Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the frequent lack of uncertainty discussions and reproducible details raises concerns about their reliability. This study investigates the use of data augmentation, a machine learning technique, to supplement experimental data with bootstrapped samples and quantify the uncertainty of such estimates. Eight datasets obtained from measurements involving a single InGaAs/GaAs epitaxial quantum dot serve as a proof-of-principle example. Analysis of one of the SPS quality metrics derived from efficient histogram fitting of the synthetic samples, i.e. the probability of multi-photon emission events, reveals significant uncertainty contributed by stochastic variability in the Poisson processes that describe detection rates. Ignoring this source of error risks severe overconfidence in both early quality estimates and claims for state-of-the-art SPS devices. Additionally, this study finds that standard least-squares fitting is comparable to using a Poisson likelihood, and expanding averages show some promise for early estimation. Also, reducing background counts improves fitting accuracy but does not address the Poisson-process variability. Ultimately, data augmentation demonstrates its value in supplementing physical experiments; its benefit here is to emphasise the need for a cautious assessment of SPS quality.

Artificial intelligence is part of our modern life by enabling machines to learn useful processes such as speech recognition and digital personal assistants. A crucial question for practical applications is how fast such intelligent machines can learn. An experiment at the University of Vienna has answered this question, showing that quantum technology enables a speed-up in the learning process. The physicists, in an international collaboration within Austria, Germany, the Netherlands, and the USA, have achieved this result by using a quantum processor for single photons as a robot. This work, which con-tributes to the advancement of quantum artificial intelligence for future applications, is published in the current issue of the journal "Nature".

Robots solving computer games, recognizing human voices, or helping in finding optimal medical treatments: those are only a few astonishing examples of what the field of artificial intelligence has produced in the past years. The ongoing race for better machines has led to the question of how and with what means improvements can be achieved. In parallel, huge recent progress in quantum technologies have confirmed the power of quantum physics, not only for its often peculiar and puzzling theories, but also for real-life applications. Over the past few years, many scientists have started to investigate how to bridge these two worlds, and to study in what ways quantum mechanics can prove beneficial for learning robots, or vice versa. Several fascinating results have shown, for example, robots deciding faster on their next move, or the design of new quantum experiments using specific learning techniques.

Quantum computing-based machine learning mainly focuses on quantum computing hardware that is experimentally challenging to realize due to requiring quantum gates that operate at very low temperature. Instead, we demonstrate the existence of a lower performance and much lower effort island on the accuracy-vs-qubits graph that may well be experimentally accessible with room temperature optics. This high temperature "quantum computing toy model" is nevertheless interesting to study as it allows rather accessible explanations of key concepts in quantum computing, in particular interference, entanglement, and the measurement process. We specifically study the problem of classifying an example from the MNIST and Fashion-MNIST datasets, subject to the constraint that we have to make a prediction after the detection of the very first photon that passed a coherently illuminated filter showing the example. Whereas a classical setup in which a photon is detected after falling on one of the 28 28 image pixels is limited to a (maximum likelihood estimation) accuracy of 21.27% for MNIST, respectively 18.27% for Fashion-MNIST, we show that the theoretically achievable accuracy when exploiting inference by optically transforming the quantum state of the photon is at least 41.27% for MNIST, respectively 36.14% for Fashion-MNIST. We show in detail how to train the corresponding transformation with TensorFlow and also explain how this example can serve as a teaching tool for the measurement process in quantum mechanics.

Decision making is a vital function in this age of machine learning and artificial intelligence, yet its physical realization and theoretical fundamentals are still not completely understood. In our former study, we demonstrated that single-photons can be used to make decisions in uncertain, dynamically changing environments. The two-armed bandit problem was successfully solved using the dual probabilistic and particle attributes of single photons. In this study, we present a category theoretic modeling and analysis of single-photon-based decision making, including a quantitative analysis that is in agreement with the experimental results. A category theoretic model reveals the complex interdependencies of subject matter entities in a simplified manner, even in dynamically changing environments. In particular, the octahedral and braid structures in triangulated categories provide a better understanding and quantitative metrics of the underlying mechanisms of a single-photon decision maker. This study provides both insight and a foundation for analyzing more complex and uncertain problems, to further machine learning and artificial intelligence.

Decision making based on behavioral and neural observations of living systems has been extensively studied in brain science, psychology, and other disciplines. Decision-making mechanisms have also been experimentally implemented in physical processes, such as single photons and chaotic lasers. The findings of these experiments suggest that there is a certain common basis in describing decision making, regardless of its physical realizations. In this study, we propose a local reservoir model to account for choice-based learning (CBL). CBL describes decision consistency as a phenomenon where making a certain decision increases the possibility of making that same decision again later, which has been intensively investigated in neuroscience, psychology, etc. Our proposed model is inspired by the viewpoint that a decision is affected by its local environment, which is referred to as a local reservoir. If the size of the local reservoir is large enough, consecutive decision making will not be affected by previous decisions, thus showing lower degrees of decision consistency in CBL. In contrast, if the size of the local reservoir decreases, a biased distribution occurs within it, which leads to higher degrees of decision consistency in CBL. In this study, an analytical approach on local reservoirs is presented, as well as several numerical demonstrations. Furthermore, a physical architecture for CBL based on single photons is discussed, and the effects of local reservoirs is numerically demonstrated. Decision consistency in human decision-making tasks and in recruiting empirical data are evaluated based on local reservoir. In summary, the proposed local reservoir model paves a path toward establishing a foundation for computational mechanisms and the systematic analysis of decision making on different levels.

You can't see the bunny, but the picosecond laser certainly can. In a lab at Stanford, engineers have set up a weird contraption, hiding a toy bunny behind a T-shaped wall. And their complex system of computation and rapidly firing lasers can see around that corner. At least that's the idea behind this technique, which uses the flight paths of the photons in lasers to calculate the shape and position of hidden objects--be they bunnies or passing pedestrians. This system deploys the same very, very precise timing that drives the laser-spewing lidar on a self-driving car.

The human eye is considered to be a wonder of evolution, yet despite decades of study, scientists have been unable to pinpoint just how good people are at seeing in the dark. But now a new study has revealed that our eyes may be far better than we could possibly have imagined in dark conditions. It found that the human eye is capable of detecting as little as a single photon - the subatomic particles that make up light - hitting the retina in a dark room. The study found that the human eye (pictured) is able to detect single photons - tiny particles of light - suggesting that people's night vision may be much more advanced than previously thought More than 30,000 trials were carried out by three men in their 20s, with one wearing contact lenses. For each test, the men sat in a completely dark room while wearing headphones with their heads kept motionless using a headrest.

The human eye is capable of detecting the presence of a single photon, the smallest measurable unit of light, in the dark, researchers said. In a study first published in the journal Nature Communications Tuesday, scientists found that the human eye can sense individual particles, seemingly concluding the quest to test the limits of human vision. "If you imagine this, it is remarkable: a photon, the smallest physical entity with quantum properties of which light consists, is interacting with a biological system consisting of billions of cells, all in a warm and wet environment," Alipasha Vaziri, lead researcher from the Rockefeller University in New York, reportedly said. "The most amazing thing is that it's not like seeing light. It's almost a feeling, at the threshold of imagination," he told the Nature. The experiment was conducted with three subjects who sat in a dark room for nearly 40 minutes and were then told to look into an optical system.

Your eyes may be more sensitive than you ever thought possible. In a study published Tuesday in Nature Communications, researchers report that our warm, wet, multicellular eyes have evolved such a high level of sensitivity that they can, on occasion, detect a single photon aimed at the retina. Even the most sophisticated man-made devices require a cool, temperature-controlled environment to achieve the same feat. A single photon is the the smallest particle that light is made of, and it is extremely hard to see. "It's not like a dim flash of light or anything like that," said Alipasha Vaziri, a quantum physicist at Rockefeller University in New York City and the senior author on the paper.