A Structured Method for Compilation of QAOA Circuits in Quantum Computing

Quantum Approximation Optimization Algorithm (QAOA) is a highly advocated variational algorithm for solving the combinatorial optimization problem. One critical feature in the quantum circuit of QAOA algorithm is that it consists of two-qubit operators that commute. The flexibility in reordering the two-qubit gates allows compiler optimizations to generate circuits with better depths, gate count, and fidelity. However, it also imposes significant challenges due to additional freedom exposed in the compilation. Prior studies lack the following: (1) Performance guarantee, (2) Scalability, and (3) Awareness of regularity in scalable hardware. We propose a structured method that ensures linear depth for any compiled QAOA circuit on multi-dimensional quantum architectures. We also demonstrate how our method runs on Google Sycamore and IBM Non-linear architectures in a scalable manner and in linear time. Overall, we can compile a circuit with up to 1024 qubits in 10 seconds with a 3.8X speedup in depth, 17% reduction in gate count, and 18X improvement for circuit ESP.Comment: 11 pages, 22 figure

A human factors methodology for real-time support applications

A general approach to the human factors (HF) analysis of new or existing projects at NASA/Goddard is delineated. Because the methodology evolved from HF evaluations of the Mission Planning Terminal (MPT) and the Earth Radiation Budget Satellite Mission Operations Room (ERBS MOR), it is directed specifically to the HF analysis of real-time support applications. Major topics included for discussion are the process of establishing a working relationship between the Human Factors Group (HFG) and the project, orientation of HF analysts to the project, human factors analysis and review, and coordination with major cycles of system development. Sub-topics include specific areas for analysis and appropriate HF tools. Management support functions are outlined. References provide a guide to sources of further information

Characterization and Benchmarking of Quantum Computers

Quantum computers are a promising technology expected to provide substantial speedups to important computational problems, but modern quantum devices are imperfect and prone to noise. In order to program and debug quantum computers as well as monitor progress towards more advanced devices, we must characterize their dynamics and benchmark their performance. Characterization methods vary in measured quantities and computational requirements, and their accuracy in describing arbitrary quantum devices in an arbitrary context is not guaranteed. The leading techniques for characterization are based on fine-grain physical models that are typically accurate but computationally expensive. This raises the question of how to extend characterization efficiently to larger scales. We present an empirical-based approach to direct characterization of quantum circuits that reconciles accuracy with scalability by using a reduced set of test circuits that target a chosen application and coarse-graining the noise modeling process to reduce the model complexity. We show that this method performs well in tests with Greenberger-Horne-Zeilinger-state preparation circuits and the Bernstein-Vazirani algorithm, though it does not describe all error present in the system. We benchmark this method with the leading methods of gate set tomography, cycle benchmarking, and Pauli channel noise reconstruction to characterize quantum circuits and we compare the accuracy of these methods in predicting quantum device behavior. We find that our method for empirical direct characterization offers competitive accuracy when compared with finer-grained techniques, while significantly reducing the resources required for characterization. By testing on quantum devices, we quantify the quantum and classical resources required for each characterization method and we monitor the decrease in accuracy as a function of circuit size. We find that these characterization methods can provide an accurate estimate of a quantum computer\u27s performance on a benchmark but the best-performing method varied by test. Our results indicate that these characterization methods perform well in describing the noise of a quantum computer but their performance depends on the size and the context of the application

The Current Utilization of Graphic Data Processing in Industry and Education with Implications for Industrial Arts

A thesis presented to the faculty of the School of Education at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Arts in Education by Chester Steven Rzonca in May of 1967

Spartan Daily, March 4, 1963

Volume 50, Issue 79https://scholarworks.sjsu.edu/spartandaily/4426/thumbnail.jp

Annotated Bibliography of Films in Automation, Data Processing, and Computer Science

With the rapid development of computer science and the expanding use of computers in all facets of American life, there has been made available a wide range of instructional and informational films on automation, data processing, and computer science. Here is the first annotated bibliography of these and related films, gathered from industrial, institutional, and other sources. This bibliography annotates 244 films, alphabetically arranged by title, with a detailed subject index. Information is also provided concerning the intended audience, rental-purchase data, ordering procedures, and such specifications as running time and film size.https://uknowledge.uky.edu/upk_computer_science/1000/thumbnail.jp

Volume 66 - Issue 7 - April, 1955

https://scholar.rose-hulman.edu/technic/1091/thumbnail.jp

Computer Graphics. Volume 2 - an Annotated Bibliography to the NASA-MSFC Digital Computer Graphics Program

Annotated bibliography on digital computer graphic