Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2008.12525 · IEEE Transactions on Quantum Engineering

Finding Small and Large $k$-Clique Instances on a Quantum Computer

Sara Ayman Metwalli, Francois Le Gall, Rodney Van Meter

Published Jan 1, 2020• 16 citations
arXivHTMLPDF

Abstract

Algorithms for triangle finding, the smallest nontrivial instance of the$k$-clique problem, have been proposed for quantum computers. Still, those algorithms assume the use of fixed access time quantum RAM. In this article, we present a practical gate-based approach to both the triangle-finding problem and its NP-hard$k$-clique generalization. We examine both constant factors for near-term implementation on a noisy intermediate scale quantum computing (NISQ) device and the scaling of the problem to evaluate long-term use of quantum computers. We compare the time complexity and circuit practicality of the theoretical approach and actual implementation. We propose and apply two different strategies to the$k$-clique problem, examining the circuit size of Qiskit implementations. We analyze our implementations by simulating triangle finding with various error models, observing the effect on damping the amplitude of the correct answer, and compare to execution on six real IBM quantum machines. Finally, we estimate the approximate quantum volume needed so that the smallest instance of our approach can be executable with minimal error on a real NISQ device.

DOI
10.1109/tqe.2020.3045692

Related papers

Solving various NP-hard problems using exponentially fewer qubits on a quantum computer

Yagnik Chatterjee, Eric Bourreau, Marko J. Rančić

NP-hard problems are not believed to be exactly solvable through general polynomial time algorithms. Hybrid quantum-classical algorithms to address such combinatorial problems have been of great interest in the past few years. Such algorithms are heuristic in nature and aim to obtain an approximate solution. Significant improvements in computational time and/or the ability to treat large problems are some of the principal promises of quantum computing in this regard. The hardware, however, is still in its infancy and the current noisy intermediate-scale quantum (NISQ) computers are not able to optimize industrially relevant problems. Moreover, the storage of qubits and introduction of entanglement require extreme physical conditions. An issue with quantum optimization algorithms such as the quantum approximate optimization algorithm is that they scale linearly with problem size. In this paper, we build upon a proprietary methodology which scales logarithmically with problem size---opening an avenue for treating optimization problems of unprecedented scale on gate-based quantum computers. To test the performance of the algorithm, we first find a way to apply it to a handful of NP-hard problems: Maximum Cut, Minimum Partition, Maximum Clique, Maximum Weighted Independent Set. Subsequently, these algorithms are tested on a quantum simulator with graph sizes of over a hundred nodes and on a real quantum computer up to graph sizes of 256. To our knowledge, these constitute the largest realistic combinatorial optimization problems ever run on a NISQ device, overcoming previous problem sizes by almost tenfold.

arXiv:2301.06978•Physical Review A•May 30, 2024•33 citations

Practical quantum approximate optimization algorithms on IBM quantum processors

Gregory Medwig, Sua Choi, Paulo Castillo +1 more

In recent years, significant progress has been made in building quantum computers by several companies. Despite the progress, these noisy intermediate-scale quantum (NISQ) computers still suffer from several noises and errors such as measurement errors, multi-qubit gate errors, and worse, short decoherence times. Though quantum computer vendors are releasing better quantum computers in terms of Quantum Volume, the quantum device still remains far from quantum supremacy in practical problems. The Quantum Approximate Optimization Algorithm (QAOA) was suggested to potentially demonstrate a computational advantage in combinatorial optimization problems on NISQ computers. In this paper, we optimize the QAOA circuits and to scale the problem size on IBM quantum processors. In addition, we study the effect of the length of the QAOA ansatz on IBM quantum processors and discuss optimal implementation methods for scalable QAOA. We test our implementations on the MaxCut problems.

Quantum Computing, Communication, and Simulation•Mar 13, 2024•1 citation

The bitter truth about gate-based quantum algorithms in the NISQ era

Frank Leymann, Johanna Barzen

Abstract Implementing a gate-based quantum algorithm on an noisy intermediate scale quantum (NISQ) device has several challenges that arise from the fact that such devices are noisy and have limited quantum resources. Thus, various factors contributing to the depth and width as well as to the noise of an implementation of a gate-based algorithm must be understood in order to assess whether an implementation will execute successfully on a given NISQ device. In this contribution, we discuss these factors and their impact on algorithm implementations. Especially, we will cover state preparation, oracle expansion, connectivity, circuit rewriting, and readout: these factors are very often ignored when presenting a gate-based algorithm but they are crucial when implementing such an algorithm on near-term quantum computers. Our contribution will help developers in charge of realizing gate-based algorithms on such machines in (i) achieving an executable implementation, and (ii) assessing the success of their implementation on a given machine.

arXiv:2006.02856•Quantum Science and Technology•Aug 12, 2020•226 citations

Circuit design for clique problem and its implementation on quantum computer

Arpita Sanyal, Amit Saha, Banani Saha +1 more

Abstract Finding cliques in a graph has a wide range of applications due to its pattern matching ability. The k ‐clique problem, a subset of the clique problem, determines whether or not an arbitrary network has a clique of size k . Modern‐day applications include a variation of the k ‐clique problem that lists all cliques of size k . However, the quantum implementation of such a variation of the k ‐clique problem has not been addressed yet. In this work, apart from the theoretical solution of such a k ‐clique problem, practical quantum‐gate‐based implementation has been addressed using Grover's algorithm. In a classical‐quantum hybrid architecture, this approach is extended to build the circuit for the maximum clique problem. Our technique is generalised since the program automatically builds the circuit for any given undirected and unweighted graph and any chosen k . For a small k with regard to a big graph, the proposed solution to addressing the k ‐clique issue has shown a reduction in qubit cost and circuit depth when compared to the state‐of‐the‐art approach. A framework is also presented for mapping the automated generated circuit for clique problems to quantum devices. Using IBM's Qiskit, an analysis of the experimental results is demonstrated.

arXiv:2004.10596•IET Quantum Communication•Dec 9, 2021•8 citations

Near-term Quantum Algorithms for Quantum Many-body Systems

Mark B. Ritter

Abstract There has been a good deal of work on algorithms to simulate quantum many-body systems with fault-tolerant quantum computers- those with full error correction. Fault-tolerant quantum computers of scale requisite to achieve computational advantage for these problems are likely over a decade away. Moreover, devices that we can build in the near term, called Noisy Intermediate Scale Quantum computers (NISQ), have too much noise to implement the long circuits required by these algorithms. We review heuristic, short-depth quantum algorithms more suited to NISQ computers; specifically, their scaling properties when applied to electronic and nuclear structure calculations, including Hamiltonian complexity with particle number, ansatz state preparation, convergence, and noise. We will present examples of actual quantum structure calculations with NISQ computers, as well as a newly-developed error mitigation technique that significantly improves accuracy. We end with an outlook for “advantage” – when NISQ systems might excel conventional HPC approaches for comparable problems.

Journal of Physics Conference Series•Oct 1, 2019•8 citations

Circuit Design for K-coloring Problem and its Implementation on Near-term Quantum Devices

Amit Saha, Debasri Saha, Amlan Chakrabarti

Nowadays in Quantum Computing, the implementation of quantum algorithm has created a stir since Noisy Intermediate-Scale Quantum (NISQ) devices are out in the market. Researchers are mostly interested in solving NP-complete problems with the help of quantum algorithms for its speedup. As per the work on computational complexity by Karp [1], if any of the NP-complete problem can be solved then any other NP-complete problem can be reduced to that problem in polynomial time. In this Paper, K-coloring problem (NP-complete problem) has been considered to solve using Grover's search. A comparator-based approach has been used to implement K-coloring problem which enables the reduction of the qubit cost compared to the state-of-the-art. An end-to-end automated framework has been proposed to implement the K-coloring problem for any unweighted and undirected graph on any available Noisy Intermediate-Scale Quantum (NISQ) devices, which helps in generalizing our approach.

arXiv:2009.06073•IEEE International Symposium on Smart Electronic Systems (iSES)•Dec 1, 2020•12 citations
Finding Small and Large kkk-Clique Instances on a Quantum Computer
Algorithms for triangle finding, the smallest nontrivial instance of thekkk-clique problem, have been proposed for quantum computers. Still, those algorithms assume the use of fixed access time quantum RAM. In this article, we present a practical gate-based approach to both the triangle-finding problem and its NP-hardkkk-clique generalization. We examine both constant factors for near-term implementation on a noisy intermediate scale quantum computing (NISQ) device and the scaling of the problem to evaluate long-term use of quantum computers. We compare the time complexity and circuit practicality of the theoretical approach and actual implementation. We propose and apply two different strategies to thekkk-clique problem, examining the circuit size of Qiskit implementations. We analyze our implementations by simulating triangle finding with various error models, observing the effect on damping the amplitude of the correct answer, and compare to execution on six real IBM quantum machines. Finally, we estimate the approximate quantum volume needed so that the smallest instance of our approach can be executable with minimal error on a real NISQ device.
NP-hard problems are not believed to be exactly solvable through general polynomial time algorithms. Hybrid quantum-classical algorithms to address such combinatorial problems have been of great interest in the past few years. Such algorithms are heuristic in nature and aim to obtain an approximate solution. Significant improvements in computational time and/or the ability to treat large problems are some of the principal promises of quantum computing in this regard. The hardware, however, is still in its infancy and the current noisy intermediate-scale quantum (NISQ) computers are not able to optimize industrially relevant problems. Moreover, the storage of qubits and introduction of entanglement require extreme physical conditions. An issue with quantum optimization algorithms such as the quantum approximate optimization algorithm is that they scale linearly with problem size. In this paper, we build upon a proprietary methodology which scales logarithmically with problem size---opening an avenue for treating optimization problems of unprecedented scale on gate-based quantum computers. To test the performance of the algorithm, we first find a way to apply it to a handful of NP-hard problems: Maximum Cut, Minimum Partition, Maximum Clique, Maximum Weighted Independent Set. Subsequently, these algorithms are tested on a quantum simulator with graph sizes of over a hundred nodes and on a real quantum computer up to graph sizes of 256. To our knowledge, these constitute the largest realistic combinatorial optimization problems ever run on a NISQ device, overcoming previous problem sizes by almost tenfold.
In recent years, significant progress has been made in building quantum computers by several companies. Despite the progress, these noisy intermediate-scale quantum (NISQ) computers still suffer from several noises and errors such as measurement errors, multi-qubit gate errors, and worse, short decoherence times. Though quantum computer vendors are releasing better quantum computers in terms of Quantum Volume, the quantum device still remains far from quantum supremacy in practical problems. The Quantum Approximate Optimization Algorithm (QAOA) was suggested to potentially demonstrate a computational advantage in combinatorial optimization problems on NISQ computers. In this paper, we optimize the QAOA circuits and to scale the problem size on IBM quantum processors. In addition, we study the effect of the length of the QAOA ansatz on IBM quantum processors and discuss optimal implementation methods for scalable QAOA. We test our implementations on the MaxCut problems.
Abstract Implementing a gate-based quantum algorithm on an noisy intermediate scale quantum (NISQ) device has several challenges that arise from the fact that such devices are noisy and have limited quantum resources. Thus, various factors contributing to the depth and width as well as to the noise of an implementation of a gate-based algorithm must be understood in order to assess whether an implementation will execute successfully on a given NISQ device. In this contribution, we discuss these factors and their impact on algorithm implementations. Especially, we will cover state preparation, oracle expansion, connectivity, circuit rewriting, and readout: these factors are very often ignored when presenting a gate-based algorithm but they are crucial when implementing such an algorithm on near-term quantum computers. Our contribution will help developers in charge of realizing gate-based algorithms on such machines in (i) achieving an executable implementation, and (ii) assessing the success of their implementation on a given machine.
Abstract Finding cliques in a graph has a wide range of applications due to its pattern matching ability. The k ‐clique problem, a subset of the clique problem, determines whether or not an arbitrary network has a clique of size k . Modern‐day applications include a variation of the k ‐clique problem that lists all cliques of size k . However, the quantum implementation of such a variation of the k ‐clique problem has not been addressed yet. In this work, apart from the theoretical solution of such a k ‐clique problem, practical quantum‐gate‐based implementation has been addressed using Grover's algorithm. In a classical‐quantum hybrid architecture, this approach is extended to build the circuit for the maximum clique problem. Our technique is generalised since the program automatically builds the circuit for any given undirected and unweighted graph and any chosen k . For a small k with regard to a big graph, the proposed solution to addressing the k ‐clique issue has shown a reduction in qubit cost and circuit depth when compared to the state‐of‐the‐art approach. A framework is also presented for mapping the automated generated circuit for clique problems to quantum devices. Using IBM's Qiskit, an analysis of the experimental results is demonstrated.
Abstract There has been a good deal of work on algorithms to simulate quantum many-body systems with fault-tolerant quantum computers- those with full error correction. Fault-tolerant quantum computers of scale requisite to achieve computational advantage for these problems are likely over a decade away. Moreover, devices that we can build in the near term, called Noisy Intermediate Scale Quantum computers (NISQ), have too much noise to implement the long circuits required by these algorithms. We review heuristic, short-depth quantum algorithms more suited to NISQ computers; specifically, their scaling properties when applied to electronic and nuclear structure calculations, including Hamiltonian complexity with particle number, ansatz state preparation, convergence, and noise. We will present examples of actual quantum structure calculations with NISQ computers, as well as a newly-developed error mitigation technique that significantly improves accuracy. We end with an outlook for “advantage” – when NISQ systems might excel conventional HPC approaches for comparable problems.
Nowadays in Quantum Computing, the implementation of quantum algorithm has created a stir since Noisy Intermediate-Scale Quantum (NISQ) devices are out in the market. Researchers are mostly interested in solving NP-complete problems with the help of quantum algorithms for its speedup. As per the work on computational complexity by Karp [1], if any of the NP-complete problem can be solved then any other NP-complete problem can be reduced to that problem in polynomial time. In this Paper, K-coloring problem (NP-complete problem) has been considered to solve using Grover's search. A comparator-based approach has been used to implement K-coloring problem which enables the reduction of the qubit cost compared to the state-of-the-art. An end-to-end automated framework has been proposed to implement the K-coloring problem for any unweighted and undirected graph on any available Noisy Intermediate-Scale Quantum (NISQ) devices, which helps in generalizing our approach.