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

IEEE Congress on Evolutionary Computation (CEC)

Finding solutions to NP problems: philosophical differences between quantum and evolutionary search algorithms

G.W. Greenwood

Published Nov 13, 2002• 32 citations
Publisher

Abstract

This paper uses instances of SAT, 3SAT and TSP to describe how evolutionary search (running on a classical computer) differs from quantum search (running on a quantum computer) for solving NP problems.
DOI
10.1109/cec.2001.934274

Related papers

Finding Solutions to NP Problems: Philosophical Difference Between Quantum and Evolutionary Search Algorithms

Greenwood, G. W.

There is no known polynomial-time algorithm that can solve an NP problem. Evolutionary search has been shown to be a viable method of finding acceptable solutions within a reasonable time period. Recently quantum computers have surfaced as another alternative method. But these two methods use radically different philosophies for solving NP problems even though both search methods are non-deterministic. This paper uses instances of {\bf SAT}, {\bf 3SAT} and {\bf TSP} to describe how these two methods differ in their approach to solving NP problems.

arXiv:quant-ph/0010021•Oct 5, 2000•3 citations

Different adiabatic quantum optimization algorithms for the NP-complete exact cover and 3SAT problems

ChoiVicky

One of the most important questions in studying quantum computation is: whether a quantum computer can solve NP-complete problems more efficiently than a classical computer? In 2000, Farhi, et al. ...

Quantum Information and Computation•Jul 1, 2011•9 citations

Using an Evolutionary Algorithm to Create (MAX)-3SAT QUBOs

Sebastian Zielinski, Maximilian Zorn, Thomas Gabor +2 more

A common way of solving satisfiability instances with quantum methods is to transform these instances into instances of QUBO. State-of-the-art transformations from MAX-3SAT to QUBO work by mapping clauses of a 3SAT formula associated with the MAX-3SAT instance to an instance of QUBO and combining the resulting QUBOs into a single QUBO instance representing the whole MAX-3SAT instance. As creating these transformations is currently done manually or via exhaustive search methods and is, therefore, algorithmically inefficient, we see potential for including search-based optimization. In this paper, we propose two methods of using evolutionary algorithms to create QUBO representations of MAX-3SAT problems automatically. We evaluate our created QUBOs on 500 and 1000-clause 3SAT formulae and find competitive performance to state-of-the-art baselines when using both classical and quantum annealing solvers.

arXiv:2405.09272•Genetic and Evolutionary Computation Conference Companion (GECCO Companion)•Jul 14, 2024•1 citation

Analysis of Classical Base Sort and Search Algorithms vs. Quantum Computing Sort and Search Algorithms in the Emerging Revolutionary Computer Science Field

Thushan Amarasinghege

•Jan 1, 2025•0 citations

A Numerical Study of the Performance of a Quantum Adiabatic Evolution Algorithm for Satisfiability

Edward Farhi, Jeffrey Goldstone, Sam Gutmann

Quantum computation by adiabatic evolution, as described in quant-ph/0001106, will solve satisfiability problems if the running time is long enough. In certain special cases (that are classically easy) we know that the quantum algorithm requires a running time that grows as a polynomial in the number of bits. In this paper we present numerical results on randomly generated instances of an NP-complete problem and of a problem that can be solved classically in polynomial time. We simulate a quantum computer (of up to 16 qubits) by integrating the Schrodinger equation on a conventional computer. For both problems considered, for the set of instances studied, the required running time appears to grow slowly as a function of the number of bits.

arXiv:quant-ph/0007071•Jul 19, 2000•44 citations

A New Quantum Evolutionary Local Search Algorithm for MAX 3-SAT Problem

Abdesslem Layeb, Djamel Eddine Saïdouni

Lecture notes in computer science•Jan 1, 2008•20 citations
There is no known polynomial-time algorithm that can solve an NP problem. Evolutionary search has been shown to be a viable method of finding acceptable solutions within a reasonable time period. Recently quantum computers have surfaced as another alternative method. But these two methods use radically different philosophies for solving NP problems even though both search methods are non-deterministic. This paper uses instances of {\bf SAT}, {\bf 3SAT} and {\bf TSP} to describe how these two methods differ in their approach to solving NP problems.
A common way of solving satisfiability instances with quantum methods is to transform these instances into instances of QUBO. State-of-the-art transformations from MAX-3SAT to QUBO work by mapping clauses of a 3SAT formula associated with the MAX-3SAT instance to an instance of QUBO and combining the resulting QUBOs into a single QUBO instance representing the whole MAX-3SAT instance. As creating these transformations is currently done manually or via exhaustive search methods and is, therefore, algorithmically inefficient, we see potential for including search-based optimization. In this paper, we propose two methods of using evolutionary algorithms to create QUBO representations of MAX-3SAT problems automatically. We evaluate our created QUBOs on 500 and 1000-clause 3SAT formulae and find competitive performance to state-of-the-art baselines when using both classical and quantum annealing solvers.
Quantum computation by adiabatic evolution, as described in quant-ph/0001106, will solve satisfiability problems if the running time is long enough. In certain special cases (that are classically easy) we know that the quantum algorithm requires a running time that grows as a polynomial in the number of bits. In this paper we present numerical results on randomly generated instances of an NP-complete problem and of a problem that can be solved classically in polynomial time. We simulate a quantum computer (of up to 16 qubits) by integrating the Schrodinger equation on a conventional computer. For both problems considered, for the set of instances studied, the required running time appears to grow slowly as a function of the number of bits.