3,538 research outputs found
Qubit Data Structures for Analyzing Computing Systems
Qubit models and methods for improving the performance of software and
hardware for analyzing digital devices through increasing the dimension of the
data structures and memory are proposed. The basic concepts, terminology and
definitions necessary for the implementation of quantum computing when
analyzing virtual computers are introduced. The investigation results
concerning design and modeling computer systems in a cyberspace based on the
use of two-component structure are presented.Comment: 9 pages,4 figures, Proceeding of the Third International Conference
on Data Mining & Knowledge Management Process (CDKP 2014
Arithmetic on a Distributed-Memory Quantum Multicomputer
We evaluate the performance of quantum arithmetic algorithms run on a
distributed quantum computer (a quantum multicomputer). We vary the node
capacity and I/O capabilities, and the network topology. The tradeoff of
choosing between gates executed remotely, through ``teleported gates'' on
entangled pairs of qubits (telegate), versus exchanging the relevant qubits via
quantum teleportation, then executing the algorithm using local gates
(teledata), is examined. We show that the teledata approach performs better,
and that carry-ripple adders perform well when the teleportation block is
decomposed so that the key quantum operations can be parallelized. A node size
of only a few logical qubits performs adequately provided that the nodes have
two transceiver qubits. A linear network topology performs acceptably for a
broad range of system sizes and performance parameters. We therefore recommend
pursuing small, high-I/O bandwidth nodes and a simple network. Such a machine
will run Shor's algorithm for factoring large numbers efficiently.Comment: 24 pages, 10 figures, ACM transactions format. Extended version of
Int. Symp. on Comp. Architecture (ISCA) paper; v2, correct one circuit error,
numerous small changes for clarity, add reference
On the Effect of Quantum Interaction Distance on Quantum Addition Circuits
We investigate the theoretical limits of the effect of the quantum
interaction distance on the speed of exact quantum addition circuits. For this
study, we exploit graph embedding for quantum circuit analysis. We study a
logical mapping of qubits and gates of any -depth quantum adder
circuit for two -qubit registers onto a practical architecture, which limits
interaction distance to the nearest neighbors only and supports only one- and
two-qubit logical gates. Unfortunately, on the chosen -dimensional practical
architecture, we prove that the depth lower bound of any exact quantum addition
circuits is no longer , but . This
result, the first application of graph embedding to quantum circuits and
devices, provides a new tool for compiler development, emphasizes the impact of
quantum computer architecture on performance, and acts as a cautionary note
when evaluating the time performance of quantum algorithms.Comment: accepted for ACM Journal on Emerging Technologies in Computing
System
Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes
In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10
Topological code Autotune
Many quantum systems are being investigated in the hope of building a
large-scale quantum computer. All of these systems suffer from decoherence,
resulting in errors during the execution of quantum gates. Quantum error
correction enables reliable quantum computation given unreliable hardware.
Unoptimized topological quantum error correction (TQEC), while still effective,
performs very suboptimally, especially at low error rates. Hand optimizing the
classical processing associated with a TQEC scheme for a specific system to
achieve better error tolerance can be extremely laborious. We describe a tool
Autotune capable of performing this optimization automatically, and give two
highly distinct examples of its use and extreme outperformance of unoptimized
TQEC. Autotune is designed to facilitate the precise study of real hardware
running TQEC with every quantum gate having a realistic, physics-based error
model.Comment: 13 pages, 17 figures, version accepted for publicatio
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