MIT physicists propose a method to create fractionalized electrons known as non-Abelian anyons in two-dimensional materials, potentially advancing quantum computing by enabling more reliable quantum bits without using magnetic fields.
MIT physicists have shown that it should be possible to create an exotic form of matter that could serve as the building blocks for future quantum computers. These quantum bits, or qubits, could make quantum computers even more powerful than those in development today.
Their research builds on a recent discovery of materials where electrons can split into fractional parts a phenomenon known as electron fractionalization. Crucially, this splitting happens without the need for a magnetic field, making the process more practical for real-world applications.
Electron fractionalization was first discovered in 1982, earning a Nobel Prize, but the original process required applying a magnetic field. The ability to create fractionalized electrons without this requirement opens the door to new research possibilities and practical technological uses.
When electrons split into fractions of themselves, those fractions are known as anyons. Anyons come in variety of flavors, or classes. The anyons discovered in the 2023 materials are known as Abelian anyons. Now, in a paper published recently in the journal Physical Review Letters, the MIT team notes that it should be possible to create the most exotic class of anyons, non-Abelian anyons.
“Non-Abelian anyons have the bewildering capacity of ‘remembering’ their spacetime trajectories; this memory effect can be useful for quantum computing,” says Liang Fu, a professor in MIT’s Department of Physics and leader of the work.
Fu further notes that “the 2023 experiments on electron fractionalization greatly exceeded theoretical expectations. My takeaway is that we theorists should be bolder.”
Fu is also affiliated with the MIT Materials Research Laboratory. His colleagues on the current work are graduate students Aidan P. Reddy and Nisarga Paul, and postdoc Ahmed Abouelkomsan, all of the MIT Department of Phsyics. Reddy and Paul are co-first authors of the Physical Review Letters paper.
The MIT work and two related studies were also featured in an recent story in Physics Magazine. “If this prediction is confirmed experimentally, it could lead to more reliable quantum computers that can execute a wider range of tasks … Theorists have already devised ways to harness non-Abelian states as workable qubits and manipulate the excitations of these states to enable robust quantum computation,” writes Ryan Wilkinson.
The current work was guided by recent advances in 2D materials, or those consisting of only one or a few layers of atoms. “The whole world of two-dimensional materials is very interesting because you can stack them and twist them, and sort of play Legos with them to get all sorts of cool sandwich structures with unusual properties,” says Paul. Those sandwich structures, in turn, are called moiré materials.
Website: International Conference on High Energy Physics and Computational Science.
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MIT physicists have shown that it should be possible to create an exotic form of matter that could serve as the building blocks for future quantum computers. These quantum bits, or qubits, could make quantum computers even more powerful than those in development today.
Their research builds on a recent discovery of materials where electrons can split into fractional parts a phenomenon known as electron fractionalization. Crucially, this splitting happens without the need for a magnetic field, making the process more practical for real-world applications.
Electron fractionalization was first discovered in 1982, earning a Nobel Prize, but the original process required applying a magnetic field. The ability to create fractionalized electrons without this requirement opens the door to new research possibilities and practical technological uses.
When electrons split into fractions of themselves, those fractions are known as anyons. Anyons come in variety of flavors, or classes. The anyons discovered in the 2023 materials are known as Abelian anyons. Now, in a paper published recently in the journal Physical Review Letters, the MIT team notes that it should be possible to create the most exotic class of anyons, non-Abelian anyons.
“Non-Abelian anyons have the bewildering capacity of ‘remembering’ their spacetime trajectories; this memory effect can be useful for quantum computing,” says Liang Fu, a professor in MIT’s Department of Physics and leader of the work.
Fu further notes that “the 2023 experiments on electron fractionalization greatly exceeded theoretical expectations. My takeaway is that we theorists should be bolder.”
Fu is also affiliated with the MIT Materials Research Laboratory. His colleagues on the current work are graduate students Aidan P. Reddy and Nisarga Paul, and postdoc Ahmed Abouelkomsan, all of the MIT Department of Phsyics. Reddy and Paul are co-first authors of the Physical Review Letters paper.
The MIT work and two related studies were also featured in an recent story in Physics Magazine. “If this prediction is confirmed experimentally, it could lead to more reliable quantum computers that can execute a wider range of tasks … Theorists have already devised ways to harness non-Abelian states as workable qubits and manipulate the excitations of these states to enable robust quantum computation,” writes Ryan Wilkinson.
The current work was guided by recent advances in 2D materials, or those consisting of only one or a few layers of atoms. “The whole world of two-dimensional materials is very interesting because you can stack them and twist them, and sort of play Legos with them to get all sorts of cool sandwich structures with unusual properties,” says Paul. Those sandwich structures, in turn, are called moiré materials.
Website: International Conference on High Energy Physics and Computational Science.
#HighEnergyPhysics#ParticlePhysics#QuantumPhysics#AstroparticlePhysics#ColliderPhysics#HiggsBoson#LHC#QuantumFieldTheory#NeutrinoPhysics#PhysicsResearch#ComputationalScience#DataScience#ScientificComputing#NumericalMethods#HighPerformanceComputing#MachineLearningInScience#BigData#AlgorithmDevelopment#SimulationScience#ParallelComputing
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