  Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ)
computers. Previous work for mitigating noise has primarily focused on
gate-level or pulse-level noise-adaptive compilation. However, limited research
efforts have explored a higher level of optimization by making the quantum
circuits themselves resilient to noise.
  We propose QuantumNAS, a comprehensive framework for noise-adaptive co-search
of the variational circuit and qubit mapping. Variational quantum circuits are
a promising approach for constructing QML and quantum simulation. However,
finding the best variational circuit and its optimal parameters is challenging
due to the large design space and parameter training cost. We propose to
decouple the circuit search and parameter training by introducing a novel
SuperCircuit. The SuperCircuit is constructed with multiple layers of
pre-defined parameterized gates and trained by iteratively sampling and
updating the parameter subsets (SubCircuits) of it. It provides an accurate
estimation of SubCircuits performance trained from scratch. Then we perform an
evolutionary co-search of SubCircuit and its qubit mapping. The SubCircuit
performance is estimated with parameters inherited from SuperCircuit and
simulated with real device noise models. Finally, we perform iterative gate
pruning and finetuning to remove redundant gates.
  Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers,
QuantumNAS significantly outperforms baselines. For QML, QuantumNAS is the
first to demonstrate over 95% 2-class, 85% 4-class, and 32% 10-class
classification accuracy on real QC. It also achieves the lowest eigenvalue for
VQE tasks on H2, H2O, LiH, CH4, BeH2 compared with UCCSD. We also open-source
TorchQuantum (https://github.com/mit-han-lab/torchquantum) for fast training of
parameterized quantum circuits to facilitate future research.
