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Turn the graphene at 1.1 degrees - it will become a superconductor.
Quantum materials sometimes behave as if physics have hidden settings. It is worth folding a few atomically thin layers of graphene and slightly rotate them relative to each other, and the material has properties that there were no individual sheets. The most famous example is the “magic angle” in graphene: the moire pattern helps to start superconductivity, in which the current goes without resistance.
The more complex the material is arranged, the more difficult it is to understand in advance how he will behave in experience. Physicists already collect not only simple moire structures, but also quasicrystals with supermoire materials. In a conventional crystal, the atoms are repeated according to an understandable pattern, so the calculations can be simplified. In quasicrystal, order remains, but there is no habitual repetitive cell. Because of this, the model quickly grows to a huge volume: for some calculations, more quadrillion numbers are needed, and even powerful supercomputers do not pull this array.
Physicists from the University of Aalto suggested a way to consider such materials without head-on too much. The team of the Department of Applied Physics has developed a quantum-in-inspired algorithm. It does not require a ready-made quantum computer, but takes the main trick from quantum computing: it describes a huge task as a quantum system of many related particles.
At the center of the work are topological quasicrystals. In such materials, not only the composition and shape of the lattice are important, but also the device of electronic states. These properties help current suffer less from noise, defects, and other interference. For future quantum devices, this resistance is especially important because quantum states are easily lost due to external influences.
But it is extremely difficult to calculate the topological quasicrystal by conventional methods. The material does not have a simple periodicity, and the necessary quantum excitations are distributed unevenly in structure. As a result, the direct calculation turns into an attempt to describe a huge pattern, where you can not just take one fragment and repeat it many times.
To simplify the task, the researchers used tensor networks. This mathematical instrument compresses the description of huge state spaces and does not require you to sort out all options directly. Quantum computers work with similar spaces, so the logic of tensor networks is well suited for modeling complex quantum materials.
The algorithm was able to calculate quasi-crystal with more than 268 million knots. For materials science, this is a very large scale: classical methods quickly rest in memory, calculation time and number of parameters. In the new scheme, the task does not turn into a giant number table. Instead, the material is described more compactly, through the structure of the connections within the quantum system.
As long as the work remains theoretical and is based on simulations. But the meaning of the result is not in the record for the sake of the record. The algorithm gives a way to design supermoire quasicrystals several orders of magnitude larger than the usual methods allow. Such a tool can be useful in the development of topological qubits based on supermoired materials.
Topological quotes are seen as one of the paths to more stable quantum computers. Conventional qubits are sensitive to noise, heating, and microscopic defects. If the material better protects quantum information due to topological properties, it is easier for engineers to build computing systems that retain the state for longer.
New quantum materials can be useful in electronics without energy loss. Data centers are getting heated more and more because of AI and consume more electricity. Components that conduct current without further heating could reduce the load on such infrastructure.
In the future, the algorithm can be transferred to real quantum computers, when processors become large and accurate. In Finland, for such experiments, the quantum processor AaltoQ20 and the national infrastructure of the Finnish Quantum Computing Infrastructure are considered. The hardware base is not yet ready to fully take on such calculations, but the work already shows where quantum algorithms can give practical benefits to one of the first.
Quantum materials sometimes behave as if physics have hidden settings. It is worth folding a few atomically thin layers of graphene and slightly rotate them relative to each other, and the material has properties that there were no individual sheets. The most famous example is the “magic angle” in graphene: the moire pattern helps to start superconductivity, in which the current goes without resistance.
The more complex the material is arranged, the more difficult it is to understand in advance how he will behave in experience. Physicists already collect not only simple moire structures, but also quasicrystals with supermoire materials. In a conventional crystal, the atoms are repeated according to an understandable pattern, so the calculations can be simplified. In quasicrystal, order remains, but there is no habitual repetitive cell. Because of this, the model quickly grows to a huge volume: for some calculations, more quadrillion numbers are needed, and even powerful supercomputers do not pull this array.
Physicists from the University of Aalto suggested a way to consider such materials without head-on too much. The team of the Department of Applied Physics has developed a quantum-in-inspired algorithm. It does not require a ready-made quantum computer, but takes the main trick from quantum computing: it describes a huge task as a quantum system of many related particles.
At the center of the work are topological quasicrystals. In such materials, not only the composition and shape of the lattice are important, but also the device of electronic states. These properties help current suffer less from noise, defects, and other interference. For future quantum devices, this resistance is especially important because quantum states are easily lost due to external influences.
But it is extremely difficult to calculate the topological quasicrystal by conventional methods. The material does not have a simple periodicity, and the necessary quantum excitations are distributed unevenly in structure. As a result, the direct calculation turns into an attempt to describe a huge pattern, where you can not just take one fragment and repeat it many times.
To simplify the task, the researchers used tensor networks. This mathematical instrument compresses the description of huge state spaces and does not require you to sort out all options directly. Quantum computers work with similar spaces, so the logic of tensor networks is well suited for modeling complex quantum materials.
The algorithm was able to calculate quasi-crystal with more than 268 million knots. For materials science, this is a very large scale: classical methods quickly rest in memory, calculation time and number of parameters. In the new scheme, the task does not turn into a giant number table. Instead, the material is described more compactly, through the structure of the connections within the quantum system.
As long as the work remains theoretical and is based on simulations. But the meaning of the result is not in the record for the sake of the record. The algorithm gives a way to design supermoire quasicrystals several orders of magnitude larger than the usual methods allow. Such a tool can be useful in the development of topological qubits based on supermoired materials.
Topological quotes are seen as one of the paths to more stable quantum computers. Conventional qubits are sensitive to noise, heating, and microscopic defects. If the material better protects quantum information due to topological properties, it is easier for engineers to build computing systems that retain the state for longer.
New quantum materials can be useful in electronics without energy loss. Data centers are getting heated more and more because of AI and consume more electricity. Components that conduct current without further heating could reduce the load on such infrastructure.
In the future, the algorithm can be transferred to real quantum computers, when processors become large and accurate. In Finland, for such experiments, the quantum processor AaltoQ20 and the national infrastructure of the Finnish Quantum Computing Infrastructure are considered. The hardware base is not yet ready to fully take on such calculations, but the work already shows where quantum algorithms can give practical benefits to one of the first.
