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Running Quantum Software on a Classical Computer


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Artist's impression of quantum computing.

A new approach to simulating the quantum approximate optimization algorithm using a traditional computer uses a classical machine-learning algorithm that closely mimics the behavior of near-term quantum computers.

Credit: iStock

Researchers at the Swiss Federal Institute of Technology Lausanne (EPFL) in Switzerland and Columbia University have developed a method for using a traditional computer to simulate the Quantum Approximate Optimization Algorithm (QAOA).

The approach employs a classical machine learning algorithm that acts like near-term quantum computers.

The researchers used an artificial neural network previously co-developed by EPFL's Giuseppe Carleo to simulate QAOA, which is considered a promising candidate for "quantum advantage" in near-term quantum computers.

Said Carleo, "This does not mean that all useful quantum algorithms that can be run on near-term quantum processors can be emulated classically. In fact, we hope that our approach will serve as a guide to devise new quantum algorithms that are both useful and hard to simulate for classical computers."

From EPFL (Switzerland)
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


 

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