The mystery of why quantum matter jumps from a wave-like state to a well-defined particle when it is observed has puzzled scientists for nearly a 100 years.
Known as ‘the measurement problem’ it is widely seen as the major complication in quantum theory and has led even well-respected scientists to suggest that the human mind may be having some kind of telepathic influence on the fabric of the universe — our thoughts actually shaping reality around us.
But physicist Jonathan Kerr, who has studied quantum mechanics for 35 years from his cottage in Surrey, believes he has solved the riddle, and the answer is more prosaic than some might have hoped.
Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals. In a new report on Science Advances Tyler W. Hughes and a research team in the departments of Applied Physics and Electrical Engineering at Stanford University, California, identified mapping between the dynamics of wave physics and computation in recurrent neural networks.
The map indicated the possibility of training physical wave systems to learn complex features in temporal data using standard training techniques used for neural networks. As proof of principle, they demonstrated an inverse-designed, inhomogeneous medium to perform English vowel classification based on raw audio signals as their waveforms scattered and propagated through it. The scientists achieved performance comparable to a standard digital implementation of a recurrent neural network. The findings will pave the way for a new class of analog machine learning platforms for fast and efficient information processing within its native domain.
The recurrent neural network (RNN) is an important machine learning model widely used to perform tasks including natural language processing and time series prediction. The team trained wave-based physical systems to function as an RNN and passively process signals and information in their native domain without analog-to-digital conversion. The work resulted in a substantial gain in speed and reduced power consumption. In the present framework, instead of implementing circuits to deliberately route signals back to the input, the recurrence relationship occurred naturally in the time dynamics of the physics itself. The device provided the memory capacity for information processing based on the waves as they propagated through space.
Physicists Neil Turok and Sabine Hossenfelder are among those who worry that physics is in a funk, in part because of the love of “beautiful” mathematics.
In 1900, so the story goes, prominent physicist Lord Kelvin addressed the British Association for the Advancement of Science with these words: “There is nothing new to be discovered in physics now.”
How wrong he was. The following century completely turned physics on its head. A huge number of theoretical and experimental discoveries have transformed our understanding of the universe, and our place within it.
Don’t expect the next century to be any different. The universe has many mysteries that still remain to be uncovered – and new technologies will help us to solve them over the next 50 years.
Scientists at the Faculty of Physics and Engineering, working with the Tomsk company Scientific and Production Center Chemical Technologies, have created and tested an improved model of a hybrid rocket engine. The team synthesized new fuel components that increased its calorie content, and therefore its efficiency.
The development emerged from a project to improve the design of a solid–fuel hybrid rocket engine and the fuel used in such engines. The scientists mathematically modeled an optimized engine and made fuel compositions based on aluminum diboride and dodecaboride. This is one of the most promising areas increasing fuel efficiency.
Rocket fuel with the addition of the components proposed by TSU specialists is distinguished by the highest calorific value, which characterizes fuel efficiency. Alexander Zhukov, professor at the Department of Mathematical Physics says that boron is the highest-energy solid component known today, but directly introducing it into the fuel is inefficient because a dense oxide film forms, leading to a high degree of burning out. But in combination with aluminum, boron burns well and increases energy.
It’s no secret that the average smart phone today packs an abundance of gadgets fitting in your pocket, which could have easily filled a car trunk a few decades ago. We like to think about video cameras, music playing equipment, and maybe even telephones here, but let’s not ignore the amount of measurement equipment we also carry around in form of tiny sensors nowadays. How to use those sensors for educational purposes to teach physics is presented in [Sebastian Staacks]’ talk at 36C3 about the phyphox mobile lab app.
While accessing a mobile device’s sensor data is usually quite straightforwardly done through some API calls, the phyphox app is not only a shortcut to nicely graph all the available sensor data on the screen, it also exports the data for additional visualization and processing later on. An accompanying experiment editor allows to define custom experiments from data capture to analysis that are stored in an XML-based file format and possible to share through QR codes.
Aside from demonstrating the app itself, if you ever wondered how sensors like the accelerometer, magnetometer, or barometric pressure sensor inside your phone actually work, and which one of them you can use to detect toilet flushing on an airplane and measure elevator velocity, and how to verify your HDD spins correctly, you will enjoy the talk. If you just want a good base for playing around with sensor data yourself, it’s all open source and available on GitHub for both Android and iOS.
We all feel the passing of time, but nothing in physics suggests it is a fundamental property of the universe. So where does our sense of time’s flow come from?
Planets beyond our galaxy could be discovered using gravitational waves. Such worlds would be like Magrathea in ‘The Hitchhiker’s Guide to the Galaxy.’
A European team of researchers including physicists from the University of Konstanz has found a way of transporting electrons at times below the femtosecond range by manipulating them with light. This could have major implications for the future of data processing and computing.
Contemporary electronic components, which are traditionally based on silicon semiconductor technology, can be switched on or off within picoseconds (i.e. 10-12 seconds). Standard mobile phones and computers work at maximum frequencies of several gigahertz (1 GHz = 109 Hz) while individual transistors can approach one terahertz (1 THz = 1012 Hz). Further increasing the speed at which electronic switching devices can be opened or closed using the standard technology has since proven a challenge. A recent series of experiments – conducted at the University of Konstanz and reported in a recent publication in Nature Physics – demonstrates that electrons can be induced to move at sub-femtosecond speeds, i.e. faster than 10-15 seconds, by manipulating them with tailored light waves.
“This may well be the distant future of electronics,” says Alfred Leitenstorfer, Professor of Ultrafast Phenomena and Photonics at the University of Konstanz (Germany) and co-author of the study. “Our experiments with single-cycle light pulses have taken us well into the attosecond range of electron transport”. Light oscillates at frequencies at least a thousand times higher than those achieved by purely electronic circuits: One femtosecond corresponds to 10-15 seconds, which is the millionth part of a billionth of a second. Leitenstorfer and his team from the Department of Physics and the Center for Applied Photonics (CAP) at the University of Konstanz believe that the future of electronics lies in integrated plasmonic and optoelectronic devices that operate in the single-electron regime at optical – rather than microwave – frequencies. “However, this is very basic research we are talking about here and may take decades to implement,” he cautions.
