Antonio Anna Mele
@antonioannamele.bsky.social
📤 163
📥 233
📝 19
Thinking about Quantum information at Freie Universität Berlin antonioannamele.com
reposted by
Antonio Anna Mele
Marco Cerezo
3 months ago
We recently posted 2 works on quantum resource theories:
arxiv.org/abs/2506.19696
,
arxiv.org/abs/2507.10851
👂Huh? Yall want more? We got you covered with a NEW work Analyzing the free states of one quantum resource theory as resource states of another
arxiv.org/abs/2507.11793
loading . . .
Characterizing quantum resourcefulness via group-Fourier decompositions
In this work we present a general framework for studying the resourcefulness in pure states for quantum resource theories (QRTs) whose free operations arise from the unitary representation of a group....
https://arxiv.org/abs/2506.19696
1
20
4
reposted by
Antonio Anna Mele
Nahuel L Diaz
3 months ago
New work on Quantum resource theories!
arxiv.org/abs/2507.10851
A big thanks to my collaborators
@antonioannamele.bsky.social
Pablo Bermejo Paolo Braccia Andrew Deneris
@martinlaroo.bsky.social
and
[email protected]
We provide a unifying framework leading to new free operations 🧵⬇️
loading . . .
A unified approach to quantum resource theories and a new class of free operations
In quantum resource theories (QRTs) certain quantum states and operations are deemed more valuable than others. While the determination of the ``free'' elements is usually guided by the constraints of...
https://arxiv.org/abs/2507.10851
2
12
5
Just had the chance to spend a wonderful week in Florence, attending the “Understanding Quantum Machine Learning” workshop
ggi.infn.it/showevent.pl...
organized by Leonardo Banchi, Giacomo De Palma, Anderson M. Hernandez & Dario Trevisan, at the charming Galileo Galilei Institute. 🇮🇹
loading . . .
https://ggi.infn.it/showevent.pl?id=523
3 months ago
1
7
0
Super happy to see our beautiful work out — led by the amazing Los Alamos team — now on arXiv: “Characterizing Quantum Resourcefulness via Group-Fourier Decompositions”
lnkd.in/dQWhG4my
. Check out Marco’s great summary!
add a skeleton here at some point
3 months ago
0
10
0
Glad to see our Clifford commutant paper accepted as a talk at TQC 2025 happening later this year in India! 🥳
add a skeleton here at some point
5 months ago
0
13
0
reposted by
Antonio Anna Mele
Jens Eisert
6 months ago
A complete theory of the Clifford commutant
scirate.com/arxiv/2504.1...
The Clifford group is ubiquitous in quantum information science, with applications in benchmarking, quantum error correction and learning algorithms. Understanding which operators commute with is a powerful tool.
1
33
3
🧵 Super excited to finally share our new paper 🎉 Together with the dream team- Lennart Bittel,
@jenseisert.bsky.social
, Lorenzo Leone,
@sfeoliviero.bsky.social
- we present a full theory of the Clifford commutant ⚡, a central object in quantum information. ⚛️ 📄
arxiv.org/abs/2504.12263
loading . . .
A complete theory of the Clifford commutant
The Clifford group plays a central role in quantum information science. It is the building block for many error-correcting schemes and matches the first three moments of the Haar measure over the unit...
https://arxiv.org/abs/2504.12263
6 months ago
1
22
4
reposted by
Antonio Anna Mele
Vishnu Iyer
6 months ago
I'm excited to share a new preprint about learning unitary operators of mildly-interacting fermions!
arxiv.org/abs/2504.11318
@antonioannamele.bsky.social
posed this very interesting question to me and I'm glad to have made progress towards it.
loading . . .
Mildly-Interacting Fermionic Unitaries are Efficiently Learnable
Recent work has shown that one can efficiently learn fermionic Gaussian unitaries, also commonly known as nearest-neighbor matchcircuits or non-interacting fermionic unitaries. However, one could ask ...
https://arxiv.org/abs/2504.11318
0
17
3
I'm super happy to see this paper out on ArXiv today:
arxiv.org/abs/2504.11318
by
@vishnu-psiyer.bsky.social
, presenting a quantum algorithm to learn t-doped fermionic Gaussian unitaries. This work solves one of the open questions we raised in our previous paper:
journals.aps.org/prxquantum/a...
.
loading . . .
Mildly-Interacting Fermionic Unitaries are Efficiently Learnable
Recent work has shown that one can efficiently learn fermionic Gaussian unitaries, also commonly known as nearest-neighbor matchcircuits or non-interacting fermionic unitaries. However, one could ask ...
https://arxiv.org/abs/2504.11318
6 months ago
2
10
1
Happy to announce that our paper is now published in
@quantum-journal.bsky.social
. We show that deciding whether a given dataset, formed by a few Majorana correlation functions estimates, can be consistent with a free-fermionic state is an NP-complete problem.
quantum-journal.org/papers/q-202...
7 months ago
0
3
0
Super happy to see our work "Efficient learning of quantum states prepared with few fermionic non-Gaussian gates" now published in PRX Quantum. ⚛️ 🥳 Huge thanks to my incredible coauthor Yaroslav Herasymenko for the fun collaboration! 🚀 Catch our talk at
#QIP2025
to learn more!
loading . . .
Efficient Learning of Quantum States Prepared With Few Fermionic Non-Gaussian Gates
A novel tomography algorithm with provably optimal run-time can uncover fundamental properties of a broad class of states and provide efficient circuit compilation.
https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.6.010319
8 months ago
1
5
0
Check out the great Bluesky-thread by Armando about our recent work. ⚛️ We show that ‘typical’ noisy circuits can be efficiently simulated classically for any local noise and circuit architecture — in contrast to fault-tolerantly designed noisy circuits. ⚡️
add a skeleton here at some point
8 months ago
0
5
0
reposted by
Antonio Anna Mele
Marco Cerezo
8 months ago
Another amazing paper from our Summer School student
@antonioannamele.bsky.social
(is that 3 papers already?!) and from our collaboration with the power house that are
@aangrisani.bsky.social
and
@quantummanuel.bsky.social
, from
@qzoeholmes.bsky.social
! 's incredible group.
arxiv.org/abs/2501.13101
add a skeleton here at some point
1
28
6
you reached the end!!
feeds!
log in