Microscopic spinon-chargon theory of magnetic polarons in the t-J model

The interplay of spin and charge degrees of freedom, introduced by doping mobile holes into a Mott insulator with strong anti-ferromagnetic (AFM) correlations, is at the heart of strongly correlated matter such as high-Tc cuprate superconductors. Here we capture this interplay in the strong coupling regime and propose a trial wavefunction of mobile holes in an AFM. Our method provides a microscopic justification for a class of theories which describe doped holes moving in an AFM environment as meson-like bound states of spinons and chargons. We discuss a model of such bound states from the perspective of geometric strings, which describe a fluctuating lattice geometry introduced by the fast motion of the chargon. This is demonstrated to give rise to short-range hidden string order, signatures of which have recently been revealed by ultracold atom experiments. We present evidence for the existence of such short-range hidden string correlations also at zero temperature by performing numerical DMRG simulations. To test our microscopic approach, we calculate the ground state energy and dispersion relation of a hole in an AFM, as well as the magnetic polaron radius, and obtain good quantitative agreement with advanced numerical simulations at strong couplings. We discuss extensions of our analysis to systems without long range AFM order to systems with short-range magnetic correlations.Comment: 13 pages, 11 figure

Fluctuation based interpretable analysis scheme for quantum many-body snapshots

Microscopically understanding and classifying phases of matter is at the heart of strongly-correlated quantum physics. With quantum simulations, genuine projective measurements (snapshots) of the many-body state can be taken, which include the full information of correlations in the system. The rise of deep neural networks has made it possible to routinely solve abstract processing and classification tasks of large datasets, which can act as a guiding hand for quantum data analysis. However, though proven to be successful in differentiating between different phases of matter, conventional neural networks mostly lack interpretability on a physical footing. Here, we combine confusion learning with correlation convolutional neural networks, which yields fully interpretable phase detection in terms of correlation functions. In particular, we study thermodynamic properties of the 2D Heisenberg model, whereby the trained network is shown to pick up qualitative changes in the snapshots above and below a characteristic temperature where magnetic correlations become significantly long-range. We identify the full counting statistics of nearest neighbor spin correlations as the most important quantity for the decision process of the neural network, which go beyond averages of local observables. With access to the fluctuations of second-order correlations -- which indirectly include contributions from higher order, long-range correlations -- the network is able to detect changes of the specific heat and spin susceptibility, the latter being in analogy to magnetic properties of the pseudogap phase in high-temperature superconductors. By combining the confusion learning scheme with transformer neural networks, our work opens new directions in interpretable quantum image processing being sensible to long-range order.Comment: 15+3 page

The dynamic structure factor in impurity-doped spin chains

The effects of impurities in spin-1/2 Heisenberg chains are recently experiencing a renewed interest due to experimental realizations in solid state systems and ultra-cold gases. The impurities effectively cut the chains into finite segments with a discrete spectrum and characteristic correlations, which have a distinct effect on the dynamic structure factor. Using bosonization and the numerical Density Matrix Renormalization Group we provide detailed quantitative predictions for the momentum and energy resolved structure factor in doped systems. Due to the impurities, spectral weight is shifted away from the antiferromagnetic wave-vector $k=\pi$ into regions which normally have no spectral weight in the thermodynamic limit. The effect can be quantitatively described in terms of scaling functions, which are derived from a recurrence relation based on bosonization.Comment: submitted version, 12 pages, 2 figures, latest version and more information can be found at https://www.physik.uni-kl.de/eggert/papers/index.htm

Dichotomy of heavy and light pairs of holes in the t−Jt-J model

A key step in unraveling the mysteries of materials exhibiting unconventional superconductivity is to understand the underlying pairing mechanism. While it is widely agreed upon that the pairing glue in many of these systems originates from antiferromagnetic spin correlations, a microscopic description of pairs of charge carriers remains lacking. Here we use state-of-the art numerical methods to probe the internal structure and dynamical properties of pairs of charge carriers in quantum antiferromagnets in four-legged cylinders. Exploiting the full momentum resolution in our simulations, we are able to distinguish two qualitatively different types of bound states: a highly mobile, meta-stable pair, which has a dispersion proportional to the hole hopping $t$, and a heavy pair, which can only move due to spin exchange processes and turns into a flat band in the Ising limit of the model. Understanding the pairing mechanism can on the one hand pave the way to boosting binding energies in related models, and on the other hand enable insights into the intricate competition of various phases of matter in strongly correlated electron systems.Comment: 6+7 pages, 5+10 figures; updated, corrected versio

Pairing of holes by confining strings in antiferromagnets

In strongly correlated quantum materials, the behavior of charge carriers is dominated by strong electron-electron interactions. These can lead to insulating states with spin order, and upon doping to competing ordered states including unconventional superconductivity. The underlying pairing mechanism remains poorly understood however, even in strongly simplified theoretical models. Recent advances in quantum simulation allow to study pairing in paradigmatic settings, e.g. in the $t-J$ and $t-J_z$ Hamiltonians. Even there, the most basic properties of paired states of only two dopants, such as their dispersion relation and excitation spectra, remain poorly studied in many cases. Here we provide new analytical insights into a possible string-based pairing mechanism of mobile holes in an antiferromagnet. We analyze an effective model of partons connected by a confining string and calculate the spectral properties of bound states. Our model is equally relevant for understanding Hubbard-Mott excitons consisting of a bound doublon-hole pair or confined states of dynamical matter in lattice gauge theories, which motivates our study of different parton statistics. Although an accurate semi-analytic estimation of binding energies is challenging, our theory provides a detailed understanding of the internal structure of pairs. For example, in a range of settings we predict heavy states of immobile pairs with flat-band dispersions -- including for the lowest-energy $d$-wave pair of fermions. Our findings shed new light on the long-standing question about the origin of pairing and competing orders in high-temperature superconductors.Comment: 17 pages, 12 figures, 2 appendice

Magnetic polarons beyond linear spin-wave theory: Mesons dressed by magnons

When a mobile hole is doped into an antiferromagnet, its movement will distort the surrounding magnetic order and yield a magnetic polaron. The resulting complex interplay of spin and charge degrees of freedom gives rise to very rich physics and is widely believed to be at the heart of high-temperature superconductivity in cuprates. In this paper, we develop a quantitative theoretical formalism, based on the phenomenological parton description, to describe magnetic polarons in the strong coupling regime. We construct an effective Hamiltonian with weak coupling to the spin-wave excitations in the background, making the use of standard polaronic methods possible. Our starting point is a single hole doped into an AFM described by a 'geometric string' capturing the strongly correlated hopping processes of charge and spin degrees of freedom, beyond linear spin-wave approximation. Subsequently, we introduce magnon excitations through a generalized 1/S expansion and derive an effective coupling of these spin-waves to the hole plus the string (the meson) to arrive at an effective polaron Hamiltonian with density-density type interactions. After making a Born-Oppenheimer-type approximation, this system is solved using the self-consistent Born approximation to extract the renormalized polaron properties. We apply our formalism (i) to calculate beyond linear spin-wave ARPES spectra, (ii) to reveal the interplay of ro-vibrational meson excitations, and (ii) to analyze the pseudogap expected at low doping. Moreover, our work paves the way for exploring magnetic polarons out-of equilibrium or in frustrated systems, where weak-coupling approaches are desirable and going beyond linear spin-wave theory becomes necessary.Comment: 16 pages, 14 figure

Fluctuation based interpretable analysis scheme for quantum many-body snapshots

Microscopically understanding and classifying phases of matter is at the heart of strongly-correlated quantum physics. With quantum simulations, genuine projective measurements (snapshots) of the many-body state can be taken, which include the full information of correlations in the system. The rise of deep neural networks has made it possible to routinely solve abstract processing and classification tasks of large datasets, which can act as a guiding hand for quantum data analysis. However, though proven to be successful in differentiating between different phases of matter, conventional neural networks mostly lack interpretability on a physical footing. Here, we combine confusion learning [Nat. Phys. 13, 435 (2017)] with correlation convolutional neural networks [Nat. Commun. 12, 3905 (2021)], which yields fully interpretable phase detection in terms of correlation functions. In particular, we study thermodynamic properties of the 2D Heisenberg model, whereby the trained network is shown to pick up qualitative changes in the snapshots above and below a characteristic temperature where magnetic correlations become significantly long-range. We identify the full counting statistics of nearest neighbor spin correlations as the most important quantity for the decision process of the neural network, which go beyond averages of local observables. With access to the fluctuations of second-order correlations — which indirectly include contributions from higher order, long-range correlations — the network is able to detect changes of the specific heat and spin susceptibility, the latter being in analogy to magnetic properties of the pseudogap phase in high-temperature superconductors [Phys. Rev. Lett. 62, 957 (1989)]. By combining the confusion learning scheme with transformer neural networks, our work opens new directions in interpretable quantum image processing being sensible to long-range order

Probing finite-temperature observables in quantum simulators with short-time dynamics

Preparing low temperature states in quantum simulators is challenging due to their almost perfect isolation from the environment. Here, we show how finite-temperature observables can be obtained with an algorithm that consists of classical importance sampling of initial states and a measurement of the Loschmidt echo with a quantum simulator. We use the method as a quantum-inspired classical algorithm and simulate the protocol with matrix product states to analyze the requirements on a quantum simulator. This way, we show that a finite temperature phase transition in the long-range transverse field Ising model can be characterized in trapped ion quantum simulators. We propose a concrete measurement protocol for the Loschmidt echo and discuss the influence of measurement noise, dephasing, as well as state preparation and measurement errors. We argue that the algorithm is robust against those imperfections under realistic conditions. The algorithm can be readily applied to study low-temperature properties in various quantum simulation platforms.Comment: 4+3 pages, 4+1 figure

C3NN: Cosmological Correlator Convolutional Neural Network -- an interpretable machine learning tool for cosmological analyses

Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference and various other tasks. However, many models which make use of CNNs are criticized as "black boxes" due to the difficulties in relating their outputs intuitively and quantitatively to the cosmological fields under investigation. To overcome this challenge, we present the Cosmological Correlator Convolutional Neural Network (C3NN) -- a fusion of CNN architecture with the framework of cosmological N-point correlation functions (NPCFs). We demonstrate that the output of this model can be expressed explicitly in terms of the analytically tractable NPCFs. Together with other auxiliary algorithms, we are able to open the "black box" by quantitatively ranking different orders of the interpretable convolution outputs based on their contribution to classification tasks. As a proof of concept, we demonstrate this by applying our framework to a series of binary classification tasks using Gaussian and Log-normal random fields and relating its outputs to the analytical NPCFs describing the two fields. Furthermore, we exhibit the model's ability to distinguish different dark energy scenarios ($w_0=-0.95$ and $-1.05$) using N-body simulated weak lensing convergence maps and discuss the physical implications coming from their interpretability. With these tests, we show that C3NN combines advanced aspects of machine learning architectures with the framework of cosmological NPCFs, thereby making it an exciting tool with the potential to extract physical insights in a robust and explainable way from observational data.Comment: 19 pages, 8 figures, 5 tables; Comments are welcome