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Automating Quantum System Mapping for Increased Stability

June 25, 2020 by Luke James

Using robotics research, Australian scientists have automated the mapping of quantum systems to understand the character of hardware errors, which helps to stabilize the technology.

Although new quantum computing architectures use qubits as sensors to provide actionable information that is useful for calibration or decoherence mitigation on neighboring data qubits, little work has been done to address how such schemes can be efficiently implemented to maximize information utilization. 

Now, as described in the journal Quantum Information on June 12, researchers at the University of Sydney in Australia have used techniques from autonomous vehicles (AVs) and robotics to assess the performance of quantum devices. This, they say, is an important process that will lead to a better understanding and stabilization of the emerging technology. 


Promising Results

According to the research team, their novel approach has been shown in experiments to outperform simplistic characterisation of these environments by three-fold, with a much higher result for more complex simulated environments. 

"Using this approach, we can map the 'noise' causing performance variations across quantum devices at least three times as quickly as a brute-force approach," said lead author Riddhi Gupta, a PhD student in the School of Physics. She added that the rapid assessment of the noise environment can help researchers to improve the stability of quantum devices. 

Quantum computing is still very much an emerging technology, however, that has not stopped promises that it will be able to revolutionize technology beyond the scope of classical computing and our wildest dreams. One of the biggest challenges facing the development of quantum systems is not being able to overcome hardware imperfections.

Qubits—the basic units of quantum technology—are highly sensitive to environmental conditions and disturbance, such as electromagnetic noise. These cause performance variations and compromise their usefulness. 


Ion trap research.

An ion trap used for the research in the Sydney Nanoscience Hub Quantum Control Laboratory. Image credit: University of Sydney.


Inspired by Robotics

Gupta has also used techniques from classical estimation used in robotics systems and adapted these to improve hardware performance. "Our idea was to adapt algorithms used in robotics that map the environment and place an object relative to other objects in their estimated terrain," she said. "We effectively use some qubits in the device as sensors to help understand the classical terrain in which other qubits are processing information."

Robotics systems like those found in robot vacuum cleaners rely on simultaneous localisation and mapping algorithms so that they can map their environments and estimate their location relative to that environment. This enables them to move around efficiently. However, applying these algorithms to destroys the qubits’ quantum information.

Instead, Gupta and the Sydney team have developed an adaptive algorithm—“Noise Mapping for Quantum Architectures”—that measures the performance of a single qubit and uses this data to estimate the capabilities of nearby qubits. 

“Rather than estimate the classical environment for each and every qubit, we are able to automate the process, reducing the number of measurements and qubits required, which speeds up the whole process," Ms Gupta said.

Gupta’s doctoral supervisor and founder of the quantum technology company Q-CTRL, Professor Michael Biercuk, said that the work is an “exciting” demonstration of how robotics could be used to directly shape the future of quantum computing. 

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