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Research papers on Quantum computing algorithms

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  1. Exponential algorithmic speedup by a quantum walk

    Andrew M. Childs, Richard Cleve, E. Deotto, et al. · 2003 · ACM Symposium on Theory of Computing (STOC) · 825 citations

    We construct a black box graph traversal problem that can be solved exponentially faster on a quantum computer than on a classical computer. The quantum algorithm is based on a continuous time quantum walk, and thus employs a different technique from previous quantum algorithms based on quantum Fourier transforms. We show how to implement the quantum walk efficiently in our black box setting. We then show how this quantum walk solves our problem by rapidly traversing a graph. Finally, we prove that no classical algorithm can solve the problem in subexponential time.

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  2. Quantum speedup of Monte Carlo methods

    Ashley Montanaro · 2015 · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 337 citations

    Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions. In this work, we describe a quantum algorithm which can accelerate Monte Carlo methods in a very general setting. The algorithm estimates the expected output value of an arbitrary randomized or quantum subroutine with bounded variance, achieving a near-quadratic speedup over the best possible classical algorithm. Combining the algorithm with the use of quantum walks gives a quantum speedup of the fastest known classical algorithms with rigorous

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  3. Quantum computing for finance

    Dylan Herman, Cody Googin, Xiao-Yuan Liu, et al. · 2023 · Nature Reviews Physics · 298 citations

    Quantum computers are expected to surpass classical computers and transform industries. This Review focuses on quantum computing for financial applications and provides a summary for physicists on potential advantages and limitations of quantum techniques, as well as challenges that physicists could help tackle. Quantum algorithms for stochastic modelling, optimization and machine learning are applicable to various financial problems. Quantum Monte Carlo integration and gradient estimation can provide quadratic speedup over classical methods, but more work is required to reduce the amount of quantum resources for early fault-tolerant feasibility and achieving an actual speedup. Financial opt

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  5. Quantum Speedups for Exponential-Time Dynamic Programming Algorithms

    Andris Ambainis, Kaspars Balodis, Jānis Iraids, et al. · 2019 · Society for Industrial and Applied Mathematics eBooks · 74 citations

    In this paper we study quantum algorithms for NP-complete problems whose best classical algorithm is an exponential time application of dynamic programming. We introduce the path in the hypercube problem that models many of these dynamic programming algorithms. In this problem we are asked whether there is a path from 0n to 1n in a given subgraph of the Boolean hypercube, where the edges are all directed from smaller to larger Hamming weight. We give a quantum algorithm that solves path in the hypercube in time O*(1.817n). The technique combines Grover's search with computing a partial dynamic programming table. We use this approach to solve a variety of vertex ordering problems on graphs in

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  6. Quantum-Walk Speedup of Backtracking Algorithms

    Ashley Montanaro · 2018 · Theory of Computing · 71 citations

    $ $ We describe a general method to obtain quantum speedups of classical algorithms which are based on the technique of backtracking, a standard approach for solving constraint satisfaction problems (CSPs). Backtracking algorithms explore a tree whose vertices are partial solutions to a CSP in an attempt to find a complete solution. Assume there is a classical backtracking algorithm which finds a solution to a CSP on $n$ variables, or outputs that none exists, and whose corresponding tree contains $T$ vertices, each vertex corresponding to a test of a partial solution. Then we show that there is a bounded-error quantum algorithm which completes the same task using $O(\sqrt{T} n^{3/2} \log n)

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  7. Parallel Algorithms with Optimal Speedup for Bounded Treewidth

    Hans L. Bodlaender, Torben Hagerup · 1998 · SIAM Journal on Computing · 70 citations

    We describe the first parallel algorithm with optimal speedup for constructing minimum-width tree decompositions of graphs of bounded treewidth. On n-vertex input graphs, the algorithm works in O((log n) 2 ) time using O(n) operations on the EREW PRAM. We also give faster parallel algorithms with optimal speedup for the problem of deciding whether the treewidth of an input graph is bounded by a given constant and for a variety of problems on graphs of bounded treewidth, including all decision problems expressible in monadic second-order logic. On n-vertex input graphs, the algorithms use O(n) operations together with O(log n log * n) time on the EREW PRAM, or O(log n) time on the CRCW PRAM.

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  8. Quantum speedup of branch-and-bound algorithms

    Ashley Montanaro · 2020 · Physical Review Research · 66 citations

    Quantum computers could significantly outperform their classical counterparts for solving hard optimization problems. One prominent classical technique for such problems is known as branch-and-bound. This work describes a quantum algorithm for accelerating general branch-and-bound methods, and gives an application to spin glasses.

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  9. Anticoncentration theorems for schemes showing a quantum speedup

    Dominik Hangleiter, Juan Bermejo-Vega, Martin Schwarz, et al. · 2018 · Quantum · 59 citations

    One of the main milestones in quantum information science is to realise quantum devices that exhibit an exponential computational advantage over classical ones without being universal quantum computers, a state of affairs dubbed quantum speedup, or sometimes "quantum computational supremacy". The known schemes heavily rely on mathematical assumptions that are plausible but unproven, prominently results on anticoncentration of random prescriptions. In this work, we aim at closing the gap by proving two anticoncentration theorems and accompanying hardness results, one for circuit-based schemes, the other for quantum quench-type schemes for quantum simulations. Compared to the few other known s

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  10. Demonstration of Algorithmic Quantum Speedup

    Bibek Pokharel, Daniel A. Lidar · 2023 · Physical Review Letters · 55 citations

    Despite the development of increasingly capable quantum computers, an experimental demonstration of a provable algorithmic quantum speedup employing today's non-fault-tolerant devices has remained elusive. Here, we unequivocally demonstrate such a speedup within the oracular model, quantified in terms of the scaling with the problem size of the time-to-solution metric. We implement the single-shot Bernstein-Vazirani algorithm, which solves the problem of identifying a hidden bitstring that changes after every oracle query, using two different 27-qubit IBM Quantum superconducting processors. The speedup is observed on only one of the two processors when the quantum computation is protected by

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  11. A review on quantum computing and deep learning algorithms and their applications

    F. Valdez, P. Melin · 2022 · Soft Computing · 46 citations

    In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms. This implies that some complex problem

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  12. Enhancing the Performance Prediction of Quantum Computing Algorithms using Gradient Boosting and Ada Boost Regression

    Rajendar Dommeti · 2026 · Journal of Quantum Computing and Advanced Algorithms · 25 citations

    Quantum computing is considered to have tremendous potential to help take the emerging field of "Computational Law" to the next level of growth in terms of the expression and implementation of legal principles. With the promise of quantum technology's increasing influence on the legal industry in mind, this essay utilizes the emerging field of Computational Complexity Theory to explore the types of problems that quantum computing is capable of solving more efficiently than classical computing, which is referred to as Quantum Supremacy. From this foundation, three emerging areas within the legal sector have been identified where quantum computing is likely to show transformative superiority.

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  15. Route-Forcing: Scalable Quantum Circuit Mapping for Scalable Quantum Computing Architectures

    Pau Escofet, Alejandro Gonzalvo, E. Alarcón, et al. · 2024 · 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) · 19 citations

    Quantum computers are expected to scale in size to close the gap that currently exists between quantum algorithms and quantum hardware. To this end, quantum compilation techniques must scale along with the hardware constraints, shifting the current paradigm of obtaining an optimal compilation to relying on heuristics that allow for a fast solution, even though the quality of such a solution may not be optimal. Significant concerns arise as the execution time of current mapping techniques experiences a notable increase when applied to quantum computers with a high number of qubits. In this work, we present Route-Forcing, a quantum circuit mapping algorithm that shows a compilation time averag

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  16. Quantum computing for genomics: conceptual challenges and practical perspectives

    Aurora Maurizio, G. Mazzola · 2025 · 17 citations

    We assess the potential of quantum computing to accelerate computation of central tasks in genomics, focusing on often-neglected theoretical limitations. We discuss state-of-the-art challenges of quantum search, optimization, and machine learning algorithms. Examining database search with Grover's algorithm, we show that the expected speedup vanishes under realistic assumptions. For combinatorial optimization prevalent in genomics, we discuss the limitations of theoretical complexity in practice and suggest carefully identifying problems genuinely suited for quantum acceleration. Given the competition from excellent classical approximate solvers, quantum computing could offer a speedup in th

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  17. Quantum speedups of some general-purpose numerical optimisation algorithms

    Ashley Montanaro, Noah Linden, Cezar-Mihail Alexandru, et al. · 2020 · Bristol Research (University of Bristol) · 15 citations

    Abstract We give quantum speedups of several general-purpose numerical optimisation methods for minimising a function <?CDATA $f:{\mathbb{R}}^{n}\to \mathbb{R}$?> f : R n → R . First, we show that many techniques for global optimisation under a Lipschitz constraint can be accelerated near-quadratically. Second, we show that backtracking line search, an ingredient in quasi-Newton optimisation algorithms, can be accelerated up to quadratically. Third, we show that a component of the Nelder–Mead algorithm can be accelerated by up to a multiplicative factor of <?CDATA $O\left(\sqrt{n}\right)$?> O ( n ) . Fourth, we show that a quantum gradient computation algorithm of Gilyén et al can be used to

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  18. Quantum Speedup Based on Classical Decision Trees

    Salman Beigi, Leila Taghavi · 2020 · Quantum · 15 citations

    Lin and Lin \cite{LL16} have recently shown how starting with a classical query algorithm (decision tree) for a function, we may find upper bounds on its quantum query complexity. More precisely, they have shown that given a decision tree for a function f:{0,1}n→[m] whose input can be accessed via queries to its bits, and a guessing algorithm that predicts answers to the queries, there is a quantum query algorithm for f which makes at most O(GT) quantum queries where T is the depth of the decision tree and G is the maximum number of mistakes of the guessing algorithm. In this paper we give a simple proof of and generalize this result for functions f:[ℓ]n→[m] with non-binary input as well as

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  19. Toward superpolynomial quantum speedup of equivariant quantum algorithms with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>SU</mml:mi> <mml:mo>(</mml:mo> <mml:mi>d</mml:mi>

    Han Zheng, Zi-Mu Li, Sergii Strelchuk, et al. · 2022 · Physical Review A · 14 citations

    We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU$(d)$ symmetries. It allows us to enhance a natural model of quantum computation -- permutational quantum computing (PQC) -- and define a more powerful model: PQC+. While PQC was shown to be efficiently classically simulatable, we exhibit a problem which can be efficiently solved on PQC+ machine, whereas no classical polynomial time algorithm is known; thus providing evidence against PQC+ being classically simulatable. We further discuss practical quantum machine learning algorithms which can be carried out in the paradigm

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  20. Nanowires: Exponential speedup in quantum computing

    Mariam Akter Mimona, Md. Hosne Mobarak, Emtiuz Ahmed, et al. · 2024 · Heliyon · 13 citations

    This review paper examines the crucial role of nanowires in the field of quantum computing, highlighting their importance as versatile platforms for qubits and vital building blocks for creating fault-tolerant and scalable quantum information processing systems. Researchers are studying many categories of nanowires, including semiconductor, superconducting, and topological nanowires, to explore their distinct quantum features that play a role in creating various qubit designs. The paper explores the interdisciplinary character of quantum computing, combining the fields of quantum physics and materials science. This text highlights the significance of quantum gate operations in manipulating q

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  21. No Exponential Quantum Speedup for SIS∞ Anymore

    Robin Kothari, Ryan O'Donnell, Ke-Wen Wu · 2025 · Proceedings of the 58th Annual ACM Symposium on Theory of Computing · 12 citations

    In 2021, Chen, Liu, and Zhandry presented an efficient quantum algorithm for the average-case ℓ∞-Short Integer Solution (SIS∞) problem, in a parameter range outside the normal range of cryptographic interest, but still with no known efficient classical algorithm. This was particularly exciting since SIS∞ is a simple problem without structure, and their algorithmic techniques were different from those used in prior exponential quantum speedups. We present efficient classical algorithms for all of the SIS∞ and (more general) Constrained Integer Solution problems studied in their paper, showing there is no exponential quantum speedup anymore.

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  22. Quantum Algorithms: Overviews, Foundations, and Speedups

    Shuangbao Paul Wang, Eric Sakk · 2021 · 12 citations

    This paper discusses quantum computing with a strong focus on quantum software, quantum networks, quantum simulation, and applications. The study on quantum speedups reveals fundamental differences between quantum algorithms and classical algorithms. As a case study, further improvement on Shor's algorithm is presented with experimental results. The study shows that quantum circuits can be generated automatically to further improve the efficiency of quantum algorithms.

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  23. (Sub)Exponential Quantum Speedup for Optimization

    Jia-Qi Leng, Ke-Wen Wu, Xiao-Di Wu, et al. · 2025 · ArXiv · 10 citations

    We demonstrate provable (sub)exponential quantum speedups in both discrete and continuous optimization, achieved through simple and natural quantum optimization algorithms, namely the quantum adiabatic algorithm for discrete optimization and quantum Hamiltonian descent for continuous optimization. Our result builds on the Gily\'en--Hastings--Vazirani (sub)exponential oracle separation for adiabatic quantum computing. With a sequence of perturbative reductions, we compile their construction into two standalone objective functions, whose oracles can be directly leveraged by the plain adiabatic evolution and Schr\"odinger operator evolution for discrete and continuous optimization, respectively

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  24. A Reconfigurable Framework for Hybrid Quantum–Classical Computing

    Pratibha, Naveed Mahmud · 2025 · Algorithms · 9 citations

    Hybrid quantum–classical (HQC) computing refers to the approach of executing algorithms coherently on both quantum and classical resources. This approach makes the best use of current or near-term quantum computers by sharing the workload with classical high-performance computing. However, HQC algorithms often require a back-and-forth exchange of data between quantum and classical processors, causing system bottlenecks and leading to high latency in applications. The objective of this study is to investigate novel frameworks that unify quantum and reconfigurable resources for HQC and mitigate system bottleneck and latency issues. In this paper, we propose a reconfigurable framework for hybri

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