Direct implementation of a perceptron in superconducting circuit quantum hardware

Pechal M, Roy F, Wilkinson S, Salis G, Werninghaus M, Hartmann M, Filipp S (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Physical Review Research American Physical Society

Book Volume: 4

Journal Issue: 3

DOI: 10.1103/PhysRevResearch.4.033190

Abstract

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 Garcia-Ripoll [Europhys. Lett. 125, 30004 (2019)], 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.

Authors with CRIS profile

Samuel Wilkinson Lehrstuhl für Theoretische Physik Michael Hartmann Lehrstuhl für Theoretische Physik

Involved external institutions

IBM Zurich Research Laboratory CH Switzerland (CH)

How to cite

APA:

Pechal, M., Roy, F., Wilkinson, S., Salis, G., Werninghaus, M., Hartmann, M., & Filipp, S. (2022). Direct implementation of a perceptron in superconducting circuit quantum hardware. Physical Review Research, 4(3). https://dx.doi.org/10.1103/PhysRevResearch.4.033190

MLA:

Pechal, Marek, et al. "Direct implementation of a perceptron in superconducting circuit quantum hardware." Physical Review Research 4.3 (2022).

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