### Abstract

Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.

Original language | English |
---|---|

Title of host publication | Proceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018 |

Publisher | IEEE Computer Society |

Pages | 186-191 |

Number of pages | 6 |

Volume | 2018-May |

ISBN (Electronic) | 9781538644638 |

DOIs | |

Publication status | Published - Jul 19 2018 |

Event | 48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018 - Linz, Austria Duration: May 16 2018 → May 18 2018 |

### Other

Other | 48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018 |
---|---|

Country | Austria |

City | Linz |

Period | 5/16/18 → 5/18/18 |

### Fingerprint

### Keywords

- CNOT logic gate
- Measurement
- Quanum neural networks

### ASJC Scopus subject areas

- Computer Science(all)
- Mathematics(all)

### Cite this

*Proceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018*(Vol. 2018-May, pp. 186-191). IEEE Computer Society. https://doi.org/10.1109/ISMVL.2018.00040

**CNOT-measure quantum neural networks.** / Lukac, Martin; Abdiyeva, Kamila; Kameyama, Michitaka.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018.*vol. 2018-May, IEEE Computer Society, pp. 186-191, 48th IEEE International Symposium on Multiple-Valued Logic, ISMVL 2018, Linz, Austria, 5/16/18. https://doi.org/10.1109/ISMVL.2018.00040

}

TY - GEN

T1 - CNOT-measure quantum neural networks

AU - Lukac, Martin

AU - Abdiyeva, Kamila

AU - Kameyama, Michitaka

PY - 2018/7/19

Y1 - 2018/7/19

N2 - Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.

AB - Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.

KW - CNOT logic gate

KW - Measurement

KW - Quanum neural networks

UR - http://www.scopus.com/inward/record.url?scp=85050992798&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050992798&partnerID=8YFLogxK

U2 - 10.1109/ISMVL.2018.00040

DO - 10.1109/ISMVL.2018.00040

M3 - Conference contribution

AN - SCOPUS:85050992798

VL - 2018-May

SP - 186

EP - 191

BT - Proceedings - 2018 IEEE 48th International Symposium on Multiple-Valued Logic, ISMVL 2018

PB - IEEE Computer Society

ER -