Monte Carlo Tree Search and Monte Carlo Search have good results for many combinatorial problems. In this paper we propose to use Monte Carlo Search to design mathematical expressions that are used as exploration terms for Monte Carlo Tree Search algorithms. The optimized Monte Carlo Tree Search algorithms are PUCT and SHUSS. We automatically design the PUCT and the SHUSS root exploration terms. For small search budgets of 32 evaluations the discovered root exploration terms make both algorithms competitive with usual PUCT.

To develop a strategy to design VQC in an automated way, i.e. quantum architecture search (QAS), some researchers Considering the noise level limit, one crucial aspect have turned their attention to the classical Neural Architecture for quantum machine learning is to design a highperforming Search (NAS) framework. NAS focuses on automating variational quantum circuit architecture the design of neural network structures [Elsken et al., with small number of quantum gates. As the classical 2019], but often grapple with the challenge of evaluating a neural architecture search (NAS), quantum architecture vast number of possible network architectures. The Monte search methods (QAS) employ methods Carlo Tree Search (MCTS) algorithm addresses this issue by like reinforcement learning, evolutionary algorithms iteratively exploring and evaluating segments of the search and supernet optimization to improve the space, thereby identifying promising neural network structures search efficiency. In this paper, we propose a novel without exhaustive enumeration [Silver et al., 2016; qubit-wise architecture search (QWAS) method, Wang et al., 2020]. However, the efficiency of the search is which progressively search one-qubit configuration significantly influenced by the manually predefined action per stage, and combine with Monte Carlo Tree space before the tree construction. To address this issue, Search algorithm to find good quantum architectures [Wang et al., 2021] proposed an improved MCTS-based algorithm by partitioning the search space into several called Latent Action Neural Architecture Search good and bad subregions. The numerical experimental (LaNAS) that learns a latent action space that best fits the results indicate that our proposed method can problem to be solved.

We consider the problem of contextual multi-armed bandits in the setting of hypothesis transfer learning. That is, we assume having access to a previously learned model on an unobserved set of contexts, and we leverage it in order to accelerate exploration on a new bandit problem. Our transfer strategy is based on a re-weighting scheme for which we show a reduction in the regret over the classic Linear UCB when transfer is desired, while recovering the classic regret rate when the two tasks are unrelated. We further extend this method to an arbitrary amount of source models, where the algorithm decides which model is preferred at each time step. Additionally we discuss an approach where a dynamic convex combination of source models is given in terms of a biased regularization term in the classic LinUCB algorithm. The algorithms and the theoretical analysis of our proposed methods substantiated by empirical evaluations on simulated and real-world data.

Monte-Carlo Tree Search (MCTS) methods are drawing great interest after yielding breakthrough results in computer Go. This paper proposes a Bayesian approach to MCTS that is inspired by distributionfree approaches such as UCT [13], yet significantly differs in important respects. The Bayesian framework allows potentially much more accurate (Bayes-optimal) estimation of node values and node uncertainties from a limited number of simulation trials. We further propose propagating inference in the tree via fast analytic Gaussian approximation methods: this can make the overhead of Bayesian inference manageable in domains such as Go, while preserving high accuracy of expected-value estimates. We find substantial empirical outperformance of UCT in an idealized bandit-tree test environment, where we can obtain valuable insights by comparing with known ground truth. Additionally we rigorously prove on-policy and off-policy convergence of the proposed methods.