Search

Fault-tolerant quantum computation using two-dimensional topological quantum error correcting codes can benefit from multi-qubit long-range operations. By using simple commutation rules, a quantum circuit can be transpiled into a sequence of solely non-Clifford multi-qubit gates. Prior work on fault-tolerant compilation avoids optimal scheduling of such gates since they reduce the parallelizability of the circuit. We observe that the reduced parallelization potential is outweighed by the significant reduction in the number of gates. We therefore devise a method for scheduling multi-qubit lattice surgery using an earliest-available-first policy, solving the associated forest packing problem using a representation of the multi-qubit gates as Steiner trees. Our extensive testing on random and application-inspired circuits demonstrates the method's scalability and performance. We show that the transpilation significantly reduces the circuit length on the set of circuits tested, and that the resulting circuit of multi-qubit gates has a further reduction in the expected circuit execution time compared to serial execution., Comment: 21 pages, 7 figures, 4 tables

The appropriate hydration is associated with better athlete performance. The imbalance is the main cofactor to the decline of hydration status of the athlete, this change, it's not just because intense sweating, but also low replacement of body fluids. This study aimed to evaluate the hydration level of junior football players during a training session. Fourteen players from a professional football team in the city of Pouso Alegre were evaluated. The urinary collection was executed before the training session. The anthropometric measurement, measuring body weight, assessing the variation of body weight before and after exercise, and identifying Theo players' hydration strategies through a questionnaire containing 6 objective questions. To assess the athletes' hydration status, the % of weight variation and urinary density were used. Data analysis and classification of hydration status were determined using a NATA (2000) table, and the results of the questionnaire where determined using statistics and tables. The hydration status after training, according to weight variation, most athletes are hydrated (79%), and the rest with minimal dehydration (21%). Pre-workout hydration status, 7% are hydrated, 79% minimal dehydration, and 14% significant dehydration. It is concluded that most players are hydrated, and the hydration strategies used by players are insufficient, which may reflect on inadequate water consumption., A hidratação adequada está relacionada ao melhor desempenho e performance do indivíduo. O desequilíbrio do balanço eletrolítico é o principal cofator para a decadência do estado de hidratação do jogador, tal mudança, não é apenas por causa da sudorese intensa, mas, também a baixa reposição de fluídos corporais. Este estudo teve como objetivo avaliar o nível de hidratação de jogadores juniores de futebol durante uma sessão de treinamento. Foram avaliados 14 jogadores de uma equipe de futebol profissional da cidade de Pouso Alegre. Foi realizado a coleta urinária antes da sessão de treinamento. A avaliação antropométrica, aferindo o peso corporal, avaliando a variação do peso corporal pré e pós exercício, e identificou as estratégias dos jogadores sobre hidratação através de um questionário contendo 6 perguntas objetivas. Para avaliar o estado de hidratação dos jogadores foi utilizado o % de da variação de peso e a densidade urinária. A análise dos dados e a classificação do estado de hidratação foi determinado através de uma tabela NATA (2000). Em relação ao estado de hidratação após o treino, de acordo com a variação de peso, a maior parte dos jogadores encontram hidratados (79%), e o restante com desidratação mínima (21%). A respeito ao estado de hidratação pré-treino, 7% estão hidratados, 79% com desidratação mínima e 14% desidratação significativa. Conclui-se que a maior parte dos jogadores estão hidratados, e é insuficiente as estratégias de hidratação utilizadas pelos jogadores, o que pode refletir no consumo hídrico inadequado.

We study a two-dimensional packing problem where rectangular items are placed into a circular container to maximize either the number or the total area of items packed. We adapt a mixed-integer linear programming model from the case with a rectangular container and design a cutting plane method to solve this problem by adding linear cuts to forbid items from being placed outside the circle. We show that this linear model allows us to prove optimality for instances larger than those solved using the state-of-the-art non-linear model for the same problem. We also propose a simple parallel algorithm that efficiently enumerates all non-dominated subsets of items and verifies whether pertinent subsets fit into the container using an adapted version of our linear model. Computational experiments using large benchmark instances attest that this enumerative algorithm generally provides better solutions than the best heuristics from the literature when maximizing the number of items packed. Instances with up to 30 items are now solved to optimality, against the eight-item instance previously solved.

We study a two-dimensional packing problem where rectangular items are placed into a circular container to maximize either the number or the total area of items packed. We adapt a mixed-integer linear programming model from the case with a rectangular container and design a cutting plane method to solve this problem by adding linear cuts to forbid items from being placed outside the circle. We show that this linear model allows us to prove optimality for instances larger than those solved using the state-of-the-art non-linear model for the same problem. We also propose a simple parallel algorithm that efficiently enumerates all non-dominated subsets of items and verifies whether pertinent subsets fit into the container using an adapted version of our linear model. Computational experiments using large benchmark instances attest that this enumerative algorithm generally provides better solutions than the best heuristics from the literature when maximizing the number of items packed. Instances with up to 30 items are now solved to optimality, against the eight-item instance previously solved.

The ABC storage is the most popular class-based policy for the storage location assignment in warehouses. It divides a storage area into three zones and assigns the most demanded products to the best-located zone. Despite the policy's popularity, arbitrary zone sizes are commonly used, which can lead to major efficiency losses. We investigate how several factors, such as the warehouse layout, the demand characteristics, and the storage and routing policies, impact the solutions for the zone sizing problem. We propose a new methodology to solve it using machine learning models to predict the optimal zone sizes considering the mentioned factors. We simulate many common manual warehouse settings, such as the multi-block layout, demand distributions, and several operating policies, to observe which zone sizes lead to the best performance in each one. The data generated is used to train four regression models – ordinary least squares, regression tree, random forest, and multilayer perceptron – to predict the optimal zone sizes from the best ones observed. Computational experiments show that zone sizes provided by all models significantly improve the order picking efficiency when compared to the arbitrary zone sizes commonly used, notably for the one-zone (random policy), the two-zone (20/80 rule), and the three-zone (20/30/50) systems. The proposed methodology is easily adaptable for different warehousing systems and problems when enough data is available to train the models. The resulting linear functions and decision trees are made available and can be used by practitioners for determining zone sizes for their particular warehouse.

Storage location and order picking are two interdependent problems arising in warehouse planning traditionally solved independently. We introduce and model the integrated storage location and order picking problem and four special cases with imposed routing policies (return, S-shape, midpoint and largest gap). Experiments show that these models are difficult to solve, even for small warehouses and few orders. Therefore, we present a General Variable Neighborhood Search metaheuristic, which is observed to be very efficient for those small instances. For larger warehouses and more pickings, we show that our metaheuristic significantly improves solutions generated by common storage policies.

In the Quadratic Assignment Problem (QAP), facilities are assigned to sites in order to minimize interactions between pairs of facilities. Although easy to define, it is among the hardest problems in combinatorial optimization, due to its non-linear nature. After decades of research on the QAP, many variants of this problem arose to deal with different applications. Along with the QAP, we consider four variants – the Quadratic Bottleneck Assignment Problem, the Biquadratic Assignment Problem, the Quadratic Semi-Assignment Problem, and the Generalized QAP – and develop a single framework to solve them all. Our parallel memetic iterated tabu search (PMITS) extends the most successful heuristics to solve the QAP. It combines the diversification phase of generating new local optima found after solutions modified by a new crossover operator that is biased towards one of the parents, with the intensification phase of an effective tabu search which uses a simplified tabu list structure to reduce the number of parameters and a new long-term memory that saves solutions previously visited to speed up the search. Solutions are improved concurrently using parallelism, and a convergence criterion determines whether the search stops according to the best solutions in each parallel search. Computational experiments using the hardest benchmark instances from the literature attest the effectiveness of the PMITS, showing its competitiveness when compared to the state-of-the-art methods, sequential and parallel, to solve the QAP. We also show that PMITS significantly outperforms the best methods found for all four variants of the QAP, significantly updating their literature.

Storage location and order picking are two interdependent problems arising in warehouse planning traditionally solved independently. We introduce and model the integrated storage location and order picking problem and four special cases with imposed routing policies (return, S-shape, midpoint and largest gap). Experiments show that these models are difficult to solve, even for small warehouses and few orders. Therefore, we present a General Variable Neighborhood Search metaheuristic, which is observed to be very efficient for those small instances. For larger warehouses and more pickings, we show that our metaheuristic significantly improves solutions generated by common storage policies.

In the Quadratic Assignment Problem (QAP), facilities are assigned to sites in order to minimize interactions between pairs of facilities. Although easy to define, it is among the hardest problems in combinatorial optimization, due to its non-linear nature. After decades of research on the QAP, many variants of this problem arose to deal with different applications. Along with the QAP, we consider four variants – the Quadratic Bottleneck Assignment Problem, the Biquadratic Assignment Problem, the Quadratic Semi-Assignment Problem, and the Generalized QAP – and develop a single framework to solve them all. Our parallel memetic iterated tabu search (PMITS) extends the most successful heuristics to solve the QAP. It combines the diversification phase of generating new local optima found after solutions modified by a new crossover operator that is biased towards one of the parents, with the intensification phase of an effective tabu search which uses a simplified tabu list structure to reduce the number of parameters and a new long-term memory that saves solutions previously visited to speed up the search. Solutions are improved concurrently using parallelism, and a convergence criterion determines whether the search stops according to the best solutions in each parallel search. Computational experiments using the hardest benchmark instances from the literature attest the effectiveness of the PMITS, showing its competitiveness when compared to the state-ofthe-art methods, sequential and parallel, to solve the QAP. We also show that PMITS significantly outperforms the best methods found for all four variants of the QAP, significantly updating their literature