Measurement methods using quasiparticles

Abstract

Method for determining a distribution of a parameter over a sample (1), comprising: a) generating a plurality of quasiparticles (3) in the sample (1), wherein the quasiparticles (3) are mobile depending on the parameter in the sample (1), b) after a waiting period, determining a distribution of the quasiparticles (3) generated in step a) over the sample (1), c) determining the distribution of the parameter over the sample (1) from the distribution of the quasiparticles (3) determined in step b), by considering the density of quasiparticles (3) as a measure of the parameter.

Description

The invention relates to a method for determining a distribution of a parameter over a sample comprising quasiparticles. More particularly, the invention relates to a method for determining a distribution of temperature over a surface of a sample with skyrmions.Numerous methods are known for measuring parameters of samples. Depending on the parameter to be measured, another method may be used. Methods are also known for measuring the distribution of a parameter over the surface of a sample. However, this can be expensive depending on the parameter to be measured.US 2022 / 0 044 142 A1 discloses a quantum computing unit which is operated by means of Majorana modes. US 2022 / 0 261 678 A1 discloses a method for topological quantum computing. However, the two documents differ substantially from the invention described herein.It is an object of the present invention to provide a simple method of determining the distribution of a parameter over a sample, starting from the described prior art.This object is achieved with a method according to the independent claim. Further advantageous embodiments are specified in the dependent claims. The features shown in the claims and in the description can be combined with one another in any desired, technologically meaningful manner.According to the invention, a method for determining a distribution of a parameter over a sample is presented. The method comprises: a) generating a plurality of quasiparticles in the sample, wherein the quasiparticles are movable in dependence on the parameter in the sample, b) after a waiting time determining a distribution of the quasiparticles generated in step a) over the sample, c) determining the distribution of the parameter over the sample from the distribution of the quasiparticles determined in step b) by considering the density of quasiparticles as a measure of the parameter.With the method described, the distribution of a parameter over a sample can be determined using quasiparticles. This makes use of the fact that the quasiparticles are movable in the sample as a function of the parameter. The quasiparticles thus interact with their environment. If quasiparticles are generated in the sample, a distribution of the quasiparticles in the sample is gradually established which is a measure of the sought-after distribution of the parameter. The method described thus far represents a measuring method.Compared to other measuring methods, the method described is particularly simple. This applies in particular in comparison with methods in which the parameter is measured locally and a distribution is determined from the measurement results. With the described method, the distribution can be determined without having to determine the parameter locally beforehand.The method can be used to determine the distribution of the parameter over the sample. This is preferably done two-dimensionally. In this case, the method determines the distribution of a parameter over the surface of the sample. The method comprises, in the case: a) generating a plurality of quasiparticles on the surface of the sample, wherein the quasiparticles are movable depending on the parameter on the surface of the sample, b) after a waiting time, determining a distribution of the quasiparticles generated in step a) over the surface of the sample, c) determining the distribution of the parameter over the surface of the sample from the distribution of the quasiparticles determined in step b) by considering the density of quasiparticles as a measure of the parameter.Such a two-dimensional determination of the distribution of the parameter can be carried out with two-dimensional quasiparticles. With other quasiparticles, however, it is also possible to determine the distribution of the parameter in the volume of the sample.The term "quasiparticles" is a fixed technical term which is based on Lew Dawidowitsch Landau. A quasiparticle is understood to mean an elemental excitation of a multiparticle system. Quasiparticles may have properties like a particle. Unlike particles, however, quasiparticles cannot freely exist, but only as part of a multiparticulate system. The term "quasiparticles" also includes a so-called "soliton" in the meaning of this term used herein.There are several quasiparticles. The process described can be carried out with all quasiparticles as long as the requirements described herein are fulfilled.The method can be used to determine the distribution of a parameter over the sample.The sample is a solid. The sample can be any physical object in which quasiparticles can move. It is sufficient that there is a type of quasiparticles for which this condition is satisfied. For example, the sample may be formed of a metal or a semiconductor material. For example, the sample may be a plate having dimensions in the range of a few millimeters or centimeters.The parameter can be any physical variable which has an influence on the movement of quasiparticles in the sample. It is sufficient that there is a type of quasiparticles for which this condition is satisfied. Whether the method described is applicable to a specific parameter thus depends on whether there is a quasiparticle whose movement is influenced by this parameter.For example, the quasiparticles can be skyrmions and the parameter can be the temperature or the defect density of the sample. Skyrmions are quasiparticles named according to Tony Skyrmé. Carbonyl ions have particle-like properties. Skyrmions can be realized as vortices in magnetic materials. Skyrmions can be generated, destroyed and manipulated. Various methods are known for this purpose. Skyrmions move due to fluctuations. These fluctuations can be quantum-mechanical and / or thermal in nature. Each system is subject to thermal fluctuations as long as it is not cooled down to the absolute temperature zero--which is impossible. The movement of skyrmions can be influenced, for example, by temperature gradients, external fields, electric currents and / or defects and inhomogeneities. Thus, skyrmions can move in particular depending on the temperature and the defect density at the surface of a sample.In step a) of the method, a plurality of quasiparticles are generated in the sample. This can also be referred to as initialization. The quasiparticles can be generated at any desired points in the sample.Since the quasiparticles are movable in the sample depending on the parameter, the quasiparticles do not remain distributed over the sample in their initial distribution. Instead, the quasiparticles move in fundamentally arbitrary paths in the sample. This movement can be, in particular, a Brownian movement. However, the movement of the quasiparticles is not completely arbitrary due to the dependence on the parameter. Rather, the quasiparticles gradually collect in the region of extremes of the parameter. Whether the quasiparticles collect in the region of a maximum or minimum depends on the type of the quasiparticles and on the parameter. Magnetic skyrm ions accumulate, for example, in the region of a minimum of the temperature.In step a), any number of quasiparticles may be generated. The costs for generating quasiparticles are comparatively low, in particular compared to the use of additional computer hardware. Preferably, in step a) at least 10 quasiparticles are produced in the sample, particularly preferably at least 100. Preferably, in step a) between 10 and 1,000 quasiparticles are produced in the sample.In step b), after a waiting time, a distribution of the quasiparticles generated in step a) over the sample is determined. Within the waiting time, the quasiparticles can move from their initial distribution into a distribution which can be understood as a measure for the distribution of the parameter. The length of time the waiting time is selected is unimportant for the basic principle of the method. In general, it is to be assumed that the greater the waiting time, the more accurate the result obtained. The optimum length of the waiting time thus results from a compromise between the accuracy of the result and the method duration. The waiting time is preferably at least 5 seconds, in particular at least 10 seconds. With shorter waiting times, the quality of the results is insufficient for most applications. The waiting time is preferably a maximum of 10 minutes, in particular a maximum of 5 minutes. In the case of longer waiting times, a significant improvement in the quality of the result is no longer to be expected in many applications.The distribution of the quasiparticles can be determined in different ways in step b). Any method can be used with which the position and / or the density of the quasiparticles can be determined for the type of quasiparticles used. Depending on the type of quasiparticles used, therefore, different methods are suitable for step b). Many quasiparticles such as, for example, skyrmions can be optically detected. Accordingly, it is preferred that in step b) after the waiting time the distribution of the quasiparticles generated in step a) over the sample is optically determined. This implies that quasiparticles are used whose position can be optically detected. In turn, various imaging methods can be used for optically determining the position of the quasiparticles. For example, the distribution of skyrmions over the sample can be determined with an electron microscope. For the functioning of the described method, however, it is immaterial in what way knowledge of the distribution of the quasiparticles over the sample can be obtained.In step c), the distribution of the parameter over the sample is determined from the distribution of the quasiparticles determined in step b) by considering the density of quasiparticles as a measure of the parameter.Depending on the type of quasiparticles used and depending on the parameter considered, it can be assumed that the density of the quasiparticles is proportional or anti-proportional to the parameter. Magnetic carbonyl ions, for example, tend to move to lower temperatures. A high density of carbonyl ions can be interpreted accordingly as a low temperature.In a preferred embodiment of the method, the parameter is a temperature of the sample.In particular in this embodiment, the quasiparticles are preferably skyrmions. Alternatively, however, the method can also be carried out with any other type of quasiparticles, as long as the movement of the quasiparticles depends on the temperature. This is the case with many quasiparticles.In a further preferred embodiment of the method, the parameter is a defect density of the sample.In particular in this embodiment, the quasiparticles are preferably skyrmions. Alternatively, however, the method can also be carried out with any other type of quasiparticles, as long as the movement of the quasiparticles depends on the defect density. This is the case with many quasiparticles.In a further preferred embodiment of the method, the quasiparticles interact with one another.Already by the movement of the quasiparticles in the sample depending on the parameter, the distribution of the parameter over the sample can be made visible with the quasiparticles. This process is accelerated if not only each quasiparticle itself tends to approach a maximum or minimum of the parameter. If the quasiparticles interact with one another, the quasiparticles can collect more quickly in the region of a maximum or minimum of the parameter.A similar effect is known from the behaviour of bees. Bees also interact with each other and can thus collect at a location, for example at a certain temperature, more quickly than if each bee were to search for this location on its own. In robotics, so-called Beeclust algorithms have been developed on the basis of this effect. The Beeclust algorithm is based on the animal world and falls within the scope of swarm intelligence. Individual robots interact with one another and collect as quickly at a specific location as would be the case without the interaction. All robots of a Beeclust algorithm move randomly. When two robots meet, both measure a parameter of the environment. Based on this measurement they decide how long they are resting at this location. When the rest time has elapsed, they rotate (randomly) and continue to move. The robots interact with one another in this case to the extent that the measurement of the parameter and the subsequent resting take place whenever two robots meet. Since two robots are more likely to meet the greater the density of robots, the robots gradually gather where the parameter results in a long rest time. In this way, for example, a maximum of the parameter can be found. In the case of the Beclust algorithm, the local communication of a plurality of robots, which communication is knitted according to simple rules, thus makes it possible to solve an optimization problem more efficiently than would be possible with only one robot.The present embodiment is based on the finding that the described effect known from the behavior of bees can be applied not only to robotics, but can also be used in connection with quasiparticles. Each of the quasiparticles has the function of an individual bee or an individual robot.In the method described, passive quasiparticles are used in the material of the sample instead of active, energy-consuming robots. The quasiparticles can interact with one another by interactions.In various quasiparticles, there is interaction between the quasiparticles. For example, this is the case with skyrmions.The interaction may be repulsive or attractive. A combination of these two types of interaction is also possible. Preferably, the interaction between two of the quasiparticles is repulsive when the distance between these two quasiparticles is smaller than a threshold value and attractive when the distance between these two quasiparticles is larger than the threshold value. For example, the quasiparticles can interact with one another via a potential in the manner of a Lennard-Jones potential. Such a type of interaction exists, for example, in the case of skyrmions.The manner in which the quasiparticles interact with one another may also depend on the type of sample. One and the same type of quasiparticles can thus have a different interaction depending on the sample.In a further preferred embodiment of the method, the quasiparticles are skyrmions.In particular in this embodiment, it is possible to use the room temperature or also the emitted temperature of other components, such as computer components, for example, as energy source for the movement of the quasiparticles.In a further preferred embodiment of the method, in step c) a maximum and / or a minimum of the distribution of the parameter over the sample are determined.In the present embodiment, the determined distribution of the parameter over the sample is used to derive further quantities therefrom. This can be done using known mathematical methods. The maximum may be a local maximum or a global maximum. The minimum may be a local minimum or a global minimum.In a further preferred embodiment of the method, the distribution of the parameter over the sample represents a solution to a mathematical problem.The method can be used to read out the result of a computing method carried out by means of quasiparticles. In this case, the distribution of the parameter over the sample can represent a solution to a mathematical problem.The method is based on the movement of the quasiparticles in the sample. The result is obtained by a plurality of quasiparticles, which together can also be referred to as a swarm. The method can therefore also be used within the scope of "in-matter-swarm computing". The method can be used to read out the result of a calculation.The invention is explained in more detail below with reference to the figures. The figures show particularly preferred exemplary embodiments, to which the invention is, however, not limited. The figures and the size relationships illustrated therein are only schematic. The following are shown: FIGS. 1 ato 1 d : show an illustration of a first embodiment of a method according to the invention for determining a distribution of a parameter over a surface of a sample, and FIGS. 2 ato 2 d : show an illustration of a second embodiment of a method according to the invention for determining a distribution of a parameter over a surface of a sample.FIG. 1 ashows a surface 2 of a sample 1. sample 1 has an unknown temperature distribution. With a method according to the invention, the distribution of the temperature over the surface 2 can be determined. The surface 2 of the sample 1 has recesses 4. The recesses 4 can be regions at which the material of the sample 1 has been removed.In FIG. 1 b, a step a) of the method is visualized. In this case, a multiplicity of quasiparticles 3 are produced on the surface 2 of the sample 1. This can be done at arbitrary locations outside the recesses 4 of the surface 2 of the sample 1. The quasiparticles 3 are movable depending on the temperature at the surface 2 of the sample 1. In addition, the quasiparticles 3 interact with one another. For example, the quasiparticles 3 may be skyrmions.In a step b), after a waiting time, a distribution of the quasiparticles 3 generated in step a) over the surface 2 of the sample 1 is detected. This is visualized in FIG. 1 c.In a step c), the desired distribution of the temperature over the surface 2 of the sample 1 is determined from the distribution of the quasiparticles 3 determined in step b) by considering the density of quasiparticles 3 as a measure of the temperature.In FIG. 1 d, the actual temperature distribution is shown. It can be seen from a comparison of FIGS. 1 cand 1 d that the quasiparticles 3 have accumulated in the colder region of the surface 2 of the sample 1.For the sake of clarity, only a few quasiparticles 3 have been shown in FIGS. 1 ato 1 c. If the method were carried out with a larger number of quasiparticles 3, the temperature distribution could be determined with correspondingly better accuracy.From the obtained distribution of the temperature over the surface 2 of the sample 1, for example, a maximum 6 and a minimum 5 can be determined. In the example shown, the minimum 5 is at the top left and the maximum 6 is at the bottom right.The method described can be used to read out the result of a computing method carried out by means of quasiparticles 3. In this case, the distribution of the temperature over the surface 2 of the sample 1 can represent a solution to a mathematical problem.FIG. 2 ashows a surface 2 of a sample 1. sample 1 has an unknown temperature distribution. With a method according to the invention, the distribution of the temperature over the surface 2 can be determined. In addition, the sample 1 has defects 7. the surface 2 of the sample 1 has recesses 4. This can be done as in FIGS. 1a to 1d. Accordingly, FIG. 2 b shows how a plurality of quasiparticles 3 are produced on the surface 2 of the sample 1. FIG. 2 cshows how these quasiparticles 3 collect at the left edge of the sample. It can also be seen from this that the quasiparticles 3 have mainly arranged inside the defects 7. Due to this circumstance, the method could also be used to determine the defect density of the sample 2. In FIG. 2 d, the actual temperature distribution over the surface 2 of the sample 1 is shown. It can be seen that the quasiparticles 3 have collected in the region of the lowest temperature, i.e. in the region of the minimum 5 and far from the maximum 6, as in the example of FIGS. 1 ato 1 d.List of reference characters1 Sample 2 Surface 3 Quasiparticles 4 Cutout 5 Minimum 6 Maximum 7 Defect

Claims

Method for determining a distribution of a parameter over a sample (1), comprising a) generating a plurality of quasiparticles (3) in the sample (1), wherein the quasiparticles (3) are movable in the sample (1) depending on the parameter, b) after a waiting time determining a distribution of the quasiparticles (3) generated in step a) over the sample (1), c) determining the distribution of the parameter over the sample (1) from the distribution of the quasiparticles (3) determined in step b) by considering the density of quasiparticles (3) as a measure of the parameter.The method of claim 1, wherein the parameter is a temperature of the sample (1).The method according to claim 1, wherein the parameter is a defect density of the sample (1).Method according to any of the preceding claims, wherein the quasi-particles (3) interact with each other.Method according to any of the preceding claims, wherein the quasiparticles (3) are skyrmions.Method according to any one of the preceding claims, wherein in step c) a maximum and / or a minimum of the distribution of the parameter over the sample (1) are determined.Method according to any of the preceding claims, wherein the distribution of the parameter over the sample (1) represents a solution to a mathematical problem.

Images