Photon catalysis for general multimode multi-photon quantum state preparation
Andrei Aralov, \'Emilie Gillet, Viet Nguyen, Andrea Cosentino, Mattia Walschaers, Massimo Frigerio

TL;DR
This paper presents a novel method for generating any multimode multi-photon quantum state using photon catalysis, multiport interferometers, and advanced algebraic techniques, achieving perfect fidelity.
Contribution
It introduces a new approach linking quantum state engineering with symmetric tensor decomposition, enabling exact state preparation with minimal catalysis photons.
Findings
Achieves 100% fidelity in state generation
Provides a systematic procedure for arbitrary multimode states
Demonstrates superiority over existing methods in benchmarks
Abstract
Multimode multiphoton states are at the center of many photonic quantum technologies, from photonic quantum computing to quantum sensing. In this work, we derive a procedure to generate exactly, and with a predictable number of steps, any such state by using only multiport interferometers, photon number resolving detectors, photon additions and displacements. We achieve this goal by establishing a connection between photonic quantum state engineering and the algebraic problem of symmetric tensor decomposition. This connection allows us to solve the problem by using corresponding results from algebraic geometry and unveils a mechanism of photon catalysis, where photons are injected and subsequently retrieved in measurements, to generate entanglement that cannot be obtained through Gaussian operations. We also introduce a tensor decomposition, that generalizes our method and allows to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Neural Networks and Reservoir Computing
