Sparse sampling for fast quasiparticle-interference mapping

Scanning tunneling microscopy (STM) is a notoriously slow technique; data-recording is serial, which renders complex measurement tasks, such as quasiparticle interference (QPI) mapping, impractical. However, QPI could provide insight into band-structure details of quantum materials that can be inaccessible to angle-resolved photoemission spectroscopy. Here we use compressed sensing (CS) to fundamentally speed-up QPI mapping. We reliably recover the QPI information from a fraction of the usual local density of state measurements. The requirement of CS is naturally fulfilled for QPI, since CS relies on sparsity in a vector domain, here given by few nonzero coefficients in Fourier space. We exemplify CS on a simulated Cu(111) surface using random sampling of uniform and varying probability density. The latter improves QPI recovery and mitigates Fourier artifacts. We further simplify the motion of the STM tip through an open traveling salesman's problem for greater efficiency and use the tip-path for drift correction. We expect that the implications of our CS approach will be transformative for the exploration of two-dimensional quantum materials

Adaptive sparse sampling for quasiparticle interference imaging

Quasiparticle interference imaging (QPI) offers insight into the band structure of quantum materials from the Fourier transform of local density of states (LDOS) maps. Their acquisition with a scanning tunneling microscope is traditionally tedious due to the large number of required measurements that may take several days to complete. The recent demonstration of sparse sampling for QPI imaging showed how the effective measurement time could be fundamentally reduced by only sampling a small and random subset of the total LDOS. However, the amount of required sub-sampling to faithfully recover the QPI image remained a recurring question. Here we introduce an adaptive sparse sampling (ASS) approach in which we gradually accumulate sparsely sampled LDOS measurements until a desired quality level is achieved via compressive sensing recovery. The iteratively measured random subset of the LDOS can be interleaved with regular topographic images that are used for image registry and drift correction. These reference topographies also allow to resume interrupted measurements to further improve the QPI quality. Our ASS approach is a convenient extension to quasiparticle interference imaging that should remove further hesitation in the implementation of sparse sampling mapping schemes

Adaptive Sparse Sampling for Quasiparticle Interference Imaging

Quasiparticle interference imaging (QPI) offers insight into the band structure of quantum materials from the Fourier transform of local density of states (LDOS) maps. Their acquisition with a scanning tunneling microscope is traditionally tedious due to the large number of required measurements that may take several days to complete. The recent demonstration of sparse sampling for QPI imaging showed how the effective measurement time could be fundamentally reduced by only sampling a small and random subset of the total LDOS. However, the amount of required sub-sampling to faithfully recover the QPI image remained a recurring question. Here we introduce an adaptive sparse sampling (ASS) approach in which we gradually accumulate sparsely sampled LDOS measurements until a desired quality level is achieved via compressive sensing recovery. The iteratively measured random subset of the LDOS can be interleaved with regular topographic images that are used for image registry and drift correction. These reference topographies also allow to resume interrupted measurements to further improve the QPI quality. Our ASS approach is a convenient extension to quasiparticle interference imaging that should remove further hesitation in the implementation of sparse sampling mapping schemes.Comment: 10 pages, 5 figure

Fast spectroscopic mapping of two-dimensional quantum materials

The discovery of quantum materials entails extensive spectroscopic studies that are carried out against multitudes of degrees of freedom, such as magnetic field, location, temperature, or doping. As this traditionally involves two or more serial measurement tasks, spectroscopic mapping can become excruciatingly slow. We demonstrate orders of magnitude faster measurements through our combination of sparse sampling and parallel spectroscopy. We exemplify our concept using quasiparticle interference imaging of Au(111) and Bi2Sr2CaCu2O8+δ (Bi2212), as two well-known model systems. Our method is accessible, straightforward to implement with existing setups, and can be easily extended to promote gate or field spectroscopy. In view of further substantial speed advantages, it is setting the stage to fundamentally promote the discovery of quantum materials

Strong hole-photon coupling in planar Ge: probing the charge degree and Wigner molecule states

Semiconductor quantum dots (QDs) in planar germanium (Ge) heterostructures have emerged as frontrunners for future hole-based quantum processors. Notably, the large spin-orbit interaction of holes offers rapid, coherent electrical control of spin states, which can be further beneficial for interfacing hole spins to microwave photons in superconducting circuits via coherent charge-photon coupling. Here, we present strong coupling between a hole charge qubit, defined in a double quantum dot (DQD) in a planar Ge, and microwave photons in a high-impedance ($Z_\mathrm{r} = 1.3 ~ \mathrm{k}\Omega$) superconducting quantum interference device (SQUID) array resonator. Our investigation reveals vacuum-Rabi splittings with coupling strengths up to $g_{0}/2\pi = 260 ~ \mathrm{MHz}$, and a cooperativity of $C \sim 100$, dependent on DQD tuning, confirming the strong charge-photon coupling regime within planar Ge. Furthermore, utilizing the frequency tunability of our resonator, we explore the quenched energy splitting associated with strongly-correlated Wigner molecule (WM) states that emerge in Ge QDs. The observed enhanced coherence of the WM excited state signals the presence of distinct symmetries within related spin functions, serving as a precursor to the strong coupling between photons and spin-charge hybrid qubits in planar Ge. This work paves the way towards coherent quantum connections between remote hole qubits in planar Ge, required to scale up hole-based quantum processors.Comment: 22 pages, 12 figure

Band engineering and study of disorder using topology in compact high kinetic inductance cavity arrays

Superconducting microwave metamaterials offer enormous potential for quantum optics and information science, enabling the development of advanced quantum technologies for sensing and amplification. In the context of circuit quantum electrodynamics, such metamaterials can be implemented as coupled cavity arrays (CCAs). In the continuous effort to miniaturize quantum devices for increasing scalability, minimizing the footprint of CCAs while preserving low disorder becomes paramount. In this work, we present a compact CCA architecture leveraging superconducting NbN thin films presenting high kinetic inductance, which enables high-impedance CCA ($\sim1.5$ k$\Omega$), while reducing the resonator footprint. We demonstrate its versatility and scalability by engineering one-dimensional CCAs with up to 100 resonators and exhibiting multiple bandgaps. Additionally, we quantitatively investigate disorder in the CCAs using symmetry-protected topological SSH modes, from which we extract a resonator frequency scattering of $0.22^{+0.04}_{-0.03}\%$. Our platform opens up exciting new prospects for analog quantum simulations of many-body physics with ultrastrongly coupled emitters

High-kinetic inductance NbN films for high-quality compact superconducting resonators

Niobium nitride (NbN) is a particularly promising material for quantum technology applications, as entails the degree of reproducibility necessary for large-scale of superconducting circuits. We demonstrate that resonators based on NbN thin films present a one-photon internal quality factor above 10$^5$ maintaining a high impedance (larger than 2k$\Omega$), with a footprint of approximately 50x100 $\mu$m$^2$ and a self-Kerr nonlinearity of few tenths of Hz. These quality factors, mostly limited by losses induced by the coupling to two-level systems, have been maintained for kinetic inductances ranging from tenths to hundreds of pH/square. We also demonstrate minimal variations in the performance of the resonators during multiple cooldowns over more than nine months. Our work proves the versatility of niobium nitride high-kinetic inductance resonators, opening perspectives towards the fabrication of compact, high-impedance and high-quality multimode circuits, with sizable interactions.Comment: 12 pages, 8 figure

Weak-signal extraction enabled by deep-neural-network denoising of diffraction data

Removal or cancellation of noise has wide-spread applications for imaging and acoustics. In every-day-life applications, denoising may even include generative aspects which are unfaithful to the ground truth. For scientific applications, however, denoising must reproduce the ground truth accurately. Here, we show how data can be denoised via a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. This way, the neural network learns about the statistical properties of the noise. We demonstrate that using artificial noise (such as Poisson and Gaussian) does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems.Comment: 8 pages, 4 figure