Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 093018 |
| Fachzeitschrift | New journal of physics |
| Jahrgang | 26 |
| Ausgabenummer | 9 |
| Publikationsstatus | Veröffentlicht - 12 Sept. 2024 |
Abstract
We train a model atom to recognize pixel-drawn digits based on hand-written numbers in the range 0-9, employing intense light-matter interaction as a computational resource. For training, the images of the digits are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states (classification states) that correspond to the input digits. A success rate of about 40% is achieved when using a basic optimization scheme that might be limited by the computational resources for finding the optimal program pulse in a high-dimensional search space. Our key result is the demonstration that the laser-programmed atom is able to generalize, i.e. successful classification is not limited to the training examples, but also the classification of previously unseen images is improved by training. This atom-sized machine-learning image-recognition scheme operates on time scales down to tens of femtoseconds, is scalable towards larger (e.g. molecular) systems, and is readily reprogrammable towards other learning/classification tasks. An experimental implementation of the scheme using ultrafast polarization pulse shaping and differential photoelectron detection is within reach.
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in: New journal of physics, Jahrgang 26, Nr. 9, 093018, 12.09.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Ultrafast artificial intelligence
T2 - machine learning with atomic-scale quantum systems
AU - Pfeifer, Thomas
AU - Wollenhaupt, Matthias
AU - Lein, Manfred
N1 - Publisher Copyright: © 2024 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.
PY - 2024/9/12
Y1 - 2024/9/12
N2 - We train a model atom to recognize pixel-drawn digits based on hand-written numbers in the range 0-9, employing intense light-matter interaction as a computational resource. For training, the images of the digits are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states (classification states) that correspond to the input digits. A success rate of about 40% is achieved when using a basic optimization scheme that might be limited by the computational resources for finding the optimal program pulse in a high-dimensional search space. Our key result is the demonstration that the laser-programmed atom is able to generalize, i.e. successful classification is not limited to the training examples, but also the classification of previously unseen images is improved by training. This atom-sized machine-learning image-recognition scheme operates on time scales down to tens of femtoseconds, is scalable towards larger (e.g. molecular) systems, and is readily reprogrammable towards other learning/classification tasks. An experimental implementation of the scheme using ultrafast polarization pulse shaping and differential photoelectron detection is within reach.
AB - We train a model atom to recognize pixel-drawn digits based on hand-written numbers in the range 0-9, employing intense light-matter interaction as a computational resource. For training, the images of the digits are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states (classification states) that correspond to the input digits. A success rate of about 40% is achieved when using a basic optimization scheme that might be limited by the computational resources for finding the optimal program pulse in a high-dimensional search space. Our key result is the demonstration that the laser-programmed atom is able to generalize, i.e. successful classification is not limited to the training examples, but also the classification of previously unseen images is improved by training. This atom-sized machine-learning image-recognition scheme operates on time scales down to tens of femtoseconds, is scalable towards larger (e.g. molecular) systems, and is readily reprogrammable towards other learning/classification tasks. An experimental implementation of the scheme using ultrafast polarization pulse shaping and differential photoelectron detection is within reach.
KW - artificial intelligence
KW - atoms
KW - electron dynamics
KW - quantum dynamics
KW - ultrafast
UR - http://www.scopus.com/inward/record.url?scp=85203874156&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2303.12231
DO - 10.48550/arXiv.2303.12231
M3 - Article
AN - SCOPUS:85203874156
VL - 26
JO - New journal of physics
JF - New journal of physics
SN - 1367-2630
IS - 9
M1 - 093018
ER -