There’s no single pathway to developing fully functional quantum computers that can correct their own errors. Several organizations are researching different quantum technologies, each at different stages of development.
Major companies such as IBM, Intel, and Google are investing in superconducting qubits alongside smaller companies like Atlantic Quantum, IQM, Anyon Systems, Rigetti Computing, and Bleximo. Given the number of companies focused on this type of quantum bit, it’s reasonable to conclude that this kind of technology receives the most support and investment, making it the leading technology in the field.
While this strategy may enable the development of more qubits, it’s also more prone to errors compared to ion trap qubits, which are an alternative to superconducting qubits. Additionally, superconducting qubits operate at extremely low temperatures (around 20 millikelvin, or approximately -460 degrees Fahrenheit) to ensure optimal isolation from the environment.
MIT’s Strategy Proposes Promising Alternative Qubits
Many research groups around the world are exploring various qubit technologies in the quest to develop fully functional quantum computers. While it’s possible that one or more of these technologies will succeed, there’s also a chance that none will prove viable. It’s plausible that the technology capable of realizing practical qubits has yet to be invented and may emerge in the future. In fact, this scenario is highlighted by a recent discovery from a team of physicists at the Massachusetts Institute of Technology.
In a paper published in Physical Review Letters, MIT researchers discuss their recent findings, which build on a discovery from 2023 concerning a type of material capable of hosting electrons that can fractionate their electric charge. This results in the formation of fractional quasiparticles without the need for a magnetic field. Normally, electrons, as fundamental particles, can’t fractionate their charge. As such, this phenomenon illustrates the collective behavior of the system involved.
Although this concept may sound exotic, it’s not entirely new. The ability of electrons to break down their electric charge into smaller fractions was first observed in 1982, which led to physicists Robert Laughlin, Horst Störmer, and Daniel Tsui being awarded the Nobel Prize in 1998 for their contributions.
The discovery of an electron’s ability to split its charge occurred in 1982, leading to physicists Robert Laughlin, Horst Störmer, and Daniel Tsui being awarded the Nobel Prize in Physics in 1998.
Laughlin, Störmer, and Tsui had to use an extremely strong magnetic field and very low temperatures. However, MIT researchers have built upon a 2023 experiment that allows for predicting the existence of non-abelian anyons without the need for a magnetic field. Anyons are a type of quasiparticle resulting from splitting an electron’s charge. Importantly, while the anyons identified in the 2023 experiment were abelian, MIT researchers have predicted the existence of non-abelian anyons, which are considered extremely exotic.
Liang Fu, a professor in MIT’s Department of Physics and the leader of this research, explains why these particles are so promising: “Non-abelian anyons have the bewildering capacity of ‘remembering’ their spacetime trajectories; this memory effect can be useful for quantum computing.” She adds, “The 2023 experiments on electron fractionalization greatly exceeded theoretical expectations. My takeaway is that we theorists should be bolder.”
Researcher Ryan Wilkinson elaborates on the potential impact of qubits made from non-abelian anyons in the future: “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.”
This is an exciting development, and we can only hope that the predictions made by MIT physicists will be confirmed through experimentation.
Image | IBM Research
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