On the correction of anomalous phase oscillation in entanglement witnesses using quantum neural networks

Entanglement of a quantum system depends upon relative phase in complicated ways, which no single measurement can reflect. Because of this, entanglement witnesses are necessarily limited in applicability and/or utility. We propose here a solution to the problem using quantum neural networks. A quantum system contains the information of its entanglement; thus, if we are clever, we can extract that information efficiently. As proof of concept, we show how this can be done for the case of pure states of a two-qubit system, using an entanglement indicator corrected for the anomalous phase oscillation. Both the entanglement indicator and the phase correction are calculated by the quantum system itself acting as a neural network

A quantum neural network computes its own relative phase

Complete characterization of the state of a quantum system made up of subsystems requires determination of relative phase, because of interference effects between the subsystems. For a system of qubits used as a quantum computer this is especially vital, because the entanglement, which is the basis for the quantum advantage in computing, depends intricately on phase. We present here a first step towards that determination, in which we use a two-qubit quantum system as a quantum neural network, which is trained to compute and output its own relative phase

Prospering through Prospera: CCT Impacts on Educational Attainment and Achievement in Mexico

This paper develops and estimates a dynamic model of student enrollment, school choice, academic achievement and grade progression to evaluate the impacts of Mexico’s conditional cash transfer program Prospera on educational outcomes over grades 4-9. Academic achievement is measured by nationwide standardized test scores in mathematics and Spanish. Enrollment decisions are the outcomes of sequential decisions at each age from individuals’ feasible choice sets, determined by the types of schools locally available and local-labor-market opportunities. The achievement production function has a value-added structure. Model parameters are estimated by maximum likelihood using nationwide administrative test-score data (the ENCEL data) combined with survey data from students and parents, census labor-market data, and geo-coded school-location data. The estimation approach controls for selective school enrollment in different types of schools, grade retention and unobserved heterogeneity. The results show that the Prospera program increases school enrollment and academic achievement for program beneficiaries in lower-secondary school grades (grades 7-9). The average test-score impacts are 0.09-0.13 standard deviations in mathematics and 0.03-0.05 standard deviations in Spanish. Students from the most disadvantaged backgrounds experience the largest impacts. The availability of telesecondary distance-learning schools is shown to be an important determinant of the Prospera program’s impacts on educational outcomes