3
talks
0
committee roles
0
leadership roles
2024–2026
years active
Contributions
QIP QCrypt TQC presenter award · △program ◇steering ○organising □local · filled = chair
Talks
| Title | Conference | Type | Co-authors |
|---|---|---|---|
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Parent Lindbladians for Matrix Product Density Operators ↗
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QIP 2026 | regular | Yuhan Liu, Georgios Styliaris, Xiao-Qi Sun, David Perez-Garcia, Ignacio Cirac |
Understanding quantum phases of matter is a fundamental goal in physics. For pure states, the representatives of phases are the ground states of locally interacting Hamiltonians, which are also renormalization fixed points (RFPs). These RFP states are exactly described by tensor networks. Extending this framework to mixed states, matrix product density operators (MPDOs) which are RFPs are believed to encapsulate mixed-state phases of matter in one dimension, where non-trivial topological phases have already been shown to exist. However, to better motivate the physical relevance of those states, and in particular the physical relevance of the recently found non-trivial phases, it remains an open question whether such MPDO RFPs can be realized as steady states of local Lindbladians. In this work, we resolve this question by analytically constructing parent Lindbladians for MPDO RFPs. These Lindbladians are local, frustration-free, and exhibit minimal steady-state degeneracy. Interestingly, we find that parent Lindbladians possess a rich structure that distinguishes them from their Hamiltonian counterparts. In particular, we uncover an intriguing connection between the non-commutativity of the Lindbladian terms and the fact that the corresponding MPDO RFP belongs to a non-trivial phase. |
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| Matrix product algebras and their application for phase classification | QIP 2024 | regular | ▸David Perez-Garcia, Andras Molnar, José Garre Rubio, Norbert Schuch, Ignacio Cirac |
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Conditional independence of 1D Gibbs states with applications to efficient learning ↗
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TQC 2024 | regular | ▸Paul Gondolf, Samuel Scalet, Alvaro Alhambra, Ángela Capel |
We show that spin chains in thermal equilibrium have a correlation structure in which individual regions are strongly correlated at most with their near vicinity. We quantify this with alternative notions of the conditional mutual information defined through the so-called Belavkin-Staszewski relative entropy. We prove that these measures decay super-exponentially, under the assumption that the spin chain Hamiltonian is translation-invariant. Using a recovery map associated with these measures, we sequentially construct tensor network approximations in terms of marginals of small (sub-logarithmic) size. As a main application, we show that classical representations of the states can be learned efficiently from local measurements with a polynomial sample complexity. We also prove an approximate factorization condition for the purity of the entire Gibbs state, which implies that it can be efficiently estimated to a small multiplicative error from a small number of local measurements. As a technical step of independent interest, we show an upper bound to the decay of the Belavkin-Staszewski relative entropy upon the application of a conditional expectation. |
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Collaborators
| Co-author | Joint talks |
|---|---|
| David Perez-Garcia | 2 |
| Ignacio Cirac | 2 |
| Alvaro Alhambra | 1 |
| Andras Molnar | 1 |
| Georgios Styliaris | 1 |
| José Garre Rubio | 1 |
| Norbert Schuch | 1 |
| Paul Gondolf | 1 |
| Samuel Scalet | 1 |
| Xiao-Qi Sun | 1 |
| Yuhan Liu | 1 |
| Ángela Capel | 1 |