Space from Hilbert Space: Recovering Geometry from Bulk Entanglement

We examine how to construct a spatial manifold and its geometry from the entanglement structure of an abstract quantum state in Hilbert space. Given a decomposition of Hilbert space $\mathcal{H}$ into a tensor product of factors, we consider a class of "redundancy-constrained states" in $\mathcal{H}$ that generalize the area-law behavior for entanglement entropy usually found in condensed-matter systems with gapped local Hamiltonians. Using mutual information to define a distance measure on the graph, we employ classical multidimensional scaling to extract the best-fit spatial dimensionality of the emergent geometry. We then show that entanglement perturbations on such emergent geometries naturally give rise to local modifications of spatial curvature which obey a (spatial) analog of Einstein's equation. The Hilbert space corresponding to a region of flat space is finite-dimensional and scales as the volume, though the entropy (and the maximum change thereof) scales like the area of the boundary. A version of the ER=EPR conjecture is recovered, in that perturbations that entangle distant parts of the emergent geometry generate a configuration that may be considered as a highly quantum wormhole.Comment: 37 pages, 5 figures. Updated notation, references, and acknowledgemen

Polymerisable surfactants for polymethacrylates using catalytic chain transfer polymerisation (CCTP) combined with sulfur free-RAFT in emulsion polymerisation

Statistical copolymers of methacrylic acid and methyl methacrylate were synthesised via free radical catalytic chain transfer polymerisation (CCTP) in emulsion to form a hydrophilic emulsifier/surfactant. The vinyl-terminated oligomers were in turn utilised as chain transfer agents, with no further purification, for the formation of diblock copolymers with butyl and methyl methacrylate which constitutes the emulsifier via sulfur-free reversible addition–fragmentation chain transfer polymerisation (SF-RAFT). In turn these polymers were solubilized with various concentrations of ammonium hydroxide and utilised in the surfactant-free emulsion polymerization of butyl methacrylate using persulfate initiators, which also stabilized the polymer particles with observed no coagulation, with solid contents as high as 40%

An experimental study of the hydrodynamic behavior of a TLP platform for a 5MW wind turbine with OWC devices

An experimental study of the hydrodynamic behavior of a Tension-Leg Platform (TLP) for a 5MW Wind Turbine, featuring Wave Energy Converter (WEC) devices of the Oscillating Water Column type is presented. The examined triangular platform includes three vertical cylinders at the corners, providing the required buoyancy, each of them surrounded by a thin skirt, open at its lower end, forming the OWC chamber. A central vertical cylinder is included for the wind turbine installation. All cylinders are structurally connected with cylindrical bracing. The hydrodynamic response of the platform in the surge direction is experimentally verified, together with the resulting pressures and air fluxes inside the OWC chamber and the dynamic tensions in the lines of the mooring system

Predicting Dominance Rankings for Score-Based Games

Game competitions may involve different player roles and be score-based rather than win/loss based. This raises the issue of how best to draw opponents for matches in ongoing competitions, and how best to rank the players in each role. An example is the Ms Pac-Man versus Ghosts Competition which requires competitors to develop software controllers to take charge of the game's protagonists: participants may develop software controllers for either or both Ms Pac-Man and the team of four ghosts. In this paper, we compare two ranking schemes for win-loss games, Bayes Elo and Glicko. We convert the game into one of win-loss ("dominance") by matching controllers of identical type against the same opponent in a series of pair-wise comparisons. This implicitly creates a "solution concept" as to what a constitutes a good player. We analyze how many games are needed under two popular ranking algorithms, Glicko and Bayes Elo, before one can infer the strength of the players, according to our proposed solution concept, without performing an exhaustive evaluation. We show that Glicko should be the method of choice for online score-based game competitions

Multiobjective Monte Carlo Tree Search for Real-Time Games

Multiobjective optimization has been traditionally a matter of study in domains like engineering or finance, with little impact on games research. However, action-decision based on multiobjective evaluation may be beneficial in order to obtain a high quality level of play. This paper presents a multiobjective Monte Carlo tree search algorithm for planning and control in real-time game domains, those where the time budget to decide the next move to make is close to 40 ms. A comparison is made between the proposed algorithm, a single-objective version of Monte Carlo tree search and a rolling horizon implementation of nondominated sorting evolutionary algorithm II (NSGA-II). Two different benchmarks are employed, deep sea treasure (DST) and the multiobjective physical traveling salesman problem (MO-PTSP). Using the same heuristics on each game, the analysis is focused on how well the algorithms explore the search space. Results show that the algorithm proposed outperforms NSGA-II. Additionally, it is also shown that the algorithm is able to converge to different optimal solutions or the optimal Pareto front (if achieved during search)

Genome sequences of two pseudorabies virus strains isolated in Greece

Pseudorabies virus (species Suid herpesvirus 1) belongs to the genus Varicellovirus, subfamily Alphaherpesvirinae, family Herpesviridae, and is the causative agent of an acute and frequently fatal disease that affects mainly pigs. Here, we report the genome sequences of two strains of this virus isolated in Greece in 2010

Rolling horizon methods for games with continuous states and actions

It is often the case that games have continuous dynamics and allow for continuous actions, possibly with with some added noise. For larger games with complicated dynamics, having agents learn offline behaviours in such a setting is a daunting task. On the other hand, provided a generative model is available, one might try to spread the cost of search/learning in a rolling horizon fashion (e.g. as in Monte Carlo Tree Search). In this paper we compare T-HOLOP (Truncated Hierarchical Open Loop Planning), an open loop planning algorithm at least partially inspired by MCTS, with a version of evolutionary planning that uses CMA-ES (which we call EVO-P) in two planning benchmark problems (Inverted Pendulum and the Double Integrator) and in Lunar Lander, a classic arcade game. We show that EVO-P outperforms T-HOLOP in the classic benchmarks, while T-HOLOP is unable to find a solution using the same heuristics. We conclude that off-the-shelf evolutionary algorithms can be used successfully in a rolling horizon setting, and that a different type of heuristics might be needed under different optimisation algorithms