  In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will
significantly reduce the fidelity of quantum computing. Besides, the noise on
quantum devices is not stable. This leads to a challenging problem: At
run-time, is there a way to efficiently achieve a consistent high-fidelity
quantum system on unstable devices? To study this problem, we take quantum
learning (a.k.a., variational quantum algorithm) as a vehicle, such as
combinatorial optimization and machine learning. A straightforward approach is
to optimize a Circuit with a parameter-shift approach on the target quantum
device before using it; however, the optimization has an extremely high time
cost, which is not practical at run-time. To address the pressing issue, in
this paper, we proposed a novel quantum pulse-based noise adaptation framework,
namely QuPAD. In the proposed framework, first, we identify that the CNOT gate
is the fidelity bottleneck of the conventional VQC, and we employ a more robust
parameterized multi-quit gate (i.e., Rzx gate) to replace the CNOT gate.
Second, by benchmarking the Rzx gate with different parameters, we build a
fitting function for each coupling qubit pair, such that the deviation between
the theoretic output of the Rzx gate and its on-device output under a given
pulse amplitude and duration can be efficiently predicted. On top of this, an
evolutionary algorithm is devised to identify the pulse amplitude and duration
of each Rzx gate (i.e., calibration) and find the quantum circuits with high
fidelity. Experiments show that the runtime on quantum devices of QuPAD with
8-10 qubits is less than 15 minutes, which is up to 270x faster than the
parameter-shift approach. In addition, compared to the vanilla VQC as a
baseline, QuPAD can achieve 59.33% accuracy gain on a classification task, and
average 66.34% closer to ground state energy for molecular simulation.
