Direct Fidelity Estimation from Few Pauli Measurements

We describe a simple method for certifying that an experimental device prepares a desired quantum state ρ. Our method is applicable to any pure state ρ, and it provides an estimate of the fidelity between ρ and the actual (arbitrary) state in the lab, up to a constant additive error. The method requires measuring only a constant number of Pauli expectation values, selected at random according to an importance-weighting rule. Our method is faster than full tomography by a factor of d, the dimension of the state space, and extends easily and naturally to quantum channels

Laws for fiscal responsibility for subnational discipline : international experience

Fiscal responsibility laws are institutions with which multiple governments in the same economy -- national and subnational --can commit to help avoid irresponsible fiscal behavior that could have short-term advantages to one of them but that would be collectively damaging. Coordination failures with subnational governments in the 1990s contributed to macroeconomic instability and led several countries to adopt fiscal responsibility laws as part of the remedy. The paper analyzes the characteristics and effects of fiscal responsibility laws in seven countries -- Argentina, Australia, Brazil, Canada, Colombia, India, and Peru. Fiscal responsibility laws are designed to address the short time horizons of policymakers, free riders among government units, and principal agent problems between the national and subnational governments. The paper describes how the laws differ in the specificity of quantitative targets, the strength of sanctions, the methods for increasing transparency, and the level of government passing the law. Evidence shows that fiscal responsibility laws can help coordinate and sustain commitments to fiscal prudence, but they are not a substitute for commitment and should not be viewed as ends in themselves. They can make a positive contribution by adding to the collection of other measures to shore up a coalition of states with the central government in support of fiscal prudence. Policymakers contemplating fiscal responsibility laws may benefit from the systematic review of international practice. One common trait of successful fiscal responsibility laws for subnational governments is the commitment of the central government to its own fiscal prudence, which is usually reinforced by the application of the law at the national as well as the subnational level.Debt Markets,Banks&Banking Reform,Subnational Economic Development,Public Sector Economics,Access to Finance

The Supplemental Nutrition Assistance Program and Nutrient Intakes

The socioeconomic determinants of Food Stamp Program participation and the effects of program participation on nutrient intakes are investigated, using data from the 2003–04 and 2005–06 National Health and Nutrition Examination Survey (NHANES). An endogenous switching regression system of equations is estimated, which includes protein, vitamin A, vitamin C, calcium and iron. Participation in the FSP is found to play an important role in nutrient intakes. Socio-demographic variables such as income, household size and presence of children are also found to affect individuals’ decisions on program participation and nutrient intakes.Food Consumption/Nutrition/Food Safety, Food Security and Poverty,

SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning

Deep reinforcement learning (DRL) has gained great success by learning directly from high-dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better. In this paper, we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can handle both high-dimensional sensory inputs and symbolic planning. The task-level interpretability is enabled by relating symbolic actions to options.This framework features a planner -- controller -- meta-controller architecture, which takes charge of subtask scheduling, data-driven subtask learning, and subtask evaluation, respectively. The three components cross-fertilize each other and eventually converge to an optimal symbolic plan along with the learned subtasks, bringing together the advantages of long-term planning capability with symbolic knowledge and end-to-end reinforcement learning directly from a high-dimensional sensory input. Experimental results validate the interpretability of subtasks, along with improved data efficiency compared with state-of-the-art approaches