Multistart Methods for Quantum Approximate Optimization

Hybrid quantum-classical algorithms such as the quantum approximate optimization algorithm (QAOA) are considered one of the most promising approaches for leveraging near-term quantum computers for practical applications. Such algorithms are often implemented in a variational form, combining classical optimization methods with a quantum machine to find parameters to maximize performance. The quality of the QAOA solution depends heavily on quality of the parameters produced by the classical optimizer. Moreover, the presence of multiple local optima in the space of parameters makes it harder for the classical optimizer. In this paper we study the use of a multistart optimization approach within a QAOA framework to improve the performance of quantum machines on important graph clustering problems. We also demonstrate that reusing the optimal parameters from similar problems can improve the performance of classical optimization methods, expanding on similar results for MAXCUT

Adaptive Neural Network Controller for ATM Traffic

Broadband-Integrated Services Digital Networks (B-ISDN), along with Asynchronous Transfer Mode (ATM), were designed to meet the requirements of modern communication networks to handle multiple users and a wide variety of diverse traffic including voice, data and video. ATM responds to requests for admission to the network by analyzing whether or not the grade of service (GOS) requirement, specified in the admission request, can be guaranteed without violating the GOS guaranteed to traffic already accepted into the network. The GOS is typically a parameter such as cell loss rate (CLR), average delay, or some other measurement associated with network performance. In order to develop a tractable mathematical algorithm for controlling admission, an accurate model of the communication network and traffic in question is necessary. The complex and dynamic nature of these communication networks make them very difficult to model. Even when such a model can be developed, often with unrealistic simplifications or unsupportable assumptions, the associated mathematical algorithm is frequently excessively cumbersome and timely processing of an admission request is lost. An alternative to conventional mathematical algorithms for cases like these is the use of neural networks (NN). NNs can learn complicated functions relating the inputs and outputs of a system without prior knowledge about the system itself. For ATM B-ISDN networks, NNs can learn the function relating input traffic parameters and resulting network performance by training on an appropriate set of traffic parameter inputs and resulting GOS outputs. In this work three neural network admission controller schemes are examined. The Bayes error rate, as bounded by the Parzen window technique, is also introduced as a benchmark for measuring the performance of these admission controllers

ECONOMIC EVALUATION OF INCOME PROTECTION CHOICES FOR WEST TENNESSEE CORN PRODUCERS

Farmers need information about the expected value and variability of net revenues for alternative crop insurance and futures hedging strategies to manage risk. Specifically, the model will determine which risk management strategies are most desirable under various levels of risk aversion. The unstable futures basis relation in the data used in the simulation model contributed to increased variability of net revenues. In general, none of the crop insurance or hedging strategies markedly reduced variability of net revenue and relative riskiness when compared with the cash strategy. Revenue Assurance strategies were the most effective at setting a floor on net revenues. As a result, Revenue Assurance products may perform well for extremely risk averse producers.Marketing, Risk and Uncertainty,

Determination of lunar ilmenite abundances from remotely sensed data

The mineral ilmenite (FeTiO3) was found in abundance in lunar mare soils returned during the Apollo project. Lunar ilmenite often contains greater than 50 weight-percent titanium dioxide (TiO2), and is a primary potential resource for oxygen and other raw materials to supply future lunar bases. Chemical and spectroscopic analysis of the returned lunar soils produced an empirical function that relates the spectral reflectance ratio at 400 and 560 nm to the weight percent abundance of TiO2. This allowed mapping of the lunar TiO2 distribution using telescopic vidicon multispectral imaging from the ground; however, the time variant photometric response of the vidicon detectors produced abundance uncertainties of at least 2 to 5 percent. Since that time, solid-state charge-coupled device (CCD) detector technology capable of much improved photometric response has become available. An investigation of the lunar TiO2 distribution was carried out utilizing groundbased telescopic CCD multispectral imagery and spectroscopy. The work was approached in phases to develop optimum technique based upon initial results. The goal is to achieve the best possible TiO2 abundance maps from the ground as a precursor to lunar orbiter and robotic sample return missions, and to produce a better idea of the peak abundances of TiO2 for benefaction studies. These phases and the results are summarized