Quantum Noise Sensing by generating Fake Noise
- URL: http://arxiv.org/abs/2107.08718v1
- Date: Mon, 19 Jul 2021 09:42:37 GMT
- Title: Quantum Noise Sensing by generating Fake Noise
- Authors: Paolo Braccia, Leonardo Banchi, Filippo Caruso
- Abstract summary: We propose a framework to characterize noise in a realistic quantum device.
Key idea is to learn about the noise by mimicking it in a way that one cannot distinguish between the real (to be sensed) and the fake (generated) one.
We find that, when applied to the benchmarking case of Pauli channels, the SuperQGAN protocol is able to learn the associated error rates even in the case of spatially and temporally correlated noise.
- Score: 5.8010446129208155
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Noisy-Intermediate-Scale-Quantum (NISQ) devices are nowadays starting to
become available to the final user, hence potentially allowing to show the
quantum speedups predicted by the quantum information theory. However, before
implementing any quantum algorithm, it is crucial to have at least a partial or
possibly full knowledge on the type and amount of noise affecting the quantum
machine. Here, by generalizing quantum generative adversarial learning from
quantum states (Q-GANs) to quantum operations/superoperators/channels (here
named as SuperQGANs), we propose a very promising framework to characterize
noise in a realistic quantum device, even in the case of spatially and
temporally correlated noise (memory channels) affecting quantum circuits. The
key idea is to learn about the noise by mimicking it in a way that one cannot
distinguish between the real (to be sensed) and the fake (generated) one. We
find that, when applied to the benchmarking case of Pauli channels, the
SuperQGAN protocol is able to learn the associated error rates even in the case
of spatially and temporally correlated noise. Moreover, we also show how to
employ it for quantum metrology applications. We believe our SuperQGANs pave
the way for new hybrid quantum-classical machine learning protocols for a
better characterization and control of the current and future unavoidably noisy
quantum devices.
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