear models, but also an interface for deep neural networks to implement on a quantum computer, by analogizing a data set as recordings of quantum states. Therefore, ECA and ECAN promise to expand the feasibility of linear learning models, by adopting the strategy of quantum machine learning to replace heavy nonlinear models with succinct linear operations in tackling complexity.
To extract useful information about quantum effects in cold atom experiments, one central task is to identify the intrinsic fluctuations from extrinsic system noises of various kinds. As a data processing method, principal component analysis can decompose fluctuations in experimental data into eigenmodes, and give a chance to separate noises originated from different physical sources. In this paper, we demonstrate for Bose-Einstein condensates in one-dimensional optical lattices that the principal component analysis can be applied to time-of-flight images to successfully separate and identify noises from different origins of leading contribution, and can help to reduce or even eliminate noises via corresponding data processing procedures. The attribution of noise modes to their physical origins is also confirmed by numerical analysis within a mean-field theory. As the method does not rely on any a priori knowledge of the system properties, it is potentially applicable to the study of other quantum states and quantum critical regions.