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Nicola Pancotti

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quant-ph cond-mat.dis-nn cond-mat.stat-mech Computer Vision Machine Learning nlin.CG q-fin.CP q-fin.RM

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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.

preprint2022arXiv

A highly efficient tensor network algorithm for multi-asset Fourier options pricing

Risk assessment and in particular derivatives pricing is one of the core areas in computational finance and accounts for a sizeable fraction of the global computing resources of the financial industry. We outline a quantum-inspired algorithm for multi-asset options pricing. The algorithm is based on tensor networks, which have allowed for major conceptual and numerical breakthroughs in quantum many body physics and quantum computation. In the proof-of-concept example explored, the tensor network approach yields several orders of magnitude speedup over vanilla Monte Carlo simulations. We take this as good evidence that the use of tensor network methods holds great promise for alleviating the computation burden of risk evaluation in the financial and other industries, thus potentially lowering the carbon footprint these simulations incur today.

preprint2022arXiv

Kinetically Constrained Quantum Dynamics in Superconducting Circuits

We study the dynamical properties of the bosonic quantum East model at low temperature. We show that a naive generalization of the corresponding spin-1/2 quantum East model does not posses analogous slow dynamical properties. In particular, conversely to the spin case, the bosonic ground state turns out to be not localized. We restore localization by introducing a repulsive interaction term. The bosonic nature of the model allows us to construct rich families of many-body localized states, including coherent, squeezed and cat states. We formalize this finding by introducing a set of superbosonic creation-annihilation operators which satisfy the bosonic commutation relations and, when acting on the vacuum, create excitations exponentially localized around a certain site of the lattice. Given the constrained nature of the model, these states retain memory of their initial conditions for long times. Even in the presence of dissipation, we show that quantum information remains localized within decoherence times tunable with the parameters of the system. We propose an implementation of the bosonic quantum East model based on state-of-the-art superconducting circuits, which could be used in the near future to explore dynamical properties of kinetically constrained models in modern platforms.

preprint2020arXiv

Quantum East model: localization, non-thermal eigenstates and slow dynamics

We study in detail the properties of the quantum East model, an interacting quantum spin chain inspired by simple kinetically-constrained models of classical glasses. Through a combination of analytics, exact diagonalization and tensor-network methods we show the existence of a transition, from a fast to a slow thermalization regime, which manifests itself throughout the spectrum. On the slow side, by exploiting the localization of the ground state and the form of the Hamiltonian, we explicitly construct a large (exponential in size) number of non-thermal states which become exact finite-energy-density eigenstates in the large-size limit, as expected for a true phase transition. A ``super-spin'' generalization allows us to find a further large class of area-law states proved to display very slow relaxation. These states retain memory of their initial conditions for extremely long times. Our numerical analysis reveals that the localization properties are not limited to the ground state and that} many eigenstates have large overlap with product states and can be approximated well by matrix product states at arbitrary energy densities. The mechanism that induces localization to the ground state, and hence the non-thermal behavior of the system, can be extended to a wide range of models including a number of simple spin chains. We discuss implications of our results for slow thermalization and non-ergodicity more generally in disorder-free systems with constraints and we give numerical evidence that these results may be extended to two dimensional systems.

preprint2020arXiv

Speed-ups to isothermality: Enhanced quantum thermal machines through control of the system-bath coupling

Isothermal transformations are minimally dissipative but slow processes, as the system needs to remain close to thermal equilibrium along the protocol. Here, we show that smoothly modifying the system-bath interaction can significantly speed up such transformations. In particular, we construct protocols where the overall dissipation $W_{\rm diss}$ decays with the total time $τ_{\rm tot}$ of the protocol as $W_{\rm diss} \propto τ_{\rm tot}^{-2α-1}$, where each value $α> 0$ can be obtained by a suitable modification of the interaction, whereas $α=0$ corresponds to a standard isothermal process where the system-bath interaction remains constant. Considering heat engines based on such speed-ups, we show that the corresponding efficiency at maximum power interpolates between the Curzon-Ahlborn efficiency for $α=0$ and the Carnot efficiency for $α\to \infty$. Analogous enhancements are obtained for the coefficient of performance of refrigerators. We confirm our analytical results with two numerical examples where $α= 1/2$, namely the time-dependent Caldeira-Leggett and resonant-level models, with strong system-environment correlations taken fully into account. We highlight the possibility of implementing our proposed speed-ups with ultracold atomic impurities and mesoscopic electronic devices.

preprint2016arXiv

Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions

We put forward a strategy to encode a quantum operation into the unmodulated dynamics of a quantum network without the need of external control pulses, measurements or active feedback. Our optimization scheme, inspired by supervised machine learning, consists in engineering the pairwise couplings between the network qubits so that the target quantum operation is encoded in the natural reduced dynamics of a network section. The efficacy of the proposed scheme is demonstrated by the finding of uncontrolled four-qubit networks that implement either the Toffoli gate, the Fredkin gate, or remote logic operations. The proposed Toffoli gate is stable against imperfections, has a high-fidelity for fault tolerant quantum computation, and is fast, being based on the non-equilibrium dynamics.