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Researcher profile

Ivan Rungger

Ivan Rungger contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
quant-phcond-mat.mtrl-scicond-mat.str-elcond-mat.mes-hallApplicationsComputer Visioncond-mat.othercond-mat.supr-conMachine Learningmath.STStatistics Theory

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Published work

11 published item(s)

preprint2026arXiv

A Langevin sampler for quantum tomography

Quantum tomography involves obtaining a full classical description of a prepared quantum state from experimental results. We propose a Langevin sampler for quantum tomography, that relies on a new formulation of Bayesian quantum tomography exploiting the Burer-Monteiro factorization of Hermitian positive-semidefinite matrices. If the rank of the target density matrix is known, this formulation allows us to define a posterior distribution that is only supported on matrices whose rank is upper-bounded by the rank of the target density matrix. Conversely, if the target rank is unknown, any upper bound on the rank can be used by our algorithm, and the rank of the resulting posterior mean estimator is further reduced by the use of a low-rank promoting prior density. This prior density is a complex extension of the one proposed in (Annales de l'Institut Henri Poincare Probability and Statistics, 56(2):1465-1483, 2020). We derive a PAC-Bayesian bound on our proposed estimator that matches the best bounds available in the literature, and we show numerically that it leads to strong scalability improvements compared to existing techniques when the rank of the density matrix is known to be small.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamical Mean-Field Theory for spin-dependent electron transport in spin-valve devices

We present the combination of Density Functional Theory (DFT) and Dynamical Mean Field Theory (DMFT) for computing the electron transmission through two-terminals nanoscale devices. The method is then applied to metallic junctions presenting alternating Cu and Co layers, which exhibit spin-dependent charge transport and giant magnetoresistance (GMR) effect. The calculations show that the coherent transmission through the $3d$ states is greatly suppressed by electron correlations. This is mainly due to the finite lifetime induced by the electron-electron interaction and is directly related to the imaginary part of the computed many-body DMFT self-energy. At the Fermi energy, where in accordance with the Fermi-liquid behavior the imaginary part of the self-energy vanishes, the suppression of the transmission is entirely due to the shifts of the energy spectrum induced by electron correlations. Based our results, we finally suggest that the GMR measured in Cu/Co heterostructures for electrons with energies about 1 eV above the Fermi energy is a clear manifestation of dynamical correlation effects.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Efficient recovery of variational quantum algorithms landscapes using classical signal processing

We employ spectral analysis and compressed sensing to identify settings where a variational algorithm's cost function can be recovered purely classically or with minimal quantum computer access. We present theoretical and numerical evidence supporting the viability of sparse recovery techniques. To demonstrate this approach, we use basis pursuit denoising to efficiently recover simulated Quantum Approximate Optimization Algorithm (QAOA) instances of large system size from very few samples. Our results indicate that sparse recovery can enable a more efficient use and distribution of quantum resources in the optimisation of variational algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Non-trivial symmetries in quantum landscapes and their resilience to quantum noise

Very little is known about the cost landscape for parametrized Quantum Circuits (PQCs). Nevertheless, PQCs are employed in Quantum Neural Networks and Variational Quantum Algorithms, which may allow for near-term quantum advantage. Such applications require good optimizers to train PQCs. Recent works have focused on quantum-aware optimizers specifically tailored for PQCs. However, ignorance of the cost landscape could hinder progress towards such optimizers. In this work, we analytically prove two results for PQCs: (1) We find an exponentially large symmetry in PQCs, yielding an exponentially large degeneracy of the minima in the cost landscape. Alternatively, this can be cast as an exponential reduction in the volume of relevant hyperparameter space. (2) We study the resilience of the symmetries under noise, and show that while it is conserved under unital noise, non-unital channels can break these symmetries and lift the degeneracy of minima, leading to multiple new local minima. Based on these results, we introduce an optimization method called Symmetry-based Minima Hopping (SYMH), which exploits the underlying symmetries in PQCs. Our numerical simulations show that SYMH improves the overall optimizer performance in the presence of non-unital noise at a level comparable to current hardware. Overall, this work derives large-scale circuit symmetries from local gate transformations, and uses them to construct a noise-aware optimization method.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Spin-orbit induced equilibrium spin currents in materials

The existence of spin-currents in absence of any driving external fields is commonly considered an exotic phenomenon appearing only in quantum materials, such as topological insulators. We demonstrate instead that equilibrium spin currents are a rather general property of materials with non negligible spin-orbit coupling (SOC). Equilibrium spin currents can be present at the surfaces of a slab. Yet, we also propose the existence of global equilibrium spin currents, which are net bulk spin-currents along specific crystallographic directions of materials. Equilibrium spin currents are allowed by symmetry in a very broad class of systems having gyrotropic point groups. The physics behind equilibrium spin currents is uncovered by making an analogy between electronic systems with SOC and non-Abelian gauge theories. The electron spin can be seen as the analogous of the color degree of freedom and equilibrium spin currents can then be identified with diamagnetic color currents appearing as the response to an effective non-Abelian magnetic field generated by SOC. Equilibrium spin currents are not associated with spin transport and accumulation, but they should nonetheless be carefully taken into account when computing transport spin currents. We provide quantitative estimates of equilibrium spin currents for several systems, specifically metallic surfaces presenting Rashba-like surface states, nitride semiconducting nanostructures and bulk materials, such as the prototypical gyrotropic medium tellurium. In doing so, we also point out the limitations of model approaches showing that first-principles calculations are needed to obtain reliable predictions. We therefore use Density Functional Theory computing the so-called bond currents, which represent a powerful tool to understand the relation between equilibrium currents, electronic structure and crystal point group.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The DFT+$Σ_2$ method for electron correlation effects at transition metal surfaces

We present a computational approach for electronically correlated metallic surfaces and interfaces, which combines Density Functional and Dynamical Mean Field Theory using a multi-orbital perturbative solver for the many-body problem. Our implementation is designed to describe ferromagnetic metallic thin films on a substrate. The performances are assessed in detail for a Fe monolayer on a W(110) substrate, a prototypical nanoscale magnetic system. Comparing our results to photoemission data, we find qualitative and quantitative improvements in the calculated spectral function with respect to the results of Density Functional Theory within the local spin density approximation. In particular, the spin-splitting of the $d$ states is drastically reduced and, at the same time, their spectral width becomes narrower. The method is therefore able to account for the main correlation effects in the system.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Maximally Localized Dynamical Quantum Embedding for Solving Many-Body Correlated Systems

We present a quantum embedding methodology to resolve the Anderson impurity model in the context of dynamical mean-field theory, based on an extended exact diagonalization method. Our method provides a maximally localized quantum impurity model, where the non-local components of the correlation potential remain minimal. This method comes at a large benefit, as the environment used in the quantum embedding approach is described by propagating correlated electrons and hence offers a polynomial increase $O(N^4)$ of the number of degrees of freedom for the embedding mapping without adding bath sites. We report that quantum impurity models with as few as 3 bath sites can reproduce both the Mott transition and the Kondo physics, thus opening a more accessible route to the description of time-dependent phenomena. Finally, we obtain excellent agreement for dynamical magnetic susceptibilities, poising this approach as a candidate to describe 2-particle excitations such as excitons in correlated systems. We expect that our approach will be highly beneficial for the implementation of embedding algorithms on quantum computers, as it allows for a fine description of the correlation in materials with a reduced number of required qubits.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Pressure induced electronic transitions in Samarium monochalcogenides

Pressure induced isostructural insulator to metal transition for SmS is characterised by the presence of an intermediate valence state at higher pressure which cannot be captured by the density functional theory. As a direct outcome of including the charge and spin fluctuations incorporated in dynamical mean field theory, we see the emergence of insulating and metallic phases with increasing pressure as a function of changing valence. This is accompanied by significantly improved predictions of the equilibrium lattice constants and bulk moduli for all Sm-monochalcogenides verifying experiments. Nudged Elastic Band analysis reveals the insulating states to have a finite quasiparticle weight, decreasing as the gap closes rendering the transition to be not Mott-like, and classifies these materials as correlated band insulators. The difference between the discontinuous and continuous natures of these transitions can be attributed to the closeness of the sharply resonant Sm-4f peaks to the fermi level in the predicted metallic states in SmS as compared to SmSe and SmTe.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantum State Discrimination Using Noisy Quantum Neural Networks

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, applicable on near-term quantum devices as it fulfils the above criteria. We find that when simulating gradient calculation on a noisy device, a large number of parameters is disadvantageous. By introducing a new smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We also show that networks trained at higher noise levels can still converge to useful parameters. Our findings show that noisy quantum computers can be used in applications for state discrimination and for classifiers of the output of quantum generative adversarial networks.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Existence of Shapiro Steps in the Dissipative Regime in Superconducting Weak Links

We present measurements of microwave-induced Shapiro steps in a superconducting nanobridge weak link in the dissipative branch of a hysteretic current-voltage characteristic. We demonstrate that Shapiro steps can be used to infer a reduced critical current and associated effective local temperature. Our observation of Shapiro steps in the dissipative branch hows that a finite Josephson coupling exists in the dissipative state and thus can be used to put an upper limit on the effective temperature and on the size of the region that can be heated above the critical temperature. This work provides evidence that Josephson behaviour can still exist in thermally-hysteretic weak link devices and will allow extension of the temperature ranges that nanobridge based single flux quantum circuits, nanoSQUIDs and Josephson voltage standards can be used.

0 citations0 reviewsNo code linkedOpen access