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Machine Learning Framework Models Quantum Devices

quantum reservoir computing representation

B represents the input states of a quantum system, and E is an auxiliary system that passes the sequence of input states to the quantum reservoir S.

Credit: Quoc Hoan Tran et al.

An algorithm developed by researchers at the University of Tokyo uses machine learning to determine the relationship between quantum inputs and outputs in order to reconstruct the workings of a time-dependent quantum device.  

The researchers used machine learning and quantum reservoir computing to build the algorithm, according to their paper on the work.

"Many researchers now report that their quantum systems exhibit some kind of memory effect where present states are affected by previous ones," says University of Tokyo postdoctoral researcher Quoc Hoan Tran. "This means that a simple inspection of input and output states cannot describe the time-dependent nature of the system. You could model the system repeatedly after every change in time, but this would be extremely computationally inefficient. Our aim was to embrace this memory effect and use it to our advantage rather than use brute force to overcome it."

From University of Tokyo
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


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