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.

翻译：在吵闹的中间级量子时代(NISQ),一个关键问题是如何应对物理量子比特(qubits)中存在的高噪音水平。 量子误差的纠正很有希望, 但需要大量( 例如超过1000) 量子误差来创建一个“ 完美” Qubit, 超过现有量子计算机的容量。 本文的目的是从另一个角度解决噪音问题: 而不是为一般量子算法创建完美的qubits, 我们研究如何减轻用于计算算法的噪音问题。 具体地说, 本文针对量子神经网络( QNNN), 并提议学习培训阶段的错误, 以便让指定的QNNM模型能够适应噪音。 结果, QNNN不需要或少许更多的物理量子比特, 这对于近期量子计算机来说更为现实。 要实现这一目标, 具体应用的编译器是不可或缺的: 一方面,如果从逻辑量子比特到物理量子网络( QNNNN) 网络(QNNNN) 的精确度网络网络(QNNN), 建议的准确性模型可以学习错误, QQ: 在另一个的实验中, 快速化模型中可以随机化过程中,, 。