Simulating quantum computing with classical machine learning
© 2021 Giuseppe Carleo and Matija Medvidović
A recent CQSL work published in NPJ Quantum Information shows how machine learning techniques can be used to simulate the inner workings of near-term quantum computers.
In a new article published in NPJ Quantum Information, Giuseppe Carleo (EPFL, CQSL) and Matija Medvidović (Columbia University and Flatiron Institute, New York City) show how an interesting class of quantum algorithms, known as the QAOA, can be simulated using neural-network quantum states.
The article is available here, free of charge.
References
Medvidović, M., Carleo, G. Classical variational simulation of the Quantum Approximate Optimization Algorithm. npj Quantum Inf 7, 101 (2021).