Lifeboat Foundation

Optical nano-antennas are ideal to enhance light-matter interactions at the nanometer scale. Yet in most designs, the region of maximum field localization and enhancement, the “hotspot”, is not readily accessible since it is buried into the nanostructure.

In a recent collaboration between EPFL in Lausanne, Fresnel Institute in Marseille and ICFO groups led by ICREA Professors at ICFO Maria Garcia-Parajo and Niek van Hulst, researchers present a new nanofabrication technique that applies planarization, etch back and template stripping to expose the excitation hotspot at the surface.

The large flat surface arrays of in-plane nano-antennas feature gaps as small as 10 nm with sharp edges, excellent reproducibility and full surface accessibility of the hotspot confined region. The novel fabrication approach drastically improves the optical performance of plasmonic nano-antennas to yield giant fluorescence enhancement factors, together with nanoscale detection volumes in the 20 zepto-liter range.

Nice advancement in QC.

Trapped ions calculate more reliably but superconducting circuits are faster.

More on the QC Blueprint which enables others to use as a reference when building a QC.

According to Prof Winfried Hensinger of the University of Sussex in the United Kingdom, he and his team have the first practical design for a quantum computer. Like millions of others, I have struggled to come to an understanding of quantum mechanics and how a quantum computer might work.

It would use qubits rather than standard on/off or 1 and 0 bits used in traditional computers. A qubit can have a state of anywhere between zero and one, including all the “states” in between. Theoretically, a quantum computer can perform a very large number of calculations simultaneously using the ideas of super positioning and quantum entanglement. The theory is that all the necessary calculations are carried out at virtually the same time, e.g. working out all the factors of a very large number. This kind of problem can take a regular computer quite a while.

Continue reading “A quantum leap for computers” »

A photonic nanostructure for constructing quantum photonic circuits for quantum networks has been developed, which could impact the optics and photoni.

Sharing in case folks would like to listen in.

Microsoft’s Station Q was founded in 2006. The focus of the team has always been topological quantum computing. By taking a full systems architecture approach, we have reached the point where we now able to start engineering a scalable quantum computer. The goal is to be able to solve major problems in areas of interest (e.g., Chemistry, Materials and Machine Learning). This talk will focus on the types of applications that we will be trying to solve as well as the unique approach to quantum computation that we’ve developed. For reference, see:

Current Approach: https://arxiv.org/abs/1610.05289 Chemistry Application: https://arxiv.org/abs/1605.03590 Other papers: https://arxiv.org/find/all/1/all:+wecker_d/0/1/0/all/0/1

I never doubt the theory.

We owe a lot to Einstein, and this week physicists have confirmed another of his theories by unraveling and proving that quantum entanglement does in fact exist. Under the standard quantum theory, nothing has a definitive state until it’s measured, and when two particles interact they become entangled. Being entangled means no longer do the particles have their probabilities but one that includes both particles together. Even though two photons become entangled, they can still travel light years apart from each other, but they will always remain linked.

It was a great moment for physicists and scientists around the world in 2016 when one of the greatest ever scientific discoveries was announced. Although technically the first gravitational waves were detected in 2015, it wasn’t until further detections were made in 2016 that scientists finally conceded they did exist and that Albert Einstein’s theory of relativity could finally be proved. Following on from that, scientists also discovered that as well as a great detector LIGO is the best producer of gravitational waves.

The majority of quantum dot (QD) blinking studies have used a model of switching between two distinct fluorescence intensity levels, “on” and “off”. However, a distinct intermediate intensity level has been identified in some recent reports – a so-called “grey” or “dim” state, which has brought this binary model into question. While this grey state has been proposed to result from the formation of a trion, it is still unclear under which conditions it is present in a QD. By performing shell-dependent blinking studies on CdSe QDs, we report that the populations of the grey state and the on state are strongly dependent on both the shell material and its thickness. We found that adding a ZnS shell did not result in a significant population of the grey state. Using ZnSe as the shell material resulted in a slightly higher population of the grey state, although it was still poorly resolved. However, adding a CdS shell resulted in the population of a grey state, which depended strongly on its thickness up to 5 ML. Interestingly, while the frequency of transitions to and from the grey state showed a very strong dependence on CdS shell thickness, the brightness of and the dwell time in the grey state did not. Moreover, we found that the grey state acts as an on-pathway intermediate state between on and off states, with the thickness of the shell determining the transition probability between them. We also identified two types of blinking behavior in QDs, one that showed long-lived but lower intensity on states and another that showed short-lived but brighter on states that also depended on the shell thickness. Intensity-resolved single QD fluorescence lifetime analysis was used to identify the relationship between the various exciton decay pathways and the resulting intensity levels. We used this data to propose a model in which multiple on, grey and off states exist whose equilibrium populations vary with time that give rise to the various intensity levels of single QDs, and which depends on shell composition and thickness.

View: PDF | PDF w/ Links.

In the past, traditional methods to understand the behavior of quantum interacting systems have worked well, but there are still many unsolved problems. To solve them, Giuseppe Carleo of ETH Zurich, Switzerland, used machine learning to form a variational approach to the quantum many-body problem.

Before digging deeper, let me tell you a little about the many-body problem. It deals with the difficulty of analyzing “multiple nontrivial relationships encoded in the exponential complexity of the many-body wave function.” In simpler language, it’s the study of interactions between many quantum particles.

If we take a look at our current computing power, modeling a wave function will need lot more powerful supercomputers. But, according to Carleo, the neural networks are pretty good at generalizing. Hence, they need only limited information to infer something. So, fiddling with this idea, Carleo and Matthias Troyer created a simple neural network to reconstruct such multi-body wave function.