  Quantum computing is among the most promising emerging techniques to solve
problems that are computationally intractable on classical hardware. A large
body of existing works focus on using variational quantum algorithms on the
gate level for machine learning tasks, such as the variational quantum circuit
(VQC). However, VQC has limited flexibility and expressibility due to limited
number of parameters, e.g. only one parameter can be trained in one rotation
gate. On the other hand, we observe that quantum pulses are lower than quantum
gates in the stack of quantum computing and offers more control parameters.
Inspired by the promising performance of VQC, in this paper we propose
variational quantum pulses (VQP), a novel paradigm to directly train quantum
pulses for learning tasks. The proposed method manipulates variational quantum
pulses by pulling and pushing the amplitudes of pulses in an optimization
framework. Similar to variational quantum algorithms, our framework to train
pulses maintains the robustness to noise on Noisy Intermediate-Scale Quantum
(NISQ) computers. In an example task of binary classification, VQP learning
achieves up to 11% and 9% higher accuracy compared with VQC learning on the
qiskit noise simulators (with noise model from real machine) and ibmq-jarkata,
respectively, demonstrating its effectiveness and feasibility. Stability for
VQP to obtain reliable results has also been verified in the presence of noise.
