TY - JOUR
T1 - Direct implementation of a perceptron in superconducting circuit quantum hardware
AU - Pechal, Marek
AU - Roy, Federico
AU - Wilkinson, Samuel A.
AU - Salis, Gian
AU - Werninghaus, Max
AU - Hartmann, Michael J.
AU - Filipp, Stefan
N1 - Publisher Copyright:
© 2022 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
PY - 2022/7
Y1 - 2022/7
N2 - The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in Torrontegui and García-Ripoll [Europhys. Lett. 125, 30004 (2019)1286-485410.1209/0295-5075/125/30004], we demonstrate a superconducting qubit implementation of a controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. In a two-qubit setup we show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the gate length, the coupling between the qubits, and the frequency of the applied drive, respectively. In its general form, the gate realizes a multiqubit entangling operation in a single step, whose decomposition into single- and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.
AB - The utility of classical neural networks as universal approximators suggests that their quantum analogues could play an important role in quantum generalizations of machine-learning methods. Inspired by the proposal in Torrontegui and García-Ripoll [Europhys. Lett. 125, 30004 (2019)1286-485410.1209/0295-5075/125/30004], we demonstrate a superconducting qubit implementation of a controlled gate, which generalizes the action of a classical perceptron as the basic building block of a quantum neural network. In a two-qubit setup we show full control over the steepness of the perceptron activation function, the input weight and the bias by tuning the gate length, the coupling between the qubits, and the frequency of the applied drive, respectively. In its general form, the gate realizes a multiqubit entangling operation in a single step, whose decomposition into single- and two-qubit gates would require a number of gates that is exponential in the number of qubits. Its demonstrated direct implementation as perceptron in quantum hardware may therefore lead to more powerful quantum neural networks when combined with suitable additional standard gates.
UR - http://www.scopus.com/inward/record.url?scp=85139180967&partnerID=8YFLogxK
U2 - 10.1103/PhysRevResearch.4.033190
DO - 10.1103/PhysRevResearch.4.033190
M3 - Article
AN - SCOPUS:85139180967
SN - 2643-1564
VL - 4
JO - Physical Review Research
JF - Physical Review Research
IS - 3
M1 - 033190
ER -