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Quantum effects for fast battery charging

01.06.2017

An international team of scientists has made theoretical studies, the results of which have shown that a matrix consisting of nano-sized rechargeable batteries can be charged much faster than each such battery individually. This phenomenon is the result of the so-called collective quantum interactions, which form the basis of a relatively new field - quantum thermodynamics. This area deals with the study of how quantum effects affect the laws of traditional physics that determine such fundamental quantities as energy, work, etc.

The vast majority of studies related to the practical use of the phenomena of quantum mechanics are aimed at the transfer of quantum information and its processing in the depths of the developed quantum computing systems. And only in isolated cases are studies demonstrating the advantages of using quantum effects in other areas, in particular, in thermodynamics. Only recently has it been shown how the phenomenon of quantum entanglement can allow more work to be done using energy drawn from a single nanoscale device, a kind of "quantum battery".

In new research, scientists have shown that quantum phenomena not only allow more efficient use of energy, they can also increase the capacity and shorten the charge time of the aforementioned quantum batteries. Moreover, this process does not require the presence of quantum entanglement, although this requires a number of conditions required to create quantum entanglement.

"We have demonstrated that it is quite possible to organize a quantum interaction between two or large bodies without involving quantum entanglement in this phenomenon," write the researchers from Monash University, Australia. all of these are bodies, quantum nanosized batteries in this case."

The quantum advantage provided by interactions also has its limits, which determine the minimum possible time for charging batteries due to quantum effects. The main limiting factor is the factor of quantum speed, the maximum speed of quantum processes, which determines both the limits of quantum thermodynamic processes and the speed of future quantum computers.

"Quantum bits, the qubits of quantum computers that are ions or atoms, can also be thought of as tiny quantum batteries to which our research can be applied," the researchers write. quantum advantage in systems with a finite number of bodies involved wherever possible.This, in turn, could lead to a whole range of fantastic technologies, ranging from tiny batteries comparable in capacity to large batteries, nanomachines capable of performing large amount of work and much more.

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New low-cost processor delivers 1000x faster video 17.08.2013

A group of researchers from the University of Michigan, USA, led by Professor Wei Lu, has begun developing a chip designed to process video data. The scientists' goal is to make the processor process video a thousand times faster than current solutions while consuming 10 times less electrical energy, according to Phys.org.

According to Lou, there are so many video surveillance systems in the world today that there will soon be a point where existing computing systems will no longer be able to keep up with the amount of video data generated. "We plan to create a system that will be able to do this," the scientist said.

The chip, which American scientists have begun to develop, is characterized as a "processor with a self-organizing adaptive neural network." This network will be made of standard transistors, as well as new elements called memristors. A memristor is a memory resistor whose resistance value depends on how much current was previously passed through the element. The new chip will be able to self-learn and process a large number of signals in parallel, scientists say.

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“The idea is based on the premise that most of the video information is noise,” Lu explains. “Instead of processing and transmitting all this noise, an adaptive neural network will be able to isolate key information and reconstruct the image based on a small portion of data.”

The work will take place in two stages. At the first stage, scientists plan to use memristors as memory in addition to conventional connections to equip the chip with a self-learning mechanism. At the second stage, it is planned to replace the junctions of traditional electrical circuits with memristors, that is, to make synapses out of them, creating an analogue of the brain of a living being.

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