In addressing the challenge posed by noise in actual quantum devices, the application of quantum error mitigation techniques becomes essential. These techniques are resource-efficient, making them viable for implement...
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ISBN:
(纸本)9798350344868;9798350344851
In addressing the challenge posed by noise in actual quantum devices, the application of quantum error mitigation techniques becomes essential. These techniques are resource-efficient, making them viable for implementation in noisy intermediate-scale quantum devices, unlike the resource-intensive quantum error correction codes. A prominent example among these techniques is clifford data regression, which employs a supervised learning approach. This work explores two variants of this technique, both of which add a non-trivial set of gates to the original circuit. The first variant leverages copies of the original circuit, whereas the second approach adds a layer of 1-qubit rotations.
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