Researchers from the Technion and Tel Aviv University have developed an innovative method to engineer quantum entanglement in a crystal, using computational learning tools
Newspaper Optica Presents a new way to create entangled photons - a collaboration between researchers from the Technion and Tel Aviv University. One of the first discoveries of quantum physics is the fact that light is made of particles called photons. Most of the light beams we see in everyday life contain a huge amount of photons, but there are interesting applications based precisely on weak light beams. Even very weak - a beam containing a single photon in each pulse of light. Quantum communication, for example, is based on the transfer of information using single photons, where the information is encoded using a certain property of light. Such a feature could be the color of the light; A photon of green light represents the number 0 and a photon of red light represents the number 1. Another possible property is the spatial shape of the photon; A photon whose spatial shape is a circle represents 0, a photon whose shape is a ring represents 1. In both cases the quantum system has two possible states.
Compared to standard communication, which is based on light pulses, each of which has a huge number of photons, quantum communication can guarantee immunity to eavesdropping. More advanced protocols of quantum communication use entanglement between light beams.
Quantum entanglement is a phenomenon in which the quantum states of two or more objects cannot be described as independent states of each of the objects but only in relation to each other; That is, as soon as a measurement is made on one of the intertwined objects, it is immediately reflected in the other object, even in a situation where the objects are far apart. An example of this is two beams of light, each of which contains a single photon, one of which is green and the other is red. We do not know what the color of each beam is, but if we make a measurement and find that the first beam is red, we will know that the second beam is green.
How can light beams of single photons or entangled photons be produced? One of the common methods is a process called "spontaneous parametric down conversion for low frequencies" or in English Spontaneous parametric down conversion - SPDC. This method is based on sending a laser light beam through a non-linear optical crystal. The laser beam has many, many photons, but occasionally, spontaneously, one of the photons will decay inside the non-linear crystal and produce two new photons. Since the two new photons were 'born' at the same time, and the sum of their energy is equal to the energy of the original photon of the laser, they can be used to create interlaced light beams.
To harness the SPDC process for quantum communication applications, for example, conditions must be created that will lead to the formation of those unique photon pairs - one green and one red photon, or one ring-shaped and one circular. The problem is that in a normal SPDC process a lot of photon pairs are created, most of which do not have these desired properties, and the ability to "engineer" the process to allow the creation of photons with the desired spatial properties is extremely limited.
Researchers from the Technion and Tel Aviv University present a new and original solution to this problem, based on computational learning tools, in an article recently published inOptica, the flagship magazine of the Optical Society. The idea for this solution came up in a social gathering of researchers from different research groups: the group of Professor Alex Bronstein from the Taub Faculty of Computer Science at the Technion, an expert in computational learning and information analysis, and the group of Professor Adi Arie (head of the Marco and Lucy Shaul Chair) from Tel Aviv University , who specializes in theoretical and applied research in the field of non-linear optics and in particular in quantum optics. In research partners, previously a master's student under the guidance of Prof. Aryeh and currently a doctoral student in Prof. Bronstein's research group, and the members of Prof. Aryeh's group - Aviv Karneali, Ofir Isharim, Dr. Sivan Trachtenberg Mills (currently a postdoctoral fellow at MIT) and G. and that Polly-comer. The project was joined by an external expert from the company "Google" - Dr. Daniel Friedman, an experienced researcher in the field of computational learning, who has already worked with Rosenberg and Prof. Bronstein on other projects. Dr. Friedman is now the head of the research group at Verily - Google Life Science.
In the first step, the researchers developed a numerical model that makes it possible to accurately predict the statistical index that evaluates the correlations between the two photons generated in the SPDC for a given optical system, that is, for the characteristics of the laser beam and the nonlinear crystal. This model was validated by comparing it to a series of previously published experimental results.
In the second step, the crystal and laser data were used as parameters on which learning can be performed and a price function was defined - a function that represents the distance between the result obtained by running the numerical model and the result the planner wishes to reach. When the learning process was activated, it produced the properties of the non-linear crystal and the shape of the laser beam that would produce a result as close as possible to the desired quantum state.
The method published in the article focused on entanglement expressed in the spatial form of the photons (circle vs. ring), but it can also be applied to entanglement related to the color of the photon or its polarization. The researchers add that apart from the application in non-linear crystals, the method can be applied in other systems such as optical fibers and non-linear waveguides. According to Rosenberg, "Our work, along with its complementary code (https://github.com/EyalRozenberg1/SPDCinv), can contribute to further exciting progress and discoveries in other quantum and classical systems. We decided to publish the entire algorithm as open source, to allow more research groups around the world to use the tools we developed."
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