A deep learning model for noise prediction on near-term quantum devices
arXiv:2005.10811
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.
arXiv comment5 pages, 4 figures, 1 table. Comments welcome
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Cited by in corpus (8)
- The Variational Quantum Eigensolver: a review of methods and best practices
- Differentiable Quantum Architecture Search
- Optimal resource cost for error mitigation
- Computationally Efficient Zero Noise Extrapolation for Quantum Gate Error Mitigation
- Error mitigation by training with fermionic linear optics
- Supervised learning of random quantum circuits via scalable neural networks
- Divide-and-conquer verification method for noisy intermediate-scale quantum computation
- Qubit Readout Error Mitigation with Bit-flip Averaging