Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers.
We present a general framework based on machine learning for reducing the impact of quantum hardware noise and limitations on quantum circuits. Given a computational task and a device model, our noise-aware circuit learning algorithm (NACL) outputs an optimized circuit to accomplish this task in the presence of noise.
For details see “Machine learning of noise-resilient quantum circuits” by Lukasz Cincio, Kenneth Rudinger, Mohan Sarovar, Patrick J. Coles. arXiv:2007.01210.