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In a world where Quantum Networks are rapidly becoming a reality, the development of the Quantum Internet is gaining increasing interest. Nevertheless, modern quantum networks are still in the early stages of development and have limited…

Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances in quantum computing toward practical applications, the need…

Quantum Physics · Physics 2022-10-26 Jernej Rudi Finžgar , Philipp Ross , Leonhard Hölscher , Johannes Klepsch , Andre Luckow

Efficient and coherent data retrieval and storage are essential for harnessing quantum algorithms' speedup. Such a fundamental task is addressed by a quantum Random Access Memory (qRAM). Despite their promising scaling properties, current…

Quantum Physics · Physics 2026-04-09 Giuseppe De Riso , Giuseppe Catalano , Seth Lloyd , Vittorio Giovannetti , Dario De Santis

In the field of quantum computing, variational quantum algorithms (VQAs) represent a pivotal category of quantum solutions across a broad spectrum of applications. These algorithms demonstrate significant potential for realising quantum…

Quantum Physics · Physics 2024-08-16 Abhishek Sadhu , Aritra Sarkar , Akash Kundu

Comparative analyses of protein-protein interaction networks play important roles in the understanding of biological processes. However, the growing enormity of available data on the networks becomes a computational challenge for the…

Computational Engineering, Finance, and Science · Computer Science 2016-04-15 Anmer Daskin

We consider the problem of search of an unstructured list for a marked element, when one is given advice as to where this element might be located, in the form of a probability distribution. The goal is to minimise the expected number of…

Quantum Physics · Physics 2012-08-02 Ashley Montanaro

Quantum computers use the quantum interference of different computational paths to enhance correct outcomes and suppress erroneous outcomes of computations. In effect, they follow the same logical paradigm as (multi-particle)…

Quantum Physics · Physics 2007-05-23 R. Cleve , A. Ekert , L. Henderson , C. Macchiavello , M. Mosca

The aim of this work is to develop a framework for realising quantum network algorithms with the use of prior knowledge about the structure of the network. We seek to obtain computational methods that allows us to locally determine network…

Quantum Physics · Physics 2017-01-30 Przemysław Sadowski

Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain scenarios. However, practical benefits require further…

Quantum Physics · Physics 2024-08-05 Michael Kölle , Daniel Seidl , Maximilian Zorn , Philipp Altmann , Jonas Stein , Thomas Gabor

Higher-order networks are efficient representations of sequential data. Unlike the classic first-order network approach, they capture indirect dependencies between items composing the input sequences by the use of \textit{memory-nodes}. We…

Physics and Society · Physics 2021-09-08 Célestin Coquidé , Julie Queiros , François Queyroi

The driving force in the pursuit for quantum computation is the exciting possibility that quantum algorithms can be more efficient than their classical analogues. Research on the subject has unraveled several aspects of how that can happen.…

Quantum Physics · Physics 2011-02-11 Apoorva Patel

This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced…

Quantum Physics · Physics 2007-05-23 Dan Ventura , Tony Martinez

Several aspects of classical and quantum mechanics applied to a class of strongly chaotic systems are studied. These consist of single particles moving without external forces on surfaces of constant negative Gaussian curvature whose…

chao-dyn · Physics 2009-10-22 Jens Bolte

Quantum reservoir computing is an emerging field in machine learning with quantum systems. While classical reservoir computing has proven to be a capable concept of enabling machine learning on real, complex dynamical systems with many…

Quantum Physics · Physics 2023-12-14 Niclas Götting , Frederik Lohof , Christopher Gies

The key approaches for machine learning, especially learning in unknown probabilistic environments are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by…

Quantum Physics · Physics 2008-10-22 Daoyi Dong , Chunlin Chen , Hanxiong Li , Tzyh-Jong Tarn

In this work, we propose a new way to (non-interactively, verifiably) demonstrate Quantum Advantage by solving the average-case $\mathsf{NP}$ search problem of finding a solution to a system of (underdetermined) multivariate quadratic…

Quantum Physics · Physics 2024-12-02 Pierre Briaud , Riddhi Ghosal , Aayush Jain , Paul Lou , Amit Sahai

The quantum information science community has seen a surge in new algorithmic developments across scientific domains. These developments have demonstrated polynomial or better improvements in computational and space complexity,…

Quantum Physics · Physics 2022-05-03 Vincent R. Pascuzzi , Ning Bao , Ang Li

In the near future, there will likely be special-purpose quantum computers with 40-50 high-quality qubits. This paper lays general theoretical foundations for how to use such devices to demonstrate "quantum supremacy": that is, a clear…

Quantum Physics · Physics 2016-12-28 Scott Aaronson , Lijie Chen

For many applications the presence of a quantum advantage crucially depends on the availability of resourceful states. Although the resource typically depends on the particular task, in the context of multipartite systems entangled quantum…

Quantum Physics · Physics 2025-01-03 Jonathan Steinberg , Otfried Gühne
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