Parallel State Transfer and Efficient Quantum Routing on Quantum Networks

We study the routing of quantum information in parallel on multi-dimensional networks of tunable qubits and oscillators. These theoretical models are inspired by recent experiments in superconducting circuits using Josephson junctions and resonators. We show that perfect parallel state transfer is possible for certain networks of harmonic oscillator modes. We further extend this to the distribution of entanglement between every pair of nodes in the network, finding that the routing efficiency of hypercube networks is both optimal and robust in the presence of dissipation and finite bandwidth.Comment: 5 pages, 3 figure

Entanglement and Composite Bosons

We build upon work by C. K. Law [Phys. Rev. A 71, 034306 (2005)] to show in general that the entanglement between two fermions largely determines the extent to which the pair behaves like an elementary boson. Specifically, we derive upper and lower bounds on a quantity that governs the bosonic character of a pair of fermions when N such pairs approximately share the same wavefunction. Our bounds depend on the purity of the single-particle density matrix, an indicator of entanglement, and demonstrate that if the entanglement is sufficiently strong, the quantity in question approaches its ideal bosonic value.Comment: 10 page

Deterministic and cascadable conditional phase gate for photonic qubits

Previous analyses of conditional \phi-phase gates for photonic qubits that treat cross-phase modulation (XPM) in a causal, multimode, quantum field setting suggest that a large (~\pi rad) nonlinear phase shift is always accompanied by fidelity-degrading noise [J. H. Shapiro, Phys. Rev. A 73, 062305 (2006); J. Gea-Banacloche, Phys. Rev. A 81, 043823 (2010)]. Using an atomic V-system to model an XPM medium, we present a conditional phase gate that, for sufficiently small nonzero \phi, has high fidelity. The gate is made cascadable by using using a special measurement, principal mode projection, to exploit the quantum Zeno effect and preclude the accumulation of fidelity-degrading departures from the principal-mode Hilbert space when both control and target photons illuminate the gate

Phase-noise limitations on single-photon cross-phase modulation with differing group velocities

A framework is established for evaluating {\sc cphase} gates that use single-photon cross-phase modulation (XPM) originating from the Kerr nonlinearity. Prior work Phys. Rev. A {\bf 73,} 062305 (2006)], which assumed that the control and target pulses propagated at the same group velocity, showed that the causality-induced phase noise required by a non-instantaneous XPM response function precluded the possibility of high-fidelity $\pi$-radian conditional phase shifts. The framework presented herein incorporates the more realistic case of group-velocity disparity between the control and target pulses, as employed in existing XPM-based fiber-optical switches. Nevertheless, the causality-induced phase noise identified in [Phys. Rev. A {\bf 73,} 062305 (2006)] still rules out high-fidelity $\pi$-radian conditional phase shifts. This is shown to be so for both a reasonable theoretical model for the XPM response function and for the experimentally-measured XPM response function of silica-core fiber.Comment: 8 pages, 4 figure

Researching for better instructional methods using AB experiments in MOOCs: results and challenges

We conducted two AB experiments (treatment vs. control) in a massive open online course. The first experiment evaluates deliberate practice activities (DPAs) for developing problem solving expertise as measured by traditional physics problems. We find that a more interactive drag-and-drop format of DPA generates quicker learning than a multiple choice format but DPAs do not improve performance on solving traditional physics problems more than normal homework practice. The second experiment shows that a different video shooting setting can improve the fluency of the instructor which in turn improves the engagement of the students although it has no significant impact on the learning outcomes. These two cases demonstrate the potential of MOOC AB experiments as an open-ended research tool but also reveal limitations. We discuss the three most important challenges: wide student distribution, “open-book” nature of assessments, and large quantity and variety of data. We suggest possible methods to cope with those.Google (Firm)Massachusetts Institute of Technolog

Learning experiments in a Massive Open Online Course

Thesis: S.M., Massachusetts Institute of Technology, Department of Physics, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 63-65).We present results from two treatment / control experiments in the 8.MReV: Mechanics ReView massive open online course (MOOC) run on edX.org during summer 2014. We compare the efficacy of physics homework problems: (1) traditional physics problems involving many skills, (2) deliberate-practice activities that train individual skills using the drag-and-drop format, and (3) analogous deliberate practice activities in multiple choice format. Using a common assessment, our results suggest that traditional instruction is more effective than deliberate practice activities cast in the multiple-choice format; comparison of traditional problems and drag-and-drop deliberate practice is so far inconclusive. Some evidence suggests users prefer the drag-and-drop format over multiple-choice and are more engaged in such problems. In a separate experiment, we investigate the validity of the pre-test/post-test methodology in a MOOC environment where students receive feedback on the pretest and can view the correct answer after finishing a pre-test problem. It seems that little learning occurs during the pre-test and that exposure to a problem on the pre-test usually does not provide students an advantage on the post-test.by Christopher A. Chudzicki.S.M

Learning Experiments Using AB Testing at Scale

We report the one of the first applications of treatment/control group learning experiments in MOOCs. We have compared the efficacy of deliberate practice-practicing a key procedure repetitively-with traditional practice on "whole problems". Evaluating the learning using traditional whole problems we find that traditional practice outperforms drag and drop, which in turn outperforms multiple choice. In addition, we measured the amount of learning that occurs during a pretest administered in a MOOC environment that transfers to the same question if placed on the posttest. We place a limit on the amount of such transfer, which suggests that this type of learning effect is very weak compared to the learning observed throughout the entire course.Google (Firm)Massachusetts Institute of TechnologyNational Science Foundation (U.S.

Validating the pre/post-test in a MOOC environment

A standard method for measuring learning is to administer the same assessment before and after instruction. This pre/post-test technique is widely used in education research and has been used in our introductory physics MOOC to measure learning. One potential weakness of this paradigm is that post-test performance gains may result from exposure on the pre-test instead of instruction. This possibility is exacerbated in MOOCs where students receive multiple attempts per item, instant correct/incorrect feedback, and unlimited time (until the due date). To find the size of this problem in our recent MOOCs, we split the student population into two groups, each of which received identical post-tests but different subsets of post-test items on their group pre-test. We report a small overall advantage (2.9% ± 1.7%) on post-test items due to pre-test exposure. However, this advantage is not robust and is strongly diminished when one obviously anomalous item is removed

HypothesisWorks/hypothesis: Hypothesis for Python - version 4.5.9

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