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Making Quantum Circuits More Robust

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The researchers focused on variational quantum circuits, which use quantum gates with trainable parameters that can learn a machine learning or quantum chemistry task.

Credit: Christine Daniloff, MIT

A team led by researchers at the Massachusetts Institute of Technology (MIT) has developed a framework to make quantum circuits more resilient to noise.

Called Quantum NAS (Noise Adaptive Search), the framework can determine the most robust quantum circuit for a certain computing task and produce a mapping pattern tailored to the qubits of the targeted quantum device.

MIT’s Song Han said, “Using this method, we can obtain many different circuits and mapping strategies at once with no need for many times of training.”

The process involved designing a “SuperCircuit” with all possible parameterized quantum gates in the design space, training the SuperCircuit once, and then using it to identify circuit architectures that meet a targeted objective.

The quantum circuits identified by the algorithm as the best were tested on real quantum devices; the researchers determined they outperformed quantum circuits produced using other methods.

From MIT News
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