Abstract
In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: Representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can theoretically be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated using (general purpose graphic processing unit) GPGPU. The main target of this paper is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum circuits. The results demonstrate that on one hand the quantum encoding and quantum implementation compatible implementation provides certain disadvantages with respect to the classical evolutionary computation. On the other hand, encoding certain components in a quantum compatible manner could in theory allow to accelerate the search by providing small overhead when built in quantum computer. Therefore acceleration would in turn counter weight the implementation limitations.
| Original language | English |
|---|---|
| Title of host publication | ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 220-225 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781450366854 |
| DOIs | |
| Publication status | Published - Apr 30 2019 |
| Event | 16th ACM International Conference on Computing Frontiers, CF 2019 - Alghero, Sardinia, Italy Duration: Apr 30 2019 → May 2 2019 |
Publication series
| Name | ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings |
|---|
Conference
| Conference | 16th ACM International Conference on Computing Frontiers, CF 2019 |
|---|---|
| Country | Italy |
| City | Alghero, Sardinia |
| Period | 4/30/19 → 5/2/19 |
Fingerprint
Keywords
- Quantum Circuits
- Quantum Computer
- Quantum Genetic Algorithm
ASJC Scopus subject areas
- Software
Cite this
Quantum Encoded Quantum Evolutionary Algorithm for the Design of Quantum Circuits. / Krylov, Georgiy; Lukac, Martin.
ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings. Association for Computing Machinery, Inc, 2019. p. 220-225 (ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Quantum Encoded Quantum Evolutionary Algorithm for the Design of Quantum Circuits
AU - Krylov, Georgiy
AU - Lukac, Martin
PY - 2019/4/30
Y1 - 2019/4/30
N2 - In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: Representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can theoretically be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated using (general purpose graphic processing unit) GPGPU. The main target of this paper is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum circuits. The results demonstrate that on one hand the quantum encoding and quantum implementation compatible implementation provides certain disadvantages with respect to the classical evolutionary computation. On the other hand, encoding certain components in a quantum compatible manner could in theory allow to accelerate the search by providing small overhead when built in quantum computer. Therefore acceleration would in turn counter weight the implementation limitations.
AB - In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: Representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can theoretically be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are accelerated using (general purpose graphic processing unit) GPGPU. The main target of this paper is not to propose a completely novel quantum genetic algorithm but to rather experimentally estimate the advantages of certain components of genetic algorithm being encoded and implemented in a quantum compatible manner. The algorithms are compared and evaluated on several reversible and quantum circuits. The results demonstrate that on one hand the quantum encoding and quantum implementation compatible implementation provides certain disadvantages with respect to the classical evolutionary computation. On the other hand, encoding certain components in a quantum compatible manner could in theory allow to accelerate the search by providing small overhead when built in quantum computer. Therefore acceleration would in turn counter weight the implementation limitations.
KW - Quantum Circuits
KW - Quantum Computer
KW - Quantum Genetic Algorithm
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U2 - 10.1145/3310273.3322826
DO - 10.1145/3310273.3322826
M3 - Conference contribution
AN - SCOPUS:85066015292
T3 - ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings
SP - 220
EP - 225
BT - ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings
PB - Association for Computing Machinery, Inc
ER -