Researchers at the California Institute of Technology (Caltech) have created a new ultrafast camera that can capture impulses images as they move through nerve cells. The camera can also record footage of other ultrafast processes, such as electromagnetic pulse propagation in electronics.
The differentially enhanced compressed ultrafast photography (Diff-CUP) camera technology has been demonstrated to be capable of recording video at 70 trillion frames per second and capturing images of laser pulses travelling at the speed of light.
Diff-CUP combines the same high-speed camera technology seen in other CUP systems with a device known as a Mach-Zehnder interferometer. The interferometer captures images of objects and materials by splitting a laser light beam in two, passing just one of the split beams through an object, and then recombining the beams.
Since light waves are affected by the objects they pass through, with different materials impacting them in different ways, the beam passing through the substance being photographed will have its waves out of sync with those of the other beam.
When the beams are recombined, the out-of-sync waves interfere with each other in patterns that disclose information about the object being photographed -this is called “interferometer.”
Although an electrical pulse moving through a nerve cell cannot be seen with the naked eye or with a standard light microscope, this form of interferometry can detect it. As a result, the Mach-Zehnder interferometer permits imaging of these pulses, and the CUP camera takes images at extremely high frame rates. Seeing nerve signals is critical to scientific understanding, but it has yet to be accomplished due to the lack of speed and sensitivity afforded by contemporary imaging modalities.
The researchers also photographed the transmission of electromagnetic pulses (EMPs), which can travel at nearly the speed of light in some materials. Even though an EMP travels at such a high speed through this material, the camera was able to clearly image it. Imaging propagating signals in peripheral nerves is the first step and it’d be important to image live traffic in a central nervous system, which would shed light on how the brain works.
Moreover, a recent study headed by Caltech and published in the journal Science reveals how machine learning methods running on classical computers can be used to make predictions about quantum systems, thereby assisting researchers in solving some of the most challenging physics and chemistry problems. Although this concept has been offered previously, the new paper is the first to mathematically demonstrate that the strategy works for situations that cannot be solved by traditional algorithms.
At tiny scales, the physical universe is governed by the extraordinarily complicated laws of quantum physics. In this domain, particles can exist simultaneously in two states, or in a superposition of states.
A superposition of states can also result in entanglement, a phenomenon in which particles are linked or correlated without being in physical contact. These bizarre states and relationships, which are prevalent in both natural and man-made materials, are extremely challenging to mathematically define.
This is the first mathematical demonstration that classical machine learning can cross the gap between humans and the quantum ecosystem. Machine learning is a sort of software that simulates the human brain to learn from data.
While prior research has demonstrated that machine learning models can handle certain quantum problems, these models often work in a manner that makes it difficult for academics to understand how the machines arrived at their solutions.
The new research will aid scientists in better comprehending and categorising complex and unusual aspects of quantum matter.
