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Scientists take important step toward mitigating errors in analog quantum simulations of many-body problems



Simulations of quantum many-body systems are an important goal for nuclear and high-energy physics. Many-body problems involve systems that consist of many microscopic particles interacting at the level of quantum mechanics. They are much more difficult to describe than simple systems with just two particles. This means that even the most powerful conventional computers cannot simulate these problems.

Quantum computing has the potential to address this challenge using an approach called analog quantum simulation. To succeed, these simulations need theoretical approximations of how quantum computers represent many-body systems. In research on this topic, nuclear physicists at the University of Washington developed a new framework to systematically analyze the interplay of these approximations. They showed that the impact of such approximations can be minimized by tuning simulation parameters.

This method provides a new tool for quantifying the uncertainties in analog quantum simulations of dynamical processes. Quantum computers are becoming more and more reliable and resilient to noise. However, to make reliable predictions, scientists need to understand and quantify sources of error and their effects on analog quantum simulations.

Researchers can use the techniques developed in this work to improve the precision of future simulations. Such optimizations are demonstrated in the context of spin models sharing key features with nuclear interactions.

In an analog quantum simulation, a highly controllable quantum system replicates the behavior of a more exotic system. A leading architecture for such simulations is Rydberg-atom quantum computers, which are scalable arrays of Rydberg atoms that support a universal quantum gate set. Scientists expect that with rapidly improving control, analog quantum computers will enable near-term advantages in uncovering new physics.

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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