197 research outputs found
Creating Responsive Information Systems with the Help of SSADM
In this paper, a program for a research is outlined. Firstly, the concept of responsive information systems is defined and then the notion of the capacity planning and software performance engineering is clarified. Secondly, the purpose of the proposed methodology of capacity planning, the interface to information systems analysis and development methodologies (SSADM), the advantage of knowledge-based approach is discussed. The interfaces to CASE tools more precisely to data dictionaries or repositories (IRDS) are examined in the context of a certain systems analysis and design methodology (e.g. SSADM)
Quantum natural gradient generalised to non-unitary circuits
Variational quantum circuits are promising tools whose efficacy depends on
their optimisation method. For noise-free unitary circuits, the quantum
generalisation of natural gradient descent was recently introduced. The method
can be shown to be equivalent to imaginary time evolution, and is highly
effective due to a metric tensor reconciling the classical parameter space to
the device's Hilbert space. Here we generalise quantum natural gradient to
consider arbitrary quantum states (both mixed and pure) via completely positive
maps; thus our circuits can incorporate both imperfect unitary gates and
fundamentally non-unitary operations such as measurements. Whereas the unitary
variant relates to classical Fisher information, here we find that quantum
Fisher information defines the core metric in the space of density operators.
Numerical simulations indicate that our approach can outperform other
variational techniques when circuit noise is present. We finally assess the
practical feasibility of our implementation and argue that its scalability is
only limited by the number and quality of imperfect gates and not by the number
of qubits.Comment: 20 pages, 6 figure
Quantum Analytic Descent
Variational algorithms have particular relevance for near-term quantum
computers but require non-trivial parameter optimisations. Here we propose
Analytic Descent: Given that the energy landscape must have a certain simple
form in the local region around any reference point, it can be efficiently
approximated in its entirety by a classical model -- we support these
observations with rigorous, complexity-theoretic arguments. One can classically
analyse this approximate function in order to directly `jump' to the
(estimated) minimum, before determining a more refined function if necessary.
We verify our technique using numerical simulations: each analytic jump can be
equivalent to many thousands of steps of canonical gradient descent.Comment: 14 pages, 4 figure
Probabilistic Interpolation of Quantum Rotation Angles
Quantum computing requires a universal set of gate operations; regarding
gates as rotations, any rotation angle must be possible. However a real device
may only be capable of bits of resolution, i.e. it might support only
possible variants of a given physical gate. Naive discretization of an
algorithm's gates to the nearest available options causes coherent errors,
while decomposing an impermissible gate into several allowed operations
increases circuit depth. Conversely, demanding higher can greatly
complexify hardware. Here we explore an alternative: Probabilistic Angle
Interpolation (PAI). This effectively implements any desired, continuously
parametrised rotation by randomly choosing one of three discretised gate
settings and postprocessing individual circuit outputs. The approach is
particularly relevant for near-term applications where one would in any case
average over many runs of circuit executions to estimate expected values. While
PAI increases that sampling cost, we prove that a) the approach is optimal in
the sense that PAI achieves the least possible overhead and c) the overhead is
remarkably modest even with thousands of parametrised gates and only bits
of resolution available. This is a profound relaxation of engineering
requirements for first generation quantum computers where even bits of
resolution may suffice and, as we demonstrate, the approach is many orders of
magnitude more efficient than prior techniques. Moreover we conclude that, even
for more mature late-NISQ hardware, no more than bits will be necessary.Comment: 15 pages, 5 figures -- includes proof of optimality of protocol,
generalisation to non-uniform settings et
Synthesis of Indole-Coupled KYNA Derivatives via C–N Bond Cleavage of Mannich Bases
KYNAs, a compound with endogenous neuroprotective functions and an indole that is a building block of many biologically active compounds, such as a variety of neurotransmitters, are reacted in a transformation building upon Mannich bases. The reaction yields triarylmethane derivatives containing two biologically potent skeletons, and it may contribute to the synthesis of new, specialised neuroprotective compounds. The synthesis has been investigated via two procedures and the results were compared to those of previous studies. A possible alternative reaction route through acid catalysis has been established
Alkoxyalkylation of Electron-Rich Aromatic Compounds
Alkoxyalkylation and hydroxyalkylation methods utilizing oxo-compound derivatives such as aldehydes, acetals or acetylenes and various alcohols or water are widely used tools in preparative organic chemistry to synthesize bioactive compounds, biosensors, supramolecular compounds and petrochemicals. The syntheses of such molecules of broad relevance are facilitated by acid, base or heterogenous catalysis. However, degradation of the N-analogous Mannich bases are reported to yield alkoxyalkyl derivatives via the retro-Mannich reaction. The mutual derivative of all mentioned species are quinone methides, which are reported to form under both alkoxy- and aminoalkylative conditions and via the degradation of the Mannich-products. The aim of this review is to summarize the alkoxyalkylation (most commonly alkoxymethylation) of electron-rich arenes sorted by the methods of alkoxyalkylation (direct or via retro-Mannich reaction) and the substrate arenes, such as phenolic and derived carbocycles, heterocycles and the widely examined indole derivatives
Distributed Simulation of Statevectors and Density Matrices
Classical simulation of quantum computers is an irreplaceable step in the
design of quantum algorithms. Exponential simulation costs demand the use of
high-performance computing techniques, and in particular distribution, whereby
the quantum state description is partitioned between a network of cooperating
computers - necessary for the exact simulation of more than approximately 30
qubits. Distributed computing is notoriously difficult, requiring bespoke
algorithms dissimilar to their serial counterparts with different resource
considerations, and which appear to restrict the utilities of a quantum
simulator. This manuscript presents a plethora of novel algorithms for
distributed full-state simulation of gates, operators, noise channels and other
calculations in digital quantum computers. We show how a simple, common but
seemingly restrictive distribution model actually permits a rich set of
advanced facilities including Pauli gadgets, many-controlled many-target
general unitaries, density matrices, general decoherence channels, and partial
traces. These algorithms include asymptotically, polynomially improved
simulations of exotic gates, and thorough motivations for high-performance
computing techniques which will be useful for even non-distributed simulators.
Our results are derived in language familiar to a quantum information theory
audience, and our algorithms formalised for the scientific simulation
community. We have implemented all algorithms herein presented into an
isolated, minimalist C++ project, hosted open-source on Github with a
permissive MIT license, and extensive testing. This manuscript aims both to
significantly improve the high-performance quantum simulation tools available,
and offer a thorough introduction to, and derivation of, full-state simulation
techniques.Comment: 56 pages, 18 figures, 28 algorithms, 1 tabl
A word of caution about biological inference - Revisiting cysteine covalent state predictions
The success of methods for predicting the redox state of cysteine residues from the sequence environment
seemed to validate the basic assumption that this state is mainly determined locally. However,
the accuracy of predictions on randomized sequences or of non-cysteine residues remained
high, suggesting that these predictions rather capture global features of proteins such as subcellular
localization, which depends on composition. This illustrates that even high prediction accuracy is
insufficient to validate implicit assumptions about a biological phenomenon. Correctly identifying
the relevant underlying biochemical reasons for the success of a method is essential to gain proper
biological insights and develop more accurate and novel bioinformatics tools.
2014 The Authors. Published by Elsevier B.V. on behalf of the Federation of European Biochemical Societies. This
is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Systematic analysis of somatic mutations driving cancer: Uncovering functional protein regions in disease development
Background: Recent advances in sequencing technologies enable the large-scale identification of genes that are affected by various genetic alterations in cancer. However, understanding tumor development requires insights into how these changes cause altered protein function and impaired network regulation in general and/or in specific cancer types. Results: In this work we present a novel method called iSiMPRe that identifies regions that are significantly enriched in somatic mutations and short in-frame insertions or deletions (indels). Applying this unbiased method to the complete human proteome, by using data enriched through various cancer genome projects, we identified around 500 protein regions which could be linked to one or more of 27 distinct cancer types. These regions covered the majority of known cancer genes, surprisingly even tumor suppressors. Additionally, iSiMPRe also identified novel genes and regions that have not yet been associated with cancer. Conclusions: While local somatic mutations correspond to only a subset of genetic variations that can lead to cancer, our systematic analyses revealed that they represent an accompanying feature of most cancer driver genes regardless of the primary mechanism by which they are perturbed during tumorigenesis. These results indicate that the accumulation of local somatic mutations can be used to pinpoint genes responsible for cancer formation and can also help to understand the effect of cancer mutations at the level of functional modules in a broad range of cancer driver genes. Reviewers: This article was reviewed by Sándor Pongor, Michael Gromiha and Zoltán Gáspári. © 2016 Mészáros et al
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