ICFO – The Institute of Photonic Sciences

About: ICFO – The Institute of Photonic Sciences is a facility organization based out in Barcelona, Spain. It is known for research contribution in the topics: Quantum & Quantum entanglement. The organization has 872 authors who have published 1965 publications receiving 56273 citations.

Continuous-wave multiphoton photoemission from plasmonic nanostars

Abstract: Highly nonlinear optical processes require high intensities, typically achieved with ultrashort laser pulses, and hence, they were first observed with the advent of picosecond laser technology. An alternative approach for reaching the required field intensities is offered by localized optical resonances in tailored plasmonic nanostructures, enabling the enhancement of a multitude of nonlinear phenomena. However, so far, plasmon-enhanced high-order nonlinear effects have been restricted to experiments involving short-pulsed and ultrafast laser sources. Here, we demonstrate localized three-photon photoemission from chemically synthesized plasmonic gold nanostars under continuous-wave illumination at sub-MWcm−2 incident intensities. Intensity- and polarization-dependent measurements confirm the nonlinearity of the photoemission process and agree with quantum mechanical calculations of the electron yield from nanostar tips with features smaller than 5 nm, which facilitate local intensity enhancement factors exceeding 1000. Our results open up new avenues for the design of accessible nanoscale coherent electron sources, with potential applications in microscopy, spectroscopy, sensing, and signal processing. Photoemission from nanostructures has raised considerable interest in recent years. The authors propose a low-budget scheme for multiphoton photemission with a continuous-wave laser that may inspire design of accessible nanoscale coherent electron sources.

Excitation-multiplexed multicolor superresolution imaging with fm-STORM and fm-DNA-PAINT

Abstract: Recent advancements in single-molecule-based superresolution microscopy have made it possible to visualize biological structures with unprecedented spatial resolution. Determining the spatial coorganization of these structures within cells under physiological and pathological conditions is an important biological goal. This goal has been stymied by the current limitations of carrying out superresolution microscopy in multiple colors. Here, we develop an approach for simultaneous multicolor superresolution imaging which relies solely on fluorophore excitation, rather than fluorescence emission properties. By modulating the intensity of the excitation lasers at different frequencies, we show that the color channel can be determined based on the fluorophore's response to the modulated excitation. We use this frequency multiplexing to reduce the image acquisition time of multicolor superresolution DNA-PAINT while maintaining all its advantages: minimal color cross-talk, minimal photobleaching, maximal signal throughput, ability to maintain the fluorophore density per imaged color, and ability to use the full camera field of view. We refer to this imaging modality as "frequency multiplexed DNA-PAINT," or fm-DNA-PAINT for short. We also show that frequency multiplexing is fully compatible with STORM superresolution imaging, which we term fm-STORM. Unlike fm-DNA-PAINT, fm-STORM is prone to color cross-talk. To overcome this caveat, we further develop a machine-learning algorithm to correct for color cross-talk with more than 95% accuracy, without the need for prior information about the imaged structure.

Bayesian deep learning on a quantum computer

Abstract: Bayesian methods in machine learning, such as Gaussian processes, have great advantages compared to other techniques. In particular, they provide estimates of the uncertainty associated with a prediction. Extending the Bayesian approach to deep architectures has remained a major challenge. Recent results connected deep feedforward neural networks with Gaussian processes, allowing training without backpropagation. This connection enables us to leverage a quantum algorithm designed for Gaussian processes and develop a new algorithm for Bayesian deep learning on quantum computers. The properties of the kernel matrix in the Gaussian process ensure the efficient execution of the core component of the protocol, quantum matrix inversion, providing at least a polynomial speedup over classical algorithms. Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.

Reconstructing the quantum critical fan of strongly correlated systems using quantum correlations.

Abstract: Albeit occurring at zero temperature, quantum critical phenomena have a huge impact on the finite-temperature phase diagram of strongly correlated systems, giving experimental access to their observation. Indeed, the existence of a gapless, zero-temperature quantum critical point induces the existence of an extended region in parameter space—the quantum critical fan (QCF)—characterized by power-law temperature dependences of all observables. Identifying experimentally the QCF and its crossovers to other regimes (renormalized classical, quantum disordered) remains nonetheless challenging. Focusing on paradigmatic models of quantum phase transitions, here we show that quantum correlations—captured by the quantum variance of the order parameter—exhibit the temperature scaling associated with the QCF over a parameter region much broader than that revealed by ordinary correlations. The link existing between the quantum variance and the dynamical susceptibility paves the way to an experimental reconstruction of the QCF using spectroscopic techniques. At a zero-temperature phase transition, quantum, rather than thermal, fluctuations determine the behaviour both at the transition and in a finite temperature ‘quantum critical’ region. Here the authors give a quantitative definition of the quantum critical region that could be applied to experimental data.

Scalable and Tunable Periodic Graphene Nanohole Arrays for Mid-Infrared Plasmonics.

Abstract: Despite its great potential for a wide variety of devices, especially mid-infrared biosensors and photodetectors, graphene plasmonics is still confined to academic research. A major reason is the fact that, so far, expensive and low-throughput lithography techniques are needed to fabricate graphene nanostructures. Here, we report for the first time a detailed experimental study on electrostatically tunable graphene nanohole array surfaces with periods down to 100 nm, showing clear plasmonic response in the range ∼1300-1600 cm-1, which can be fabricated by a scalable nanoimprint technique. Such large area plasmonic nanostructures are suitable for industrial applications, for example, surface-enhanced infrared absorption (SEIRA) sensing, as they combine easy design, extreme field confinement, and the possibility to excite multiple plasmon modes enabling multiband sensing, a feature not readily available in nanoribbons or other localized resonant structures.