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

What is a quantum computer, and how do we build one?

The DiVincenzo criteria for implementing a quantum computer have been seminal in focussing both experimental and theoretical research in quantum information processing. These criteria were formulated specifically for the circuit model of quantum computing. However, several new models for quantum computing (paradigms) have been proposed that do not seem to fit the criteria well. The question is therefore what are the general criteria for implementing quantum computers. To this end, a formal operational definition of a quantum computer is introduced. It is then shown that according to this definition a device is a quantum computer if it obeys the following four criteria: Any quantum computer must (1) have a quantum memory; (2) facilitate a controlled quantum evolution of the quantum memory; (3) include a method for cooling the quantum memory; and (4) provide a readout mechanism for subsets of the quantum memory. The criteria are met when the device is scalable and operates fault-tolerantly. We discuss various existing quantum computing paradigms, and how they fit within this framework. Finally, we lay out a roadmap for selecting an avenue towards building a quantum computer. This is summarized in a decision tree intended to help experimentalists determine the most natural paradigm given a particular physical implementation

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

Äärelliset tilamallit lukupuheen tunnistamisessa ja tarkastamisessa

An automatic speech recognition system has to combine acoustic and linguistic information. Therefore the search space spans multiple layers. Finite state models and weighted finite state transducers in particular can efficiently represent this search space by modeling each layer as a transducer and combining them using generic weighted finite state transducer algorithms. When recognising a text prompt being read aloud, the prompt gives a good estimate of what is going to be said. However human reading naturally produces some deviations from the text, called miscues. The purpose of this thesis is to create a system which accurately recognises recordings of reading. A miscue tolerant finite state language model is implemented and compared against two traditional approaches, an N-gram model and forced alignment. The recognition result will ultimately be used to validate the recording as fit for further automatic processing in a spoken foreign language exam, which Project DigiTala is designing for the Finnish matriculation examination. The computerization of the matriculation examination in Finland makes the use of such automatic tools possible. This thesis first introduces the context for the task of recognising and validating reading. Then it explores three methodologies needed to solve the task: automatic speech recognition, finite state models, and the modeling of reading. Next it recounts the implementation of the miscue tolerant finite state language models and the two baseline methods. After that it describes experiments which show that the miscue tolerant finite state language models solve the task of this thesis significantly better than the baseline methods. Finally the thesis concludes with a discussion of the results and future work.Automaattinen puheentunnistusjärjestelmä yhdistää akustista ja kielellistä tietoa, joten sen hakuavaruus on monitasoinen. Tämän hakuavaruuden voi esittää tehokkaasti äärellisillä tilamalleilla. Erityisesti painotetut äärelliset tilamuuttajat voivat esittää jokaista hakuavaruuden tasoa ja nämä muuttajat voidaan yhdistää yleisillä muuttaja-algoritmeilla. Kun tunnistetaan ääneen lukemista syötteestä, syöte rajaa hakuavaruutta hyvin. Ihmiset kuitenkin poikkeavat tekstistä hieman. Kutsun näitä lukupoikkeamiksi, koska ne ovat luonnollinen osa taitavaakin lukemista, eivätkä siis suoranaisesti lukuvirheitä. Tämän diplomityön tavoite on luoda järjestelmä, joka tunnistaa lukupuheäänitteitä tarkasti. Tätä varten toteutetaan lukupoikkeamia sietävä äärellisen tilan kielimalli, jota verrataan kahteen perinteiseen menetelmään, N-gram malleihin ja pakotettuun kohdistukseen. Lukupuheen tunnistustulosta käytetään, kun tarkastetaan, sopiiko äänite seuraaviin automaattisiin käsittelyvaiheisiin puhutussa vieraan kielen kokeessa. DigiTalaprojekti muotoilee puhuttua osiota vieraan kielen ylioppilaskokeisiin. Ylioppilaskokeiden sähköistäminen mahdollistaa tällaisten automaattisten menetelmien käytön. Kokeet sekä englanninkielisellä simuloidulla aineistolla että ruotsinkielisellä tosimaailman aineistolla osoittavat, että lukupoikkeamia sietävä äärellisen tilan kielimalli ratkaisee diplomityön ongelmanasettelun. Vaikealla tosimaailman aineistolla saadaan 3.77 ± 0.47 prosentuaalinen sanavirhemäärä