  Although quantum supremacy is yet to come, there has recently been an
increasing interest in identifying the potential of quantum machine learning
(QML) in the looming era of practical quantum computing. Motivated by this, in
this article we re-design multi-agent reinforcement learning (MARL) based on
the unique characteristics of quantum neural networks (QNNs) having two
separate dimensions of trainable parameters: angle parameters affecting the
output qubit states, and pole parameters associated with the output measurement
basis. Exploiting this dyadic trainability as meta-learning capability, we
propose quantum meta MARL (QM2ARL) that first applies angle training for
meta-QNN learning, followed by pole training for few-shot or local-QNN
training. To avoid overfitting, we develop an angle-to-pole regularization
technique injecting noise into the pole domain during angle training.
Furthermore, by exploiting the pole as the memory address of each trained QNN,
we introduce the concept of pole memory allowing one to save and load trained
QNNs using only two-parameter pole values. We theoretically prove the
convergence of angle training under the angle-to-pole regularization, and by
simulation corroborate the effectiveness of QM2ARL in achieving high reward and
fast convergence, as well as of the pole memory in fast adaptation to a
time-varying environment.
