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Running quantum software on a classical computer

— Two physicists, from EPFL and Columbia University, have introduced an approach for simulating the quantum approximate optimization algorithm using a traditional computer. Instead of running the algorithm on advanced quantum processors, the new approach uses a classical machine-learning algorithm that closely mimics the behavior of near-term quantum computers.

© 2021 American Physical Society

Neural-Network Quantum States for Nuclear Matter

— The first application of neural-network quantum states to nuclear matter has been published in Physical Review Letters, in a collaboration between CQSL and Argonne National Laboratory, in Chicago. 

© 2021 Giuseppe Carleo and Matija Medvidović

Simulating quantum computing with classical machine learning

— 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.

© 2021 EPFL

Quantum computing: cold chips can control qubits

— A cryogenic controller chip opens the door to solving the ‘wiring bottleneck’ and subsequently to realize a fully integrated, scalable quantum computer. A research from QuTech in the Netherlands, from Intel Corp and from EPFL professor Edoardo Charbon.

A cryogenic dilution refrigerator. The base temperature is 10 milliKelvin. Credit: Andrea Bancora, Amir Youssefi

Light meets superconducting circuits

— EPFL researchers have developed a light-based approach to read out superconducting circuits, overcoming the scaling-up limitations of quantum computing systems.

Integrated silicon nitride photonic chips with meter-long spiral waveguides. Credit: Jijun He and Junqiu Liu (EPFL).

New tech builds ultralow-loss integrated photonic circuits

— EPFL scientists have developed ultralow-loss silicon nitride integrated circuits that are central for many photonic devices, such as chip-scale frequency combs, narrow-linewidth lasers, coherent LiDAR, and neuromorphic computing.

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