An Introduction to Quantum Computing for Non-Physicists

Richard Feynman's observation that quantum mechanical effects could not be simulated efficiently on a computer led to speculation that computation in general could be done more efficiently if it used quantum effects. This speculation appeared justified when Peter Shor described a polynomial time quantum algorithm for factoring integers. In quantum systems, the computational space increases exponentially with the size of the system which enables exponential parallelism. This parallelism could lead to exponentially faster quantum algorithms than possible classically. The catch is that accessing the results, which requires measurement, proves tricky and requires new non-traditional programming techniques. The aim of this paper is to guide computer scientists and other non-physicists through the conceptual and notational barriers that separate quantum computing from conventional computing. We introduce basic principles of quantum mechanics to explain where the power of quantum computers comes from and why it is difficult to harness. We describe quantum cryptography, teleportation, and dense coding. Various approaches to harnessing the power of quantum parallelism are explained, including Shor's algorithm, Grover's algorithm, and Hogg's algorithms. We conclude with a discussion of quantum error correction.Comment: 45 pages. To appear in ACM Computing Surveys. LATEX file. Exposition improved throughout thanks to reviewers' comment

Poverty, policy, and industrialization : lessons from the distant past

Pessimists say industrialization increased poverty; optimists say it did not. The authors argue that how much industrialization eradicates poverty depends on the form industrialization takes. It is not economic growth by itself, but the processes and policies associated with different growth regimes which make the poor poorer. The authors address two questions : 1) what happened to the proportionate share of the population living in poverty, and to the living standards of the poor, during nineteenth century industrial revolutions?; and 2) why did poverty statistics behave the way they did? Modern economic growth may erode traditional entitlements that serve as safety nets in preindustrial societies. It may be convenient to think otherwise, but typically the poor in preindustrial European and North American societies were not supported by the family and private institutions. Much of the responsibility for the poor lay with the state and other formal, statelike institutions that intervened in food markets. Where laissez-faire policies were adopted during the Industrial Revolution, as in America and England, many of the poor (especially the extremely poor) became more vulnerable to adverse conditions.Environmental Economics&Policies,Services&Transfers to Poor,Rural Poverty Reduction,Safety Nets and Transfers,Governance Indicators

Adaptive Horizon Model Predictive Control and Al'brekht's Method

A standard way of finding a feedback law that stabilizes a control system to an operating point is to recast the problem as an infinite horizon optimal control problem. If the optimal cost and the optmal feedback can be found on a large domain around the operating point then a Lyapunov argument can be used to verify the asymptotic stability of the closed loop dynamics. The problem with this approach is that is usually very difficult to find the optimal cost and the optmal feedback on a large domain for nonlinear problems with or without constraints. Hence the increasing interest in Model Predictive Control (MPC). In standard MPC a finite horizon optimal control problem is solved in real time but just at the current state, the first control action is implimented, the system evolves one time step and the process is repeated. A terminal cost and terminal feedback found by Al'brekht's methoddefined in a neighborhood of the operating point is used to shorten the horizon and thereby make the nonlinear programs easier to solve because they have less decision variables. Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying the horizon length of Model Predictive Control (MPC) as needed. Its goal is to achieve stabilization with horizons as small as possible so that MPC methods can be used on faster and/or more complicated dynamic processes.Comment: arXiv admin note: text overlap with arXiv:1602.0861

An Introduction to Quantum Computing for Non-Physicists

Richard Feynman's observation that quantum mechanical effects could not be simulated efficiently on a computer led to speculation that computation in general could be done more efficiently if it used quantum effects. This speculation appeared justified when Peter Shor described a polynomial time quantum algorithm for factoring integers. In quantum systems, the computational space increases exponentially with the size of the system which enables exponential parallelism. This parallelism could lead to exponentially faster quantum algorithms than possible classically. The catch is that accessing the results, which requires measurement, proves tricky and requires new non-traditional programming techniques. The aim of this paper is to guide computer scientists and other non-physicists through the conceptual and notational barriers that separate quantum computing from conventional computing. We introduce basic principles of quantum mechanics to explain where the power of quantum computers comes from and why it is difficult to harness. We describe quantum cryptography, teleportation, and dense coding. Various approaches to harnessing the power of quantum parallelism are explained, including Shor's algorithm, Grover's algorithm, and Hogg's algorithms. We conclude with a discussion of quantum error correction

A unified evaluation of iterative projection algorithms for phase retrieval

Iterative projection algorithms are successfully being used as a substitute of lenses to recombine, numerically rather than optically, light scattered by illuminated objects. Images obtained computationally allow aberration-free diffraction-limited imaging and the possibility of using radiation for which no lenses exist. The challenge of this imaging technique is transfered from the lenses to the algorithms. We evaluate these new computational ``instruments'' developed for the phase retrieval problem, and discuss acceleration strategies.Comment: 12 pages, 9 figures, revte

Limits on the Dipole Moments of the τ\tau-Lepton via the Process $e^{+}e^{-}\to \tau^+ \tau^- \gamma in a Left-Right Symmetric Model

Limits on the anomalous magnetic moment and the electric dipole moment of the $\tau$ lepton are calculated through the reaction $e^{+}e^{-}\to \tau^+ \tau^- \gamma$ at the $Z_1$-pole and in the framework of a left-right symmetric model. The results are based on the recent data reported by the L3 Collaboration at CERN LEP. Due to the stringent limit of the model mixing angle $\phi$, the effect of this angle on the dipole moments is quite small.Comment: 15 pages, 3 figure

A Multi-Exchange Neighborhood Search Heuristic for an Integrated Clustering and Machine Setup Model for PCB Manufacturing

In the manufacture of printed circuit boards, electronic components are attached to a blank board by one or more pick-and-place machines. Frequent machine setups, though time consuming, can reduce overall processing time. We consider the Integrated Clustering and Machine Setup (ICMS) model, which incorporates this tradeoff between processing time and setup time and seeks to minimize the sum of the two. Solving this model to optimality is intractable for very large-scale instances. We show that ICMS is NP-hard and consequently propose and test a heuristic based on multi-exchange neighborhood search structures. Initial numerical results are very encouraging. Keywords: Printed circuit board assembly, feeder slot assignment, product clustering, integer programming, computational complexity, heuristics