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We couple to group field theory (GFT) a scalar field that encodes the entanglement between manifold sites. The scalar field provides a relational clock that enables the derivation of the Hamiltonian of the system from the GFT action. Inspecting the Hamiltonian, we show that a theory of emergent gravity arises, and that this can be recast according to the Ashtekar's formulation of general relativity. The evolution of the GFT observables is regulated by the Shroedinger equation generated by the Hamiltonian. This is achieved by imposing a renormalization group (RG) flow that corresponds to a simplified Ricci flow. As a consequence of the quantization procedure, the Hamiltonian is recovered to be non-Hermitian, and can be related to the complex action formalism, in which the initial conditions and the related future evolution of the systems are dictated by the imaginary part of the action., Comment: 32 pages, to appear in JHEP

Deep neural networks (DNN) can approximate value functions or policies for reinforcement learning, which makes the reinforcement learning algorithms more powerful. However, some DNNs, such as convolutional neural networks (CNN), cannot extract enough information or take too long to obtain enough features from the inputs under specific circumstances of reinforcement learning. For example, the input data of Google Research Football, a reinforcement learning environment which trains agents to play football, is the small map of players' locations. The information is contained not only in the coordinates of players, but also in the relationships between different players. CNNs can neither extract enough information nor take too long to train. To address this issue, this paper proposes a deep q-learning network (DQN) with a graph neural network (GNN) as its model. The GNN transforms the input data into a graph which better represents the football players' locations so that it extracts more information of the interactions between different players. With two GNNs to approximate its local and target value functions, this DQN allows players to learn from their experience by using value functions to see the prospective value of each intended action. The proposed model demonstrated the power of GNN in the football game by outperforming other DRL models with significantly fewer steps., Comment: Accepted by the 2nd International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC 2022), which was held in Zhuhai, China in February 2022

In this work we provide a link between a nearly vanishing cosmological constant and chiral symmetry. This is accomplished with a modification of General Relativity coupled to a topological field theory, namely BF theory by introducing fermions charged under the BF theory gauge group. We find that the cosmological constant sources a chiral anomaly for the fermions, providing a `technical naturalness' explanation for the smallness of the observed cosmological constant. Applied to the early universe, we show that production of fermions during inflation can provide all the dark matter in the universe today, in the form of superheavy dark baryons., Comment: 10 pages, some typographical errors corrected, results unchanged