Emergent Hydrodynamics in Nonequilibrium Quantum Systems

Abstract

A tremendous amount of recent attention has focused on characterizing the dynamical properties of periodically driven many-body systems. Here, we use a novel numerical tool termed "density matrix truncation" (DMT) to investigate the late-time dynamics of large-scale Floquet systems. We find that DMT accurately captures two essential pieces of Floquet physics, namely, prethermalization and late-time heating to infinite temperature. Moreover, by implementing a spatially inhomogeneous drive, we demonstrate that an interplay between Floquet heating and diffusive transport is crucial to understanding the system's dynamics. Finally, we show that DMT also provides a powerful method for quantitatively capturing the emergence of hydrodynamics in static (undriven) Hamiltonians; in particular, by simulating the dynamics of generic, large-scale quantum spin chains (up to L ¼ 100), we are able to directly extract the energy diffusion coefficient.

Quantum gas microscopy of Kardar-Parisi-Zhang superdiffusion

“…To further explore the superdiffusive dynamics, we investigate the spatially resolved spin profiles. Our experimental observations are in quantitative agreement with simulations based upon novel tensor-network numerical techniques [37,41,42] and conform to KPZ dynamics (Fig. 2A).…”

Long-Range Prethermal Phases of Nonequilibrium Matter

“…As a result, to ensure that τ Ã S L=2 is capturing the same heating timescale τ Ã , the extracted value must be multiplied by a factor of 2 (for more details, see the Appendix of Ref. [103]).…”

Intrinsic and induced quantum quenches for enhancing qubit-based quantum noise spectroscopy

“…Commonly referred to as quantum noise spectroscopy (QNS) [1], this modality allows one to understand and possibly mitigate sources of decoherence that degrade a quantum processor [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. It also serves as a powerful means to probe a complicated manybody target system via its fluctuation properties (see, e.g., [20][21][22][23][24]). While many QNS protocols focus on the more specific problem of characterizing classical Gaussian noise [4][5][6][7][8][9][10][11][12], recent work has explored methods that go beyond these assumptions [13][14][15][16][17][18][19][25][26][27][28][29][30].…”