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Quantum machine learning with Adaptive Boson Sampling via post-selection
Francesco Hoch1, Eugenio Caruccio1, Giovanni Rodari1
1Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, I-00185, Roma, Italy.
Nature Communications
|January 21, 2025
Summary
Adaptive Boson Sampling enhances quantum machine learning. This approach uses programmable photonic circuits and post-selection, offering a viable path for dimension-enhanced quantum machine learning with linear optical devices.
Area of Science:
- Quantum Information Science
- Quantum Machine Learning
- Photonic Quantum Computing
Background:
- Large-scale universal quantum computation is challenging.
- Non-universal models like Boson Sampling offer a path to quantum computational advantage.
- Quantum machine learning with linear optics is an underexplored area.
Purpose of the Study:
- To experimentally implement quantum machine learning protocols.
- To explore adaptive strategies in photonic quantum computing.
- To demonstrate dimension-enhanced quantum machine learning using linear optical devices.
Main Methods:
- Utilized a Boson Sampling platform with universal programmable photonic circuits.
- Fabricated circuits using femtosecond laser writing.
- Introduced adaptivity through post-selection.
Main Results:
- Successfully implemented quantum machine learning protocols with adaptive Boson Sampling.
- Demonstrated the viability of adaptive strategies in photonic quantum systems.
- Showcased dimension-enhanced capabilities for quantum machine learning.
Conclusions:
- Adaptive Boson Sampling is a practical approach for quantum machine learning.
- Femtosecond laser-written photonic circuits enable advanced quantum functionalities.
- This work paves the way for useful applications of linear optical quantum devices.
