Genetic algorithm based quantum circuit synthesis with adaptive parameters control

Authors:
Cristian Ruican;Mihai Udrescu;Lucian Prodan;Mircea Vladutiu
Affiliations:
Computer Science and Engineering Department, University "Politehnica" Timisoara, Timisoara, Romania;Computer Science and Engineering Department, University "Politehnica" Timisoara, Timisoara, Romania;Computer Science and Engineering Department, University "Politehnica" Timisoara, Timisoara, Romania;Computer Science and Engineering Department, University "Politehnica" Timisoara, Timisoara, Romania
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 6
Cited 1

Quantum computation and quantum information

Quantum computation and quantum information
Evolving Quantum Circuits Using Genetic Algorithm

EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
A Layered Software Architecture for Quantum Computing Design Tools

Computer
Arithmetic on a distributed-memory quantum multicomputer

ACM Journal on Emerging Technologies in Computing Systems (JETC)
Parameter control in evolutionary algorithms

IEEE Transactions on Evolutionary Computation
Quantum Circuit Simplification and Level Compaction

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Adaptive vs. self-adaptive parameters for evolving quantum circuits

ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware

Quantified Score

Hi-index	0.01

Visualization

Abstract

Genetic algorithms were among the early choices for quantum circuit synthesis, because of their ability to evolve a given starting circuit towards one of the possible solutions. The synthesis method presented here is the first GA-based approach that dynamically adjusts its control parameters. The adaptive parameter control takes into account the analysis performed on each genetic operator, in order to automatically find an acceptable tradeoff between runtime and appropriate exploration. The experimental results prove that this method improves the synthesis runtime and the size of the circuit to be handled up to 7 qubits (previous GA-based techniques are effective only for 3-4 qubit circuits).