This Machine Learning Algorithm Allows for Efficient and Accurate Verification of Quantum Devices

Technologies that exploit unique quantum mechanical behaviours are likely to become mainstream in the near future. Devices that utilise quantum information as input and output data, for example, may need thorough verification owing to inherent uncertainties. When the output of a device is time dependent and depends on previous inputs, verification becomes more difficult. For the first time, researchers utilising machine learning increased the efficiency of verification for time-dependent quantum devices by adding a memory effect found in these systems.

Although quantum computers have made headlines in the scientific press, most experts believe that these devices are still in their infancy. A quantum internet, on the other hand, may be closer to reality. Among other things, this would provide considerable security advantages over our current internet. Even this, though, will rely on technologies that have yet to see the light of day outside of the lab. While many basics of the devices that can power our quantum internet have been figured out, there are several engineering obstacles that must be overcome before they can be sold as goods. However, substantial research is being conducted to provide tools for the design of quantum devices.

Quoc Hoan Tran, a postdoctoral researcher, and Associate Professor Kohei Nakajima of the University of Tokyo’s Graduate School of Information Science and Technology have pioneered just such a tool, which they believe could make verifying the behaviour of quantum devices more efficient and precise than it is now. Their contribution is an algorithm that can learn the connection between quantum inputs and outputs and recreate the workings of a time-dependent quantum device. This strategy is indeed popular when investigating a classical physical system, but quantum information is notoriously difficult to store, making it nearly impossible.

“The technique to describe a quantum system based on its inputs and outputs is called quantum process tomography,” said Tran. “However, many researchers now report that their quantum systems exhibit some kind of memory effect where present states are affected by previous ones. 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.”

To develop their unique approach, Tran and Nakajima used machine learning and a technology known as quantum reservoir computing. This algorithm learns patterns of inputs and outputs that vary over time in a quantum system and successfully predicts how these patterns will change in scenarios that the algorithm has not yet seen. Because it does not require to understand the inner workings of a quantum system, as a more empirical technique could, but merely the inputs and outputs, the team’s algorithm can be simpler and give faster results.

“At present, our algorithm can emulate a certain kind of quantum system, but hypothetical devices may vary widely in their processing ability and have different memory effects. So the next stage of research will be to broaden the capabilities of our algorithms, essentially making something more general purpose and thus more useful,” said Tran. “I am excited by what quantum machine learning methods could do, by the hypothetical devices they might lead to.”

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