An international team of researchers led by scientists from the University of Bristol’s Quantum Engineering Technology Labs (QETLabs) are using machine learning to boost the performance of quantum computers.
“Machine learning will play a key role in the understanding and controlling of complex quantum systems such as chemical molecules”
The work, with Microsoft’s Quantum Architectures and Computation Group (QuArC) in the US, and Eindhoven University of Technology in the Netherlands, couples a machine learning algorithm on a classical computer with a reprogrammable two-qubit silicon quantum photonic processor. This allows a quantum device to be characterised, in this case measuring the behaviour of a single electron in a diamond.
Quantum simulation: Integrated silicon-photonic quantum processor (left);
Set-up for nitrogen-vacancy centre’s spin manipulation in diamond (right)
Characterising these quantum devices and predicting the behaviour of complex quantum systems are problems that can’t be solved with classical computing. However, feeding the results of the photonic processor into the machine learning system using Bayesian inference allows it to act as a simulator to explore the quantum system, dramatically speeding up the analysis of the quantum device, says researcher Dr Raffaele Santagati (above).
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“Machine learning will play a key role in the efficient characterisation, verification and validation of future quantum devices such as quantum computers, and also in the understanding and controlling of complex quantum systems such as chemical molecules,” says Dr Nathan Wiebe from Microsoft Research.
“Silicon based quantum photonics technologies allow the potential integration of tens of thousands of components on a single, tiny chip”
“The optical circuits on silicon, the same material as used in our microelectronic circuits, allow the processing of information carried by a single particle of light,” said Professor Mark Thompson, who led the Bristol research team. “Silicon based quantum photonics technologies allow the potential integration of tens of thousands of components on a single, tiny chip, promising numerous applications in the fields of communication, simulation and computing.”