Junseo Lee
@harris-junseo.bsky.social
Reposted by Junseo Lee
Super happy to share our new preprint today on ArXiv: 🥳
arxiv.org/pdf/2510.05531
It’s about efficient learning of bosonic Gaussian unitaries with provable recovery guarantees in a physically motivated accuracy metric: the "energy-constrained diamond-norm".
arxiv.org/pdf/2510.05531
It’s about efficient learning of bosonic Gaussian unitaries with provable recovery guarantees in a physically motivated accuracy metric: the "energy-constrained diamond-norm".
October 8, 2025 at 12:23 PM
Super happy to share our new preprint today on ArXiv: 🥳
arxiv.org/pdf/2510.05531
It’s about efficient learning of bosonic Gaussian unitaries with provable recovery guarantees in a physically motivated accuracy metric: the "energy-constrained diamond-norm".
arxiv.org/pdf/2510.05531
It’s about efficient learning of bosonic Gaussian unitaries with provable recovery guarantees in a physically motivated accuracy metric: the "energy-constrained diamond-norm".
Reposted by Junseo Lee
The saga of *quantum learning theory with CV systems* never ends!
And indeed, when you look closely at this field, many natural and promising questions arise. For instance:
How to learn CV Gaussian unitaries?
arxiv.org/pdf/2510.05531
1/
And indeed, when you look closely at this field, many natural and promising questions arise. For instance:
How to learn CV Gaussian unitaries?
arxiv.org/pdf/2510.05531
1/
October 8, 2025 at 10:36 AM
The saga of *quantum learning theory with CV systems* never ends!
And indeed, when you look closely at this field, many natural and promising questions arise. For instance:
How to learn CV Gaussian unitaries?
arxiv.org/pdf/2510.05531
1/
And indeed, when you look closely at this field, many natural and promising questions arise. For instance:
How to learn CV Gaussian unitaries?
arxiv.org/pdf/2510.05531
1/