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We present an approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device.
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Joel J Wallman and Joseph Emerson, “Noise tailoring for scalable quantum computation via randomized compiling,” Physical Review A 94
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Code is available at https://github.com/quantummind/deepQ
Cited in the paper.
As with most machine-learning applications, understanding how the learned information is represented within the network is challenging. We leave the problem of extracting characteristics of the noise model from the trained network for future work
Cited in the paper.
2019
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Gadi Aleksandrowicz et al., “Qiskit: An open-source framework for quantum computing,” (2019)
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