Giuseppe Carleo
@gppcarleo
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Associate professor of computational quantum science (@EPFL_en @cqs_lab). ML for quantum; quantum computing; growing the @NetKetOrg.
Lausanne, Switzerland
Joined April 2016
Fresh in @NaturePhysics from @cqs_lab @epfl! Led by Linda Mauron, Zakari Denis & Jannes Nys: we predict topological ent. entropy in Rydberg simulators via tVMC. Surprisingly, we find RVB-like properties without the expected topological entropy signatures.
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We have an opening @EPFL_en for a Condensed Matter/ Interacting Quantum Matter **theorist**, at our Physics Institute. Appointment is at the level of the tenure track assistant professor, with generous support and excellent conditions. DM for more info.
epfl.ch
The School of Basic Sciences (Physics, Chemistry and Mathematics) at EPFL seeks to appoint a Tenure Track Assistant Professor in condensed matter theory with a focus on interacting quantum matter....
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RT @sinibandro: Thrilled to share our latest paper where we formalize VMC for multiple wave function optimization….
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Excited to share our latest quantum chemistry preprint @cqs_lab, the result of the hard work of Clemens Giuliani, We use a "simple" variational wavefunction composed of a few hundred optimized non-orthogonal Slater determinants and show that it achieves
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We provide a classical simulation of DWave quantum "s-word" paper. Here it is , great work by Linda Mauron @cqs_lab, check it out! . Our findings are in line with the other pre-print from @FlatironInst @MStoudenmire that just came out, but with a very.
arxiv.org
Recent demonstrations of D-Wave's annealing-based quantum simulators have established new benchmarks for quantum computational advantage [arXiv:2403.00910]. However, the precise location of the...
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RT @sinibandro: Check out our new work with A. F. Mello, M. Collura and @gppcarleo on the first scalable method to compute non-stabilizerne….
arxiv.org
We introduce a methodology to estimate non-stabilizerness or "magic", a key resource for quantum complexity, with Neural Quantum States (NQS). Our framework relies on two schemes based on Monte...
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Most importantly, you can download these models for free and have state-of-the-art pre trained wave functions that work on many problems ! Many thanks to @ll_viteritti of @EPFL_en @cqs_lab and @riccardorende of @Sissaschool for taking this to the finish.
huggingface.co
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In the case of disordered models P(gamma) is naturally the distribution over disorder, however it can be anything meaningful, for example the distribution of couplings in a generalized long-range Heisenberg model or even the lattice structure underlying a given hamiltonian. (4/5).
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The general idea is to minimize the ensemble energy over a suitable distribution of Hamiltonian parameters P(gamma). The wave function (a transformer) depends on the Hamiltonian, and generalizes across many instances of different Hamiltonians. We give a version of SR specialized
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These models allows for applications that are hard to do with the standard framework, e.g.: 1. Automatic ensemble averages for hamiltonians with disorder 2. Computing fidelity susceptibility for automatic, and from first-principles, detection of phase transitions 3. Fine tuning
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Our work on Foundation Neural Quantum States finally out: These are generalized NQS that work for many hamiltonians: think of a general solver working for many ground-state problems! Pre-trained models ready for fine tuning are available here.
arxiv.org
Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification...
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Looking for a talented postdoc to join my group @cqs_lab @EPFL_en! 🇨🇭 . Research Topics Include: Neural Quantum States, Many-Body Systems, Ab-Initio & Quantum Chemistry. etc . Start: Fall 2025, excellent conditions . Apply:
epfl.ch
At the Computational Quantum Science Lab we typically have several openings yearly (at the PhD/ Postdoc level). Applications are reviewed twice a year, at the beginning of April and at the beginning...
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For the french-speaking friends: an article in @lemondefr "AI versus quantum computing : a very physical fight!" also featuring excerpts from an interview I had with David Larousserie (needs a subscription to Le Monde).
lemonde.fr
Dans la course à la résolution des problèmes fondamentaux de physique et de chimie, l’intelligence artificielle et l’informatique quantique sont les deux champions en lice. Pour mieux en comprendre...
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(4/n) We showcase the method on quantum quenches in the Transverse-Field Ising model in both 1D and 2D. By extracting asymptotic behavior, we uncover signatures of ergodicity breaking in 2D. I believe this method is very promising, can set a new standard to benchmark quantum.
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(3/n) We use a Galerkin-inspired ansatz combining Neural Quantum States with Fourier decomposition to capture the relevant dynamical frequencies. The time-depedent state is the linear combination fo time-independent basis states, fully optimized by the loss function.
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(2/n) Unlike conventional step-by-step approaches, our method minimizes a physically-motivated loss function across the whole time window. The loss is general and works for arbitrary, unnormalized time-dep states.
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(1/n) Excited to finally share our new paper to study quantum dynamics with neural quantum states: "Time-dependent Neural Galerkin Method for Quantum Dynamics" with @sinibandro,.@philipvinc.and D Hendry, @cqs_lab @EPFL_en @Polytechnique. Paper here:
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It's the second time in less than a week that an @arxiv submission from my group is placed on hold (quant-ph). Never happened to me before, what's going on?.
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