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Quantum computation is a promising emerging technology which, compared to conventional computation, allows for substantial speed-ups e.g. for integer factorization or database search. However, since physical realizations of quantum…

Quantum Physics · Physics 2018-06-07 Alwin Zulehner , Robert Wille

Effective quantum computation relies upon making good use of the exponential information capacity of a quantum machine. A large barrier to designing quantum algorithms for execution on real quantum machines is that, in general, it is…

Quantum Physics · Physics 2020-05-12 Adam Holmes , A. Y. Matsuura

Neural-network quantum states (NQS) have become a powerful tool in many-body physics. Of the numerous possible architectures in which neural-networks can encode amplitudes of quantum states the simplicity of the Restricted Boltzmann Machine…

Quantum Physics · Physics 2021-09-15 Michael Y. Pei , Stephen R. Clark

Efficient methods for the representation and simulation of quantum states and quantum operations are crucial for the optimization of quantum circuits. Decision diagrams (DDs), a well-studied data structure originally used to represent…

Quantum Physics · Physics 2023-09-13 Lieuwe Vinkhuijzen , Tim Coopmans , David Elkouss , Vedran Dunjko , Alfons Laarman

Neural-network state representations of quantum many-body systems are attracting great attention and more rigorous quantitative analysis about their expressibility and complexity is warranted. Our analysis of the restricted Boltzmann…

Quantum Physics · Physics 2024-05-24 Ruizhi Pan , Charles W. Clark

The aim of this paper is to introduce a new graphic representation of quantum states by means of a specific application: the analysis of two models of quantum copying machines. The graphic representation by diagrams of states offers a clear…

Quantum Physics · Physics 2007-05-23 Sara Felloni , Giuliano Strini

I provide an alternative way of seeing quantum computation. First, I describe an idealized classical problem solving machine that, thanks to a many body interaction, reversibly and nondeterministically produces the solution of the problem…

Quantum Physics · Physics 2007-10-10 Giuseppe Castagnoli

Quantum computing is a promising paradigm that may overcome the current computational power bottlenecks. The increasing maturity of quantum processors provides more possibilities for the development and implementation of quantum algorithms.…

Quantum Physics · Physics 2025-10-15 Ge Yan , Wenjie Wu , Yuheng Chen , Kaisen Pan , Xudong Lu , Zixiang Zhou , Yuhan Wang , Ruocheng Wang , Junchi Yan

Image processing is popular in our daily life because of the need to extract essential information from our 3D world, including a variety of applications in widely separated fields like bio-medicine, economics, entertainment, and industry.…

Quantum Physics · Physics 2022-03-04 Alok Anand , Meizhong Lyu , Prabh Simran Baweja , Vinay Patil

Quantum state preparation is a fundamental primitive in quantum algorithms for encoding classical data into quantum amplitudes. We compare the cost of preparing general $n$-qubit states with real amplitudes using two common paradigms:…

Quantum Physics · Physics 2026-05-20 Diyi Liu , Hanyu Wang , Shuchen Zhu , Jason Cong , Wibe A. de Jong , Di Fang , Zhen Huang , Costin Iancu , Chao Yang

Modeling open quantum systems -- quantum systems coupled to a bath -- is of value in condensed matter theory, cavity quantum electrodynamics, nanosciences and biophysics. The real-time simulation of open quantum systems was advanced…

Quantum Physics · Physics 2023-07-14 Hanggai Nuomin , David N. Beratan , Peng Zhang

This paper presents a comprehensive exploration of semi-definite programming (SDP) techniques within the context of quantum information. It examines the mathematical foundations of convex optimization, duality, and SDP formulations,…

Quantum Physics · Physics 2024-04-18 Piotr Mironowicz

Quantum computing promises substantial speedups by exploiting quantum mechanical phenomena such as superposition and entanglement. Corresponding design methods require efficient means of representation and manipulation of quantum…

Quantum Physics · Physics 2023-11-15 Alwin Zulehner , Stefan Hillmich , Robert Wille

We explore the main processes involved in the evolution of general quantum systems by means of Diagrams of States, a novel method to graphically represent and analyze how quantum information is elaborated during computations performed by…

Quantum Physics · Physics 2009-12-02 Sara Felloni , Alberto Leporati , Giuliano Strini

Quantum information refers to the distinctive information-processing properties of quantum systems, which arise when information is stored in or retrieved from nonorthogonal quantum states. More information is required to prepare an…

Quantum Physics · Physics 2007-05-23 Carlton M. Caves , Christopher A. Fuchs

One of the key considerations in the development of Quantum Machine Learning (QML) protocols is the encoding of classical data onto a quantum device. In this chapter we introduce the Matrix Product State representation of quantum systems…

Quantum Physics · Physics 2026-01-15 Chris Nakhl , Maxwell West , Muhammad Usman

The completeness of quantum state space, is usually expressed as \sum_{m=0}^{\infty}|m><m|=1, where {|m>} is selected set of quantum states (basis). Density matrix |m><m| describes a pure quantum state. In this paper, by virtue of the…

Quantum Physics · Physics 2017-11-28 Hongyi Fan , Jun-hua Chen , Dehui Zhan , Liyun Hu

We present "Diagrams of States", a way to graphically represent and analyze how quantum information is elaborated during the execution of quantum circuits. This introductory tutorial illustrates the basics, providing useful examples of…

Quantum Physics · Physics 2009-04-20 Sara Felloni , Alberto Leporati , Giuliano Strini

A method of representing probabilistic aspects of quantum systems is introduced by means of a density function on the space of pure quantum states. In particular, a maximum entropy argument allows us to obtain a natural density function…

Quantum Physics · Physics 2015-06-26 D. C. Brody , L. P. Hughston

Machine Learning (ML) models are trained using historical data to classify new, unseen data. However, traditional computing resources often struggle to handle the immense amount of data, commonly known as Big Data, within a reasonable time…

Quantum Physics · Physics 2024-11-01 Minati Rath , Hema Date