Generating and using truly random quantum states in Mathematica

The problem of generating random quantum states is of a great interest from the quantum information theory point of view. In this paper we present a package for Mathematica computing system harnessing a specific piece of hardware, namely Quantis quantum random number generator (QRNG), for investigating statistical properties of quantum states. The described package implements a number of functions for generating random states, which use Quantis QRNG as a source of randomness. It also provides procedures which can be used in simulations not related directly to quantum information processing.Comment: 12 pages, 3 figures, see http://www.iitis.pl/~miszczak/trqs.html for related softwar

Parallel Simulation of Quantum Search

Simulation of quantum computers using classical computers is a computationally hard problem, requiring a huge amount of operations and storage. Parallelization can alleviate this problem, allowing the simulation of more qubits at the same time or the same number of qubits to be simulated in less time. A promising approach is represented by executing these simulators in Grid systems that can provide access to high performance resources. In this paper we present a parallel implementation of the QC-lib quantum computer simulator deployed as a Grid service. Using a specific scheme for partitioning the terms describing quantum states and efficient parallelization of the general singe qubit operator and of the controlled operators, very good speed-ups were obtained for the simulation of the quantum search problem

Qcmpi: A Parallel Environment for Quantum Computing

QCMPI is a quantum computer (QC) simulation package written in Fortran 90 with parallel processing capabilities. It is an accessible research tool that permits rapid evaluation of quantum algorithms for a large number of qubits and for various "noise" scenarios. The prime motivation for developing QCMPI is to facilitate numerical examination of not only how QC algorithms work, but also to include noise, decoherence, and attenuation effects and to evaluate the efficacy of error correction schemes. The present work builds on an earlier Mathematica code QDENSITY, which is mainly a pedagogic tool. In QCMPI, the stress is on state vectors, in order to employ a large number of qubits. The parallel processing feature is implemented by using the Message-Passing Interface (MPI) protocol. Codes for Grover's search and Shor's factoring algorithms are provided as examples. A major feature of this work is that concurrent versions of the algorithms can be evaluated with each version subject to alternate noise effects, which corresponds to the idea of solving a stochastic Schr\"{o}dinger equation.Comment: Package webpage http://www.pitt.edu/~tabakin/QCMP

Dynamic Trace Analysis with Zero-Suppressed BDDs

Instruction level parallelism (ILP) limitations have forced processor manufacturers to develop multi-core platforms with the expectation that programs will be able to exploit thread level parallelism (TLP). Multi-core programming shifts the burden of locating additional performance away from computer hardware to the software developers, who often attempt high-level redesigns focused on exposing thread level parallelism, as well as explore aggressive optimizations for sequential codes. Precise dynamic analysis can provide useful guidance for program optimization efforts, including efforts to find and extract thread level parallelism. Unfortunately, finding regions of code amenable to further optimization efforts requires analyzing traces that can quickly grow in size. Analysis of large dynamic traces (e.g. one billion instructions or more) is often impractical for commodity hardware. An ideal representation for dynamic trace data would provide compression. However, decompressing large software traces, even if decompressed data is never permanently stored, would make many analysis impractical. A better solution would allow analysis of the compressed data, without a costly decompression step. Prior works have developed trace compressors that generate an analyzable representation, but often limit the precision or scope of analyses. Zero-suppressed binary decision diagram (ZDDs) exhibit many of the desired properties of an ideal trace representation. This thesis shows: (1) dynamic trace data may be represented by zero-suppressed binary decision diagrams (ZDDs); (2) ZDDs allow many analyses to scale; (3) encoding traces as ZDDs can be performed in a reasonable amount of time; and, (4) ZDD-based analyses, such as irrelevant instruction detection and potential coarse-grained thread level parallelism extraction, can reveal a number of performanc

Memorias matriciales correlacionadas cuánticas, simples y mejoradas: una propuesta para su estudio y simulación sobre GPGPU

En este trabajo se desarrollan-en orden-los fundamentos de la Física Cuántica, y de la Computación Cuántica, una noción completa de las arquitecturas multicapa tolerante a fallos para la implementación física de una computadora cuántica, para completar los primeros cuatro capítulos con las técnicas propias para la simulación de este nuevo paradigma sobre placas multicore del tipo General-Purpose Computing on Graphics Processing Units (GPGPU). La segunda parte de este trabajo consiste en los tres capítulos inmediatamente siguientes, los cuales suman 10 innovaciones en este campo, a saber: 1. el Proceso de Ortogonalización Booleano (POB) con su inversa, 2. el Proceso de Ortogonalización de Gram-Schmidt Mejorado (POGSMe) con su inversa, 3. el Proceso de Ortogonalización Cuántico (POCu) con su inversa, 4. la Red Ortogonalizadora Booleana Sistólica (ROBS), 5. la Red Ortogonalizadora Cuántica Sistólica (ROCS), y 6. una métrica que llamamos Tasa Dimensional de Entrada-Salida (TDES) la cual fue creada para monitorear el impacto del mejorador para la estabilidad del Proceso Ortogonalizador de Gram-Schmidt en el costo computacional final. 7. una versión mejorada de las ya conocidas Memorias Matriciales Correlacionadas Booleanas (MMCB), es decir, la MMCB mejorada (MMCBMe) en base al innovador Proceso de Ortonormalización Booleano (POB) del Capítulo 5, 8. la Memoria Matricial Correlacionada Cuántica (MMCCu), y 9. la MMCCu Mejorada (MMCCuMe) en base al Proceso de Ortogonalización Cuántico (POCu) implementado en forma sistólica y conocida como la Red Ortogonalizadora Cuántica Sistólica (ROCS) del Capítulo 5.10. el Capítulo 7, el cual contiene las simulaciones computacionales, las cuales verifican fehacientemente la mejora en la performance de almacenamiento como resultado de aplicar el POCu a las MMCCu, así como una serie de simulaciones relativas a arreglos uni, bi y tridimensionales, los cuales representan señales, imágenes (multimediales, documentales, satelitales, biométricas, etc.) y video o bien imágenes multi e hiper-espectrales satelitales, tomografías o resonancias magnéticas seriadas, respectivamente. Dichas simulaciones tienen por objeto verificar los atributos de ortogonalización de los algoritmos desarrollados. Dado que es la primera vez que en la literatura se realizan este tipo de simulaciones en GPGPU para esta tecnología, el Capítulo 7 representa en si mismo el décimo aporte del presente trabajo a esta área del conocimiento. Un último capítulo reservado a conclusiones parciales por capítulo y generales del trabajo como un todo.Facultad de Informátic