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New type of quantum computer studies the dance of elementary particles




The standard model of particle physics is our best theory of the elementary particles and forces that make up our world: particles and antiparticles, such as electrons and positrons, are described as quantum fields. They interact through other force-fields, such as the electromagnetic force that binds charged particles.

To understand the behaviour of these quantum fields and with that our universe, researchers perform complex computer simulations of quantum field theories. Unfortunately, many of these calculations are too complicated for even our best supercomputers and pose great challenges for quantum computers as well, leaving many pressing questions unanswered.

Using a novel type of quantum computer, Martin Ringbauer’s experimental team at the University of Innsbruck, and the theory group led by Christine Muschik at IQC at the University of Waterloo, Canada report in a publication in the journal Nature Physics how they have successfully simulated a complete quantum field theory in more than one spatial dimension.

A natural representation of quantum fields

The crux that makes simulations of quantum field theories challenging for quantum computers comes from the need to capture the fields that represent the forces between particles, such as the electromagnetic force between charged particles. These fields can point in different directions and have different degrees of strength or excitations. Such objects do not neatly fit into the traditional binary computing paradigm based on zeros and ones, which is the basis of today's classical and quantum computers.

The new advance was possible through the combination of a qudit quantum computer developed in Innsbruck, and a qudit algorithm to simulate fundamental particle interactions developed in Waterloo. This approach is based on using up to five values per quantum information carrier, rather than just zero and one, to efficiently store and process information. Such a quantum computer is ideally suited to the challenge of representing complex quantum fields in particle physics calculations. “Our approach enables a natural representation of the quantum fields, which makes the computations much more efficient,” explains Michael Meth, lead author of the study. This enabled the team to observe the fundamental features of quantum electrodynamics in two spatial dimensions.

Huge potential for particle physics

Already in 2016, the creation of particle-antiparticle pairs was demonstrated in Innsbruck. “In that demonstration, we simplified the problem by restricting the particles to move on a line. Removing this restriction is a critical step to use quantum computers to understand fundamental particle interactions,” says Christine Muschik. Now the teams have presented the first quantum simulation in two spatial dimensions, “In addition to the behaviour of particles, we now also see magnetic fields between them, which can only exist if particles are not restricted to move on a line and bring us an important step closer to studying nature,” explains Martin Ringbauer.

The new work on quantum electrodynamics is just the beginning. With only a few qudits more it will be possible to extend the current results not only to three-dimensional models, but also to the strong nuclear force, which holds atoms together and contains many of physics’ remaining mysteries. “We are excited about the potential of quantum computers to contribute to the study of these fascinating questions,” says Ringbauer enthusiastically.

The research was financially supported among others by the Austrian Science Fund (FWF), the Austrian Federal Ministry of Education, Science and Research, the Austrian Research Promotion Agency (FFG), the European Union, and the Canada First Research Excellence Fund.

Website: International Research Awards 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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