One leading algorithmic paradigm on NISQ computers is theVariational Quantum Algorithm (VQA) with a few prominent examples like the Variational Quantum Eignensolver (VQE) [50], quantum approximate optimization algorithm (QAOA) [20], and more in [4]. Quantum machine learning isafast-developing emerging field (e.g., see the survey [5]) where variational quantum algorithms (VQAs) (e.g., see thesurvey[4]areoneofthemost promising candidates forNISQ applications.

However, the performance of label-mapping-based visual prompting (LM-VP) under adversarial attacks and MIAs lacks evaluation.

We introduced an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features.

The understanding of such failure cases is still limited. For instance, the training process of reinforcement learning is unstable and the learning curve can fluctuate during training in ways that are hard to predict. The probability of obtaining satisfactory policies can also be inherently low in reward-sparse or highly nonlinear control tasks.

However , an condition (MaincontribONLINE-DPP that dence, but , the onlypoly(k,log ( 1/ )) vectors, (k+ log ( 1/ ))O(k) alongwith . P L = O( log ( 1/ )+ klogk)isdefinedlog ( 1/ )(using(3)).

J (Q)= E E[ (Q(s, a) Es0 P( |s,a)V (s0))] (1 )E 0[V (s0)], (9) withV (s) = log P aexpQ(s, a).