2,092 research outputs found
Programming Quantum Computers Using Design Automation
Recent developments in quantum hardware indicate that systems featuring more
than 50 physical qubits are within reach. At this scale, classical simulation
will no longer be feasible and there is a possibility that such quantum devices
may outperform even classical supercomputers at certain tasks. With the rapid
growth of qubit numbers and coherence times comes the increasingly difficult
challenge of quantum program compilation. This entails the translation of a
high-level description of a quantum algorithm to hardware-specific low-level
operations which can be carried out by the quantum device. Some parts of the
calculation may still be performed manually due to the lack of efficient
methods. This, in turn, may lead to a design gap, which will prevent the
programming of a quantum computer. In this paper, we discuss the challenges in
fully-automatic quantum compilation. We motivate directions for future research
to tackle these challenges. Yet, with the algorithms and approaches that exist
today, we demonstrate how to automatically perform the quantum programming flow
from algorithm to a physical quantum computer for a simple algorithmic
benchmark, namely the hidden shift problem. We present and use two tool flows
which invoke RevKit. One which is based on ProjectQ and which targets the IBM
Quantum Experience or a local simulator, and one which is based on Microsoft's
quantum programming language Q.Comment: 10 pages, 10 figures. To appear in: Proceedings of Design, Automation
and Test in Europe (DATE 2018
Computational self-assembly
International audienceThe object of this paper is to appreciate the computational limits inherent in the combinatorics of an applied concurrent (aka agent-based) language kappa. That language is primarily meant as a visual and concise notation for biological signalling pathways. Descriptions in kappa, when enriched with suitable kinetic information, generate simulations as continuous time Markov chains. However, kappa can be studied independently of the intended application, in a purely computational fashion, and this is what we are doing here. Specifically, we define a compilation of kappa into a language where interactions can involve at most two agents at a time. That compilation is generic, the blow up in the number of rules is linear in the total rule set size, and the methodology used in deriving the compilation relies on an implicit causality analysis. The correctness proof is given in details, and correctness is spelt out in terms of the existence of a specific weak bisimulation. To compensate for the binary restriction, one allows components to create unique identifiers (aka names). An interesting by-product of the analysis is that when using acyclic rules, one sees that name creation is not needed, and can be fully reduced to binary form
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Quantum Stochastic Processes and Quantum Many-Body Physics
This dissertation investigates the theory of quantum stochastic processes and its applications in quantum many-body physics.
The main goal is to analyse complexity-theoretic aspects of both static and dynamic properties of physical systems modelled by quantum stochastic processes.
The thesis consists of two parts: the first one addresses the computational complexity of certain quantum and classical divisibility questions, whereas the second one addresses the topic of Hamiltonian complexity theory.
In the divisibility part, we discuss the question whether one can efficiently sub-divide a map describing the evolution of a system in a noisy environment, i.e. a CPTP- or stochastic map for quantum and classical processes, respectively, and we prove that taking the nth root of a CPTP or stochastic map is an NP-complete problem.
Furthermore, we show that answering the question whether one can divide up a random variable into a sum of iid random variables , i.e. , is poly-time computable; relaxing the iid condition renders the problem NP-hard.
In the local Hamiltonian part, we study computation embedded into the ground state of a many-body quantum system, going beyond "history state" constructions with a linear clock.
We first develop a series of mathematical techniques which allow us to study the energy spectrum of the resulting Hamiltonian, and extend classical string rewriting to the quantum setting.
This allows us to construct the most physically-realistic QMAEXP-complete instances for the LOCAL HAMILTONIAN problem (i.e. the question of estimating the ground state energy of a quantum many-body system) known to date, both in one- and three dimensions.
Furthermore, we study weighted versions of linear history state constructions, allowing us to obtain tight lower and upper bounds on the promise gap of the LOCAL HAMILTONIAN problem in various cases.
We finally study a classical embedding of a Busy Beaver Turing Machine into a low-dimensional lattice spin model, which allows us to dictate a transition from a purely classical phase to a Toric Code phase at arbitrarily large and potentially even uncomputable system sizes
Towards dynamical network biomarkers in neuromodulation of episodic migraine
Computational methods have complemented experimental and clinical
neursciences and led to improvements in our understanding of the nervous
systems in health and disease. In parallel, neuromodulation in form of electric
and magnetic stimulation is gaining increasing acceptance in chronic and
intractable diseases. In this paper, we firstly explore the relevant state of
the art in fusion of both developments towards translational computational
neuroscience. Then, we propose a strategy to employ the new theoretical concept
of dynamical network biomarkers (DNB) in episodic manifestations of chronic
disorders. In particular, as a first example, we introduce the use of
computational models in migraine and illustrate on the basis of this example
the potential of DNB as early-warning signals for neuromodulation in episodic
migraine.Comment: 13 pages, 5 figure
High capacity data embedding schemes for digital media
High capacity image data hiding methods and robust high capacity digital audio watermarking algorithms are studied in this thesis. The main results of this work are the development of novel algorithms with state-of-the-art performance, high capacity and transparency for image data hiding and robustness, high capacity and low distortion for audio watermarking.En esta tesis se estudian y proponen diversos métodos de data hiding de imágenes y watermarking de audio de alta capacidad. Los principales resultados de este trabajo consisten en la publicación de varios algoritmos novedosos con rendimiento a la altura de los mejores métodos del estado del arte, alta capacidad y transparencia, en el caso de data hiding de imágenes, y robustez, alta capacidad y baja distorsión para el watermarking de audio.En aquesta tesi s'estudien i es proposen diversos mètodes de data hiding d'imatges i watermarking d'àudio d'alta capacitat. Els resultats principals d'aquest treball consisteixen en la publicació de diversos algorismes nous amb rendiment a l'alçada dels millors mètodes de l'estat de l'art, alta capacitat i transparència, en el cas de data hiding d'imatges, i robustesa, alta capacitat i baixa distorsió per al watermarking d'àudio.Societat de la informació i el coneixemen
Quantum Computing for Molecular Biology
Molecular biology and biochemistry interpret microscopic processes in the
living world in terms of molecular structures and their interactions, which are
quantum mechanical by their very nature. Whereas the theoretical foundations of
these interactions are very well established, the computational solution of the
relevant quantum mechanical equations is very hard. However, much of molecular
function in biology can be understood in terms of classical mechanics, where
the interactions of electrons and nuclei have been mapped onto effective
classical surrogate potentials that model the interaction of atoms or even
larger entities. The simple mathematical structure of these potentials offers
huge computational advantages; however, this comes at the cost that all quantum
correlations and the rigorous many-particle nature of the interactions are
omitted. In this work, we discuss how quantum computation may advance the
practical usefulness of the quantum foundations of molecular biology by
offering computational advantages for simulations of biomolecules. We not only
discuss typical quantum mechanical problems of the electronic structure of
biomolecules in this context, but also consider the dominating classical
problems (such as protein folding and drug design) as well as data-driven
approaches of bioinformatics and the degree to which they might become amenable
to quantum simulation and quantum computation.Comment: 76 pages, 7 figure
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