Out-of-Distribution Generalization for Learning Quantum Channels with Low-Energy Coherent States
Year: 2025
Type: article
Abstract: When experimentally learning the action of a continuous-variable quantum process by probing it with inputs, there will often be some restriction on the input states used. One experimentally simple way to probe a quantum channel is to use low-energy coherent states. Learning a quantum channel in this way presents difficulties, due to the fact that two channels may act similarly on low-energy inputs but very differently for high-energy inputs. They may also act similarly on coherent-state inputs but differently on nonclassical inputs. Extrapolating the behavior of a channel for more general input states from its action on the far more limited set of low-energy coherent states is a case of out-of-distribution generalization. To be sure that such generalization gives meaningful results, one needs to relate error bounds for the training set to bounds that are valid for all inputs. We show that for any pair of channels that act sufficiently similarly on low-energy coherent-state inputs, one can bound how different the input-output relations are for any (high-energy or highly nonclassical) input. This proves that out-of-distribution generalization is always possible for learning quantum channels using low-energy coherent states, as long as enough samples are used.
Source: PRX Quantum
DOI: 10.1103/5m7p-kbf3
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,450
Awards: G1523888516, G1531353286, G1770695805, NGI International Collaboration – Transatlantic Fellowship Programme, G1872762428, Scalable Continuous Variable Cluster State Quantum Technologies, G2708218424, G3877413663, G498139845, G5616713406, G585908603, European Photonic Quantum Computer, G6098521345, QuSeC-TAQS: Sensing-Intelligence on The Move: Quantum-Enhanced Optical Diagnosis of Crop Diseases, G6901457620, CAREER: Using Entanglement to Enhance Communication and Sensing in the Presence of Noise, Collaborative Research: NeTS: Small: Models and Algorithms for Hybrid Continuous-Discrete Variable Quantum Networking, G716412294, G8557621597, G86560745, G8876996369, G969889393
OpenAlex ID: W4414958613