A deep learning model for noise prediction on near-term quantum devices
- URL: http://arxiv.org/abs/2005.10811v1
- Date: Thu, 21 May 2020 17:47:29 GMT
- Title: A deep learning model for noise prediction on near-term quantum devices
- Authors: Alexander Zlokapa, Alexandru Gheorghiu
- Abstract summary: We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model.
A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise.
- Score: 137.6408511310322
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. We
train a convolutional neural network on experimental data from a quantum device
to learn a hardware-specific noise model. A compiler then uses the trained
network as a noise predictor and inserts sequences of gates in circuits so as
to minimize expected noise. We tested this approach on the IBM 5-qubit devices
and observed a reduction in output noise of 12.3% (95% CI [11.5%, 13.0%])
compared to the circuits obtained by the Qiskit compiler. Moreover, the trained
noise model is hardware-specific: applying a noise model trained on one device
to another device yields a noise reduction of only 5.2% (95% CI [4.9%, 5.6%]).
These results suggest that device-specific compilers using machine learning may
yield higher fidelity operations and provide insights for the design of noise
models.
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