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In the noisy intermediate-scale quantum (NISQ) era, one of the key questionsis how to deal with the high noise level existing in physical quantum bits(qubits). Quantum error correction is promising but requires an extensivenumber (e.g., over 1,000) of physical qubits to create one "perfect" qubit,exceeding the capacity of the existing quantum computers. This paper aims totackle the noise issue from another angle: instead of creating perfect qubitsfor general quantum algorithms, we investigate the potential to mitigate thenoise issue for dedicate algorithms. Specifically, this paper targets quantumneural 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, theimplementation of QNN needs no or a small number of additional physical qubits,which is more realistic for the near-term quantum computers. To achieve thisgoal, an application-specific compiler is essential: on the one hand, the errorcannot be learned if the mapping from logical qubits to physical qubits existsrandomness; on the other hand, the compiler needs to be efficient so that thelengthy training procedure can be completed in a reasonable time. In thispaper, we utilize the recent QNN framework, QuantumFlow, as a case study.Experimental results show that the proposed approach can optimize QNN modelsfor different errors in qubits, achieving up to 28% accuracy improvementcompared with the model obtained by the error-agnostic training.

Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow