The field of quantum technologies has garnered considerable interest and witnessed noteworthy progress in recent years. It is also anticipated that these technologies will continue to flourish and exert a considerable...
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The field of quantum technologies has garnered considerable interest and witnessed noteworthy progress in recent years. It is also anticipated that these technologies will continue to flourish and exert a considerable influence on society, i.e., a plethora of real-world problems that cannot be solved by classical algorithms are believed to benefit from the implementation of quantum algorithms. Meanwhile, as an alternative to cryptographic methods, physical layer security (PLS) has been extensively studied as a means to realize secure wireless communication that is resistant to attacks by both classical and quantum computers, i.e., quantum-safe. While the prevailing approach to PLS has been based on classical algorithms, this could potentially be accelerated by the application of quantum algorithms. This paper examines the potential applications of various quantum algorithms, including quantum annealing, hybrid quantum-classical algorithms, and Grover-based algorithms, to the the PLS problems. In particular, we begin with a concise overview of their applications to physical layer techniques and then proceed to discuss their use in addressing the challenges of secret message transmission and secret key generation from wireless channels.
In this paper, we consider two versions of the Text Assembling problem. We are given a sequence of strings s1, ... , sn of total length L that is a dictionary, and a string t of length m that is a text. The first vers...
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In this paper, we consider two versions of the Text Assembling problem. We are given a sequence of strings s1, ... , sn of total length L that is a dictionary, and a string t of length m that is a text. The first version of the problem is assembling t from the dictionary. The second version is the "Shortest Superstring Problem"(SSP) or the "Shortest Common Superstring Problem"(SCS). In this case, t is not given, and we should construct the shortest string (we call it superstring) that contains each string from the given sequence as a substring. These problems are connected with the sequence assembly method for reconstructing a long DNA sequence from small fragments. For both problems, we suggest new quantum algorithms that work better than their classical counterparts. In the first case, we present a quantum algorithm with O(m+log m nL) query complexity. In the case of SSP, we present a quantum algorithm with O-similar to(n(3)1.728(n) + L + n(1.5)root L) query complexity. Here O-similar to hides not only constants but logarithms of L and n also.
The realm of quantum computing is experiencing rapid growth, harnessing the principles of quantum mechanics to exponentially accelerate computations that surpass classical computing capabilities. Such advancements can...
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The realm of quantum computing is experiencing rapid growth, harnessing the principles of quantum mechanics to exponentially accelerate computations that surpass classical computing capabilities. Such advancements can potentially reshape the landscape of reliable artificial intelligence, particularly in the context of data-intensive, intricate decision-making processes. Diverse trust-oriented AI frameworks have been introduced to address various AI applications. This paper evaluates multiple AI systems focused on trust, compiling observations about their corresponding quantum algorithms. The analysis delves into quantum algorithmic approaches within three key trust-centric AI areas: detecting fraudulent users in social networks, creating diagnostic systems for healthcare, and optimising pathways for trust aggregation in social networks. This study unveils a pivotal observation: quantum algorithms demonstrate diminished time complexity, enhancing trust-based AI applications' expeditiousness.
Modelling of electromagnetic wave propagation in telecommunications has evolved from empirical models to highly deterministic ray tracing and numerical methods. The extraordinary computational effort of these methods ...
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Modelling of electromagnetic wave propagation in telecommunications has evolved from empirical models to highly deterministic ray tracing and numerical methods. The extraordinary computational effort of these methods and their wave nature seem ideally suited to be approached by quantum algorithms. We first examine current progress by reviewing recent proposals in the field. We scrutinize potentially advantageous quantum architectures, ranging from mainstream gate-level computers and near-term, mid-scale noisy architectures with limited capabilities, to adiabatic and annealing approaches that are already in commercial use. We analyze the weaknesses and strengths of recent proposals. Beyond the core algorithm, mechanisms to bridge the quantum and classical worlds are of particular interest. Extremely diverse algorithm specifications, from those based on Hamiltonian simulations and emulation of variational optimization to the unconstrained binary formulation, are compared with the use of pure gate-level circuits and known quantum subroutines. We show that the graph Laplacian, given its ability to integrate boundary conditions, is uniquely suited for quantum propagation modelling algorithms rooted in differential numerical methods. quantum computers could overcome the temporal and spatial limitations of classical methods for larger computational domains and, to some extent, address the problems of dispersion and stability in finite-difference approximations. The ability to express the solution of a problem as an eigenvalue problem turns out to be an advantage in the quantum world, where eigenvalues and eigenvectors are inextricably intertwined with quantum mechanics. In this paper, we identify the most promising techniques and scenarios that hold the greatest potential.
Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint trac...
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Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting to digital maps, carbon footprint tracking, and vegetation monitoring. However, there are limitations;satellites are difficult to manufacture, expensive to maintain, and tricky to launch into orbit. Therefore, satellites must be employed efficiently. This poses a challenge known as the satellite mission planning problem, which could be computationally prohibitive to solve on large scales. However, close-to-optimal algorithms, such as greedy reinforcement learning and optimization algorithms can often provide satisfactory resolutions. This article introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far. The problem is formulated as maximizing the number of high-priority tasks completed on real datasets containing thousands of tasks and multiple satellites. This work demonstrates that through solution-chaining and clustering, optimization and machine learning algorithms offer the greatest potential for optimal solutions. This article notably illustrates that a hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks, significantly improving over the baseline greedy methods with a completion rate of 75.8%. The results presented in this work pave the way to quantum-enabled solutions in the space industry and, more generally, future mission planning problems across industries.
Recent breakthroughs have provided a sublinear time quantum algorithm for the Longest Common Substring Problem running in (O) over tilde (n(2/3)/d(1/6)) time for two strings of length at most n, where d is the length ...
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ISBN:
(纸本)9783031721991;9783031722004
Recent breakthroughs have provided a sublinear time quantum algorithm for the Longest Common Substring Problem running in (O) over tilde (n(2/3)/d(1/6)) time for two strings of length at most n, where d is the length of the solution. At the same time, no subquadratic time quantum algorithm for the Longest Common Subsequence Problem is known, implying increasing difficulty as gaps are allowed within the solution. In this work, we consider the problem of finding two ordered matching substrings such that their total length is maximized. We present a strongly sublinear-time quantum algorithm.
quantum computing is the beginning of a new age for diverse industries, and educational technologies will significantly benefit from such quantum developments. This is a novel approach, applying quantum algorithms to ...
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A promising area of applications for quantum computing is in linear algebra problems. In this work, we introduce two new quantum t-SVD (tensor-SVD) algorithms. The first algorithm is largely based on previous work tha...
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ISBN:
(纸本)9798331541378
A promising area of applications for quantum computing is in linear algebra problems. In this work, we introduce two new quantum t-SVD (tensor-SVD) algorithms. The first algorithm is largely based on previous work that proposed a quantum t-SVD algorithm for context-aware recommendation systems. The new algorithm however seeks to address and fix certain drawbacks in the original, and is fundamentally different in its approach compared to the existing work. The second algorithm proposed uses a hybrid variational approach largely based on a known variational quantum SVD algorithm.
The task of aligning DNA sequences has been pivotal in bioinformatics since the Needleman-Wunsch algorithm in 1970 In Recent advances in sequencing technology have dramatically reduced the cost of DNA data acquisition...
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ISBN:
(纸本)9798331541378
The task of aligning DNA sequences has been pivotal in bioinformatics since the Needleman-Wunsch algorithm in 1970 In Recent advances in sequencing technology have dramatically reduced the cost of DNA data acquisition, outpacing Moore's Law for computing costs. The COVID-19 pandemic further intensified data generation from viral and human samples, creating significant computational challenges. This study investigates the potential of near-term quantum computing to address these bottlenecks. We introduce two quantum algorithms tailored for crucial bioinformatics tasks. The first algorithm applies a quantum dynamic programming approach, inspired by Ambainis et al. [2], to pairwise Sequence Alignment, demonstrating minimal speedup. The second algorithm employs a quantum search via random walk, based on Magniez et al. [3], for the Center String problem, achieving a near-quadratic speedup over the best classical algorithm for unbounded alphabets and superior performance for bounded alphabets in key parameter regimes. Our findings suggest that while quantum computing offers promising speedups for certain bioinformatics problems, the extent of its benefits varies significantly across different tasks. Future work will investigate the potential of quantum computing in analysing graph-based representations of DNA data, such as pangenome models.
quantum algorithms are often represented in terms of quantum circuits operating on ideal (logical) qubits. However, the practical implementation of these algorithms poses significant challenges. Many quantum algorithm...
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quantum algorithms are often represented in terms of quantum circuits operating on ideal (logical) qubits. However, the practical implementation of these algorithms poses significant challenges. Many quantum algorithms require a substantial number of logical qubits, and the inherent susceptibility to errors of quantum computers require quantum error correction. The integration of error correction introduces overhead in terms of both space (physical qubits required) and runtime (how long the algorithm needs to be run for). This paper addresses the complexity of comparing classical and quantum algorithms, primarily stemming from the additional quantum error correction overhead. We propose a comprehensive framework that facilitates a direct and meaningful comparison between classical and quantum algorithms. By acknowledging and addressing the challenges introduced by quantum error correction, our framework aims to provide a clearer understanding of the comparative performance of classical and quantum computing approaches. This work contributes to understanding the practical viability and potential advantages of quantum algorithms in real-world applications. We apply our framework to quantum cryptanalysis, since it is well known that quantum algorithms can break factoring and discrete logarithm based cryptography and weaken symmetric cryptography and hash functions. In order to estimate the real-world impact of these attacks, apart from tracking the development of fault-tolerant quantum computers it is important to have an estimate of the resources needed to implement these quantum attacks. This analysis provides state-of-the art snap-shot estimates of the realistic costs of implementing quantum attacks on these important cryptographic algorithms, assuming quantum fault-tolerance is achieved using surface code methods, and spanning a range of potential error rates. These estimates serve as a guide for gauging the realistic impact of these algorithms and for benchmarking
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