Quantum Circuit Compilation for the Graph Coloring Problem
Authors: 
Angelo Oddi, Riccardo Rasconi, Marco Baioletti, Vieri Giuliano Santucci, Hamish BeckAuthors Info & Claims
Angelo Oddi, Riccardo Rasconi, Marco Baioletti, Vieri Giuliano Santucci, Hamish BeckAuthors Info & ClaimsPages 374 - 386
Abstract
In this work we investigate the performance of greedy randomised search (GRS) techniques to the problem of compiling quantum circuits that solve instances of the Graph Coloring problem. Quantum computing uses quantum gates that manipulate multi-valued bits (qubits). A quantum circuit is composed of a number of qubits and a series of quantum gates that operate on those qubits, and whose execution realises a specific quantum algorithm.
Current quantum computing technologies limit the qubit interaction distance allowing the execution of gates between adjacent qubits only. This has opened the way to the exploration of possible techniques aimed at guaranteeing nearest-neighbor (NN) compliance in any quantum circuit through the addition of a number of so-called swap gates between adjacent qubits. In addition, technological limitations (decoherence effect) impose that the overall duration (i.e., depth) of the quantum circuit realization be minimized.
One core contribution of the paper is the application of an upgraded version of the greedy randomized search (GRS) technique originally introduced in the literature that synthesises NN-compliant quantum circuits realizations, starting from a set of benchmark instances of different size belonging to the Quantum Approximate Optimization Algorithm (QAOA) class tailored for the Graph Coloring problem. We propose a comparison between the presented method and the SABRE compiler, one of the best-performing compilation procedures present in Qiskit, an open-source SDK for quantum development, both from the CPU efficiency and from the solution quality standpoint.
References
[1]
Qiskit: an open-source framework for quantum computing (2021). https://doi.org/10.5281/zenodo.2573505
[2]
Baioletti M, Rasconi R, and Oddi A Zarges C and Verel S A novel ant colony optimization strategy for the quantum circuit compilation problem Evolutionary Computation in Combinatorial Optimization 2021 Cham Springer 1-16
[3]
Chand, S., Singh, H.K., Ray, T., Ryan, M.: Rollout based heuristics for the quantum circuit compilation problem. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 974–981 (2019)
[4]
Cruz D et al. Efficient quantum algorithms for GHZ and w states, and implementation on the IBM quantum computer Adv. Quant. Technol. 2019 2 5–6 1900015
[5]
Erdos P and Renyi A On the evolution of random graphs Publ. Math. Inst. Hungary. Acad. Sci. 1960 5 17-61
[6]
Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 (2014)
[7]
Fuchs FG, Kolden HØ, Aase NH, and Sartor G Efficient encoding of the weighted max $$k$$-cut on a quantum computer using QAOA SN Comput. Sci. 2021 2 2 89
[8]
Guerreschi, G.G., Park, J.: Gate scheduling for quantum algorithms. arXiv preprint arXiv:1708.00023 (2017)
[9]
Hadfield S, Wang Z, O’Gorman B, Rieffel E, Venturelli D, and Biswas R From the quantum approximate optimization algorithm to a quantum alternating operator ansatz Algorithms 2019 12 2 34
[10]
Hart J and Shogan A Semi-greedy heuristics: an empirical study Oper. Res. Lett. 1987 6 107-114
[11]
Kole A, Datta K, and Sengupta I A heuristic for linear nearest neighbor realization of quantum circuits by swap gate insertion using -gate lookahead IEEE J. Emerg. Sel. Topics Circuits Syst. 2016 6 1 62-72
[12]
Kole A, Datta K, and Sengupta I A new heuristic for -dimensional nearest neighbor realization of a quantum circuit IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2018 37 1 182-192
[13]
Li, G., Ding, Y., Xie, Y.: Tackling the qubit mapping problem for NISQ-era quantum devices. CoRR abs/1809.02573 (2018). https://arxiv.org/1809.02573arxiv.org/abs/1809.02573
[14]
Maslov, D., Falconer, S.M., Mosca, M.: Quantum circuit placement: optimizing qubit-to-qubit interactions through mapping quantum circuits into a physical experiment. In: Proceedings of the 44th Annual Design Automation Conference, DAC’07, pp. 962–965. ACM, New York, NY, USA (2007).
[15]
Nau D, Ghallab M, and Traverso P Automated Planning: Theory & Practice 2004 San Francisco Morgan Kaufmann Publishers Inc.
[16]
Oddi, A., Smith, S.: Stochastic procedures for generating feasible schedules. In: Proceedings 14th National Conference on AI (AAAI-97), pp. 308–314 (1997)
[17]
Oddi A and Rasconi R van Hoeve WJ Greedy randomized search for scalable compilation of quantum circuits Integration of Constraint Programming, Artificial Intelligence, and Operations Research 2018 Cham Springer International Publishing 446-461
[18]
Preskill J Quantum computing in the NISQ era and beyond Quantum 2018 2 79
[19]
Resende MG and Werneck RF A hybrid heuristic for the p-median problem J. Heuristics 2004 10 1 59-88
[20]
Sete, E.A., Zeng, W.J., Rigetti, C.T.: A functional architecture for scalable quantum computing. In: 2016 IEEE International Conference on Rebooting Computing (ICRC), pp. 1–6 (2016).
[21]
Venturelli, D., Do, M., Rieffel, E., Frank, J.: Temporal planning for compilation of quantum approximate optimization circuits. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 4440–4446 (2017).
[22]
Wang Z, Rubin NC, Dominy JM, and Rieffel EG mixers: analytical and numerical results for the quantum alternating operator ansatz Phys. Rev. A 2020 101 012320
Index Terms
- Quantum Circuit Compilation for the Graph Coloring Problem
Index terms have been assigned to the content through auto-classification.
Recommendations
Solving quantum circuit compilation problem variants through genetic algorithms
AbstractThe gate-based model is one of the leading quantum computing paradigms for representing quantum circuits. Within this paradigm, a quantum algorithm is expressed in terms of a set of quantum gates that are executed on the quantum hardware over time,...
A Novel Ant Colony Optimization Strategy for the Quantum Circuit Compilation Problem
Evolutionary Computation in Combinatorial OptimizationAbstractQuantum Computing represents the most promising technology towards speed boost in computation, opening the possibility of major breakthroughs in several disciplines including Artificial Intelligence. This paper investigates the performance of a ...
An efficient circuit compilation flow for quantum approximate optimization algorithm
DAC '20: Proceedings of the 57th ACM/EDAC/IEEE Design Automation ConferenceQuantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid algorithm to solve hard combinatorial optimization problems. The two-qubits gates used in quantum circuit for QAOA are commutative i.e., the order of gates can be ...
Comments
Information & Contributors
Information
Published In
Nov 2022
503 pages
ISBN:978-3-031-27180-9
DOI:10.1007/978-3-031-27181-6
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 11 March 2023
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Sep 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in