Communications of the ACM
Running Quantum Software on a Classical Computer
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.
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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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