Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study
on QuantumFlow
- URL: http://arxiv.org/abs/2109.03430v1
- Date: Wed, 8 Sep 2021 04:43:12 GMT
- Title: Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study
on QuantumFlow
- Authors: Zhiding Liang, Zhepeng Wang, Junhuan Yang, Lei Yang, Jinjun Xiong,
Yiyu Shi, Weiwen Jiang
- Abstract summary: This paper aims to tackle the noise issue from another angle.
Instead of creating perfect qubits for general quantum algorithms, we investigate the potential to mitigate the noise issue for dedicate algorithms.
This paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.
- Score: 25.408147000243158
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the noisy intermediate-scale quantum (NISQ) era, one of the key questions
is how to deal with the high noise level existing in physical quantum bits
(qubits). Quantum error correction is promising but requires an extensive
number (e.g., over 1,000) of physical qubits to create one "perfect" qubit,
exceeding the capacity of the existing quantum computers. This paper aims to
tackle the noise issue from another angle: instead of creating perfect qubits
for general quantum algorithms, we investigate the potential to mitigate the
noise issue for dedicate algorithms. Specifically, this paper targets quantum
neural network (QNN), and proposes to learn the errors in the training phase,
so that the identified QNN model can be resilient to noise. As a result, the
implementation of QNN needs no or a small number of additional physical qubits,
which is more realistic for the near-term quantum computers. To achieve this
goal, an application-specific compiler is essential: on the one hand, the error
cannot be learned if the mapping from logical qubits to physical qubits exists
randomness; on the other hand, the compiler needs to be efficient so that the
lengthy training procedure can be completed in a reasonable time. In this
paper, we utilize the recent QNN framework, QuantumFlow, as a case study.
Experimental results show that the proposed approach can optimize QNN models
for different errors in qubits, achieving up to 28% accuracy improvement
compared with the model obtained by the error-agnostic training.
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