1,665,611 research outputs found

    Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics

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    Quantum computing is powerful because unitary operators describing the time-evolution of a quantum system have exponential size in terms of the number of qubits present in the system. We develop a new "Singular value transformation" algorithm capable of harnessing this exponential advantage, that can apply polynomial transformations to the singular values of a block of a unitary, generalizing the optimal Hamiltonian simulation results of Low and Chuang. The proposed quantum circuits have a very simple structure, often give rise to optimal algorithms and have appealing constant factors, while usually only use a constant number of ancilla qubits. We show that singular value transformation leads to novel algorithms. We give an efficient solution to a certain "non-commutative" measurement problem and propose a new method for singular value estimation. We also show how to exponentially improve the complexity of implementing fractional queries to unitaries with a gapped spectrum. Finally, as a quantum machine learning application we show how to efficiently implement principal component regression. "Singular value transformation" is conceptually simple and efficient, and leads to a unified framework of quantum algorithms incorporating a variety of quantum speed-ups. We illustrate this by showing how it generalizes a number of prominent quantum algorithms, including: optimal Hamiltonian simulation, implementing the Moore-Penrose pseudoinverse with exponential precision, fixed-point amplitude amplification, robust oblivious amplitude amplification, fast QMA amplification, fast quantum OR lemma, certain quantum walk results and several quantum machine learning algorithms. In order to exploit the strengths of the presented method it is useful to know its limitations too, therefore we also prove a lower bound on the efficiency of singular value transformation, which often gives optimal bounds.Comment: 67 pages, 1 figur

    Quantum Theory of Probability and Decisions

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    The probabilistic predictions of quantum theory are conventionally obtained from a special probabilistic axiom. But that is unnecessary because all the practical consequences of such predictions follow from the remaining, non-probabilistic, axioms of quantum theory, together with the non-probabilistic part of classical decision theory

    On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking

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    Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking environments is to dynamically exploit existing complementarities and synergies among tasks, helping each other through the transfer of genetic material. More concretely, Evolutionary Multitasking (EM) regards to the resolution of multitasking scenarios using concepts inherited from Evolutionary Computation. EM approaches such as the well-known Multifactorial Evolutionary Algorithm (MFEA) are lately gaining a notable research momentum when facing with multiple optimization problems. This work is focused on the application of the recently proposed Multifactorial Cellular Genetic Algorithm (MFCGA) to the well-known Capacitated Vehicle Routing Problem (CVRP). In overall, 11 different multitasking setups have been built using 12 datasets. The contribution of this research is twofold. On the one hand, it is the first application of the MFCGA to the Vehicle Routing Problem family of problems. On the other hand, equally interesting is the second contribution, which is focused on the quantitative analysis of the positive genetic transferability among the problem instances. To do that, we provide an empirical demonstration of the synergies arisen between the different optimization tasks.Comment: 8 pages, 1 figure, paper accepted for presentation in the 23rd IEEE International Conference on Intelligent Transportation Systems 2020 (IEEE ITSC 2020

    Quantumlike Chaos in the Frequency Distributions of the Bases A, C, G, T in Drosophila DNA

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    Continuous periodogram power spectral analyses of fractal fluctuations of frequency distributions of bases A, C, G, T in Drosophila DNA show that the power spectra follow the universal inverse power-law form of the statistical normal distribution. Inverse power-law form for power spectra of space-time fluctuations is generic to dynamical systems in nature and is identified as self-organized criticality. The author has developed a general systems theory, which provides universal quantification for observed self-organized criticality in terms of the statistical normal distribution. The long-range correlations intrinsic to self-organized criticality in macro-scale dynamical systems are a signature of quantumlike chaos. The fractal fluctuations self-organize to form an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure. Power spectral analysis resolves such a spiral trajectory as an eddy continuum with embedded dominant wavebands. The dominant peak periodicities are functions of the golden mean. The observed fractal frequency distributions of the Drosophila DNA base sequences exhibit quasicrystalline structure with long-range spatial correlations or self-organized criticality. Modification of the DNA base sequence structure at any location may have significant noticeable effects on the function of the DNA molecule as a whole. The presence of non-coding introns may not be redundant, but serve to organize the effective functioning of the coding exons in the DNA molecule as a complete unit.Comment: 46 pages, 9 figure

    Quantum Weak Coin Flipping

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    We investigate weak coin flipping, a fundamental cryptographic primitive where two distrustful parties need to remotely establish a shared random bit. A cheating player can try to bias the output bit towards a preferred value. For weak coin flipping the players have known opposite preferred values. A weak coin-flipping protocol has a bias ϵ\epsilon if neither player can force the outcome towards their preferred value with probability more than 12+ϵ\frac{1}{2}+\epsilon. While it is known that all classical protocols have ϵ=12\epsilon=\frac{1}{2}, Mochon showed in 2007 [arXiv:0711.4114] that quantumly weak coin flipping can be achieved with arbitrarily small bias (near perfect) but the best known explicit protocol has bias 1/61/6 (also due to Mochon, 2005 [Phys. Rev. A 72, 022341]). We propose a framework to construct new explicit protocols achieving biases below 1/61/6. In particular, we construct explicit unitaries for protocols with bias approaching 1/101/10. To go below, we introduce what we call the Elliptic Monotone Align (EMA) algorithm which, together with the framework, allows us to numerically construct protocols with arbitrarily small biases.Comment: 98 pages split into 3 parts, 10 figures; For updates and contact information see https://atulsingharora.github.io/WCF. Version 2 has minor improvements. arXiv admin note: text overlap with arXiv:1402.7166 by other author

    Thermal Conductivity and Thermal Rectification in Graphene Nanoribbons: a Molecular Dynamics Study

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    We have used molecular dynamics to calculate the thermal conductivity of symmetric and asymmetric graphene nanoribbons (GNRs) of several nanometers in size (up to ~4 nm wide and ~10 nm long). For symmetric nanoribbons, the calculated thermal conductivity (e.g. ~2000 W/m-K @400K for a 1.5 nm {\times} 5.7 nm zigzag GNR) is on the similar order of magnitude of the experimentally measured value for graphene. We have investigated the effects of edge chirality and found that nanoribbons with zigzag edges have appreciably larger thermal conductivity than nanoribbons with armchair edges. For asymmetric nanoribbons, we have found significant thermal rectification. Among various triangularly-shaped GNRs we investigated, the GNR with armchair bottom edge and a vertex angle of 30{\deg} gives the maximal thermal rectification. We also studied the effect of defects and found that vacancies and edge roughness in the nanoribbons can significantly decrease the thermal conductivity. However, substantial thermal rectification is observed even in the presence of edge roughness.Comment: 13 pages, 5 figures, slightly expanded from the published version on Nano Lett. with some additional note

    The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4

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    In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks that extend beyond language processing. In this report, we delve into the performance of LLMs within the context of scientific discovery, focusing on GPT-4, the state-of-the-art language model. Our investigation spans a diverse range of scientific areas encompassing drug discovery, biology, computational chemistry (density functional theory (DFT) and molecular dynamics (MD)), materials design, and partial differential equations (PDE). Evaluating GPT-4 on scientific tasks is crucial for uncovering its potential across various research domains, validating its domain-specific expertise, accelerating scientific progress, optimizing resource allocation, guiding future model development, and fostering interdisciplinary research. Our exploration methodology primarily consists of expert-driven case assessments, which offer qualitative insights into the model's comprehension of intricate scientific concepts and relationships, and occasionally benchmark testing, which quantitatively evaluates the model's capacity to solve well-defined domain-specific problems. Our preliminary exploration indicates that GPT-4 exhibits promising potential for a variety of scientific applications, demonstrating its aptitude for handling complex problem-solving and knowledge integration tasks. Broadly speaking, we evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.Comment: 230 pages report; 181 pages for main content

    Dilaton Gravity with a Non-minmally Coupled Scalar Field

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    We discuss the two-dimensional dilaton gravity with a scalar field as the source matter. The coupling between the gravity and the scalar, massless, field is presented in an unusual form. We work out two examples of these couplings and solutions with black-hole behaviour are discussed and compared with those found in the literature

    Quantum parallel dense coding of optical images

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    We propose quantum dense coding protocol for optical images. This protocol extends the earlier proposed dense coding scheme for continuous variables [S.L.Braunstein and H.J.Kimble, Phys.Rev.A 61, 042302 (2000)] to an essentially multimode in space and time optical quantum communication channel. This new scheme allows, in particular, for parallel dense coding of non-stationary optical images. Similar to some other quantum dense coding protocols, our scheme exploits the possibility of sending a classical message through only one of the two entangled spatially-multimode beams, using the other one as a reference system. We evaluate the Shannon mutual information for our protocol and find that it is superior to the standard quantum limit. Finally, we show how to optimize the performance of our scheme as a function of the spatio-temporal parameters of the multimode entangled light and of the input images.Comment: 15 pages, 4 figures, RevTeX4. Submitted to the Special Issue on Quantum Imaging in Journal of Modern Optic
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