arXiv subjects

Jérôme F. Gonthier

Publications and source records attributed to Jérôme F. Gonthier.

6 recordsLinked to original sources

arXiv2026

Optimizing ground state preparation protocols with autoresearch

Artificial intelligent language-model based coding agents have significantly changed the way we interact with computers in our day-to-day, as it is common to use them to create, improve, and run programming scripts only using natural language. Agent code updates can be better guided when such programs can be executed and scored automatically rather than judged by human preference. In quantum computing and classical quantum simulation settings, ground-state preparation has a parallel structure: candidate protocols can be ranked by estimated energies and other proxies indicating proper quantum-state convergence. In this work, we study how autoresearch, a code optimization strategy based on coding agents, can be used to optimize hyperparameter choices of different ground-state preparation and sampling protocols, including the variational quantum eigensolver (VQE), density matrix renormalization group (DMRG), and auxiliary-field quantum Monte Carlo (AFQMC). We validate the viability and capacity of this method on simple spin models and molecular Hamiltonians. Across all three settings, the agent mutates simple baselines into complex protocols with improved energy proxies while operating under constrained space-time computational budgets. We conclude with discussions of other quantum routines that support executable scalar scoring, enabling evolutionary coding agents to automate a substantial portion of the protocol-tuning work that would otherwise be required manually.

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arXiv2026

El Agente Cuantico: Automating quantum simulations

Quantum simulation is central to understanding and designing quantum systems across physics and chemistry. Yet it has barriers to access from both computational complexity and computational perspectives, due to the exponential growth of Hilbert space and the complexity of modern software tools. Here we introduce{\cinzel El Agente Cuántico}, a multi-agent AI system that automates quantum-simulation workflows by translating natural-language scientific intent into executed and validated computations across heterogeneous quantum-software frameworks. By reasoning directly over library documentation and APIs, our agentic system dynamically assembles end-to-end simulations spanning state preparation, closed- and open-system dynamics, tensor-network methods, quantum control, quantum error correction, and quantum resource estimation. The developed system unifies traditionally distinct simulation paradigms behind a single natural-language interface. Beyond reducing technical barriers, this approach opens a path toward scalable, adaptive, and increasingly autonomous quantum simulation, enabling faster exploration of physical models, rapid hypothesis testing, and closer integration between theory, simulation, and emerging quantum hardware.

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arXiv2024

Experimental demonstration of Robust Amplitude Estimation on near-term quantum devices for chemistry applications

This study explores hardware implementation of Robust Amplitude Estimation (RAE) on IBM quantum devices, demonstrating its application in quantum chemistry for one- and two-qubit Hamiltonian systems. Known for potentially offering quadratic speedups over traditional methods in estimating expectation values, RAE is evaluated under realistic noisy conditions. Our experiments provide detailed insights into the practical challenges associated with RAE. We achieved a significant reduction in sampling requirements compared to direct measurement techniques. In estimating the ground state energy of the hydrogen molecule, the RAE implementation demonstrated two orders of magnitude better accuracy for the two-qubit experiments and achieved chemical accuracy. These findings reveal its potential to enhance computational efficiencies in quantum chemistry applications despite the inherent limitations posed by hardware noise. We also found that its performance can be adversely impacted by coherent error and device stability and does not always correlate with the average gate error. These results underscore the importance of adapting quantum computational methods to hardware specifics to realize their full potential in practical scenarios.

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arXiv2022

Neural network enhanced measurement efficiency for molecular groundstates

It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state preparation and readout is obtaining physical observables of such states, which are typically estimated using projective measurements on the qubits. At present, measurement data is costly and time-consuming to obtain on any quantum computing architecture, which has significant consequences for the statistical errors of estimators. In this paper, we adapt common neural network models (restricted Boltzmann machines and recurrent neural networks) to learn complex groundstate wavefunctions for several prototypical molecular qubit Hamiltonians from typical measurement data. By relating the accuracy $\varepsilon$ of the reconstructed groundstate energy to the number of measurements, we find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables. This enhancement yields an asymptotic scaling near $\varepsilon^{-1}$ for the model-based approaches, as opposed to $\varepsilon^{-2}$ in the case of classical shadow tomography.

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arXiv2022

Measurements as a roadblock to near-term practical quantum advantage in chemistry: resource analysis

Recent advances in quantum computing devices have brought attention to hybrid quantum-classical algorithms like the Variational Quantum Eigensolver (VQE) as a potential route to practical quantum advantage in chemistry. However, it is not yet clear whether such algorithms, even in the absence of device error, could actually achieve quantum advantage for systems of practical interest. We have performed an exhaustive analysis to estimate the number of qubits and number of measurements required to compute the combustion energies of small organic molecules and related systems to within chemical accuracy of experimental values using VQE. We consider several key modern improvements to VQE, including low-rank factorizations of the Hamiltonian. Our results indicate that although these techniques are useful, they will not be sufficient to achieve practical quantum computational advantage for our molecular set, or for similar molecules. This suggests that novel approaches to operator estimation leveraging quantum coherence, such as Enhanced Likelihood Functions [arxiv:2006.09350, arxiv:2006.09349], may be required.

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arXiv2022

Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation

Quantum chemistry and materials is one of the most promising applications of quantum computing. Yet much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them. Most previous efforts have carried out resource estimations for quantum algorithms run on large-scale fault-tolerant architectures, which include the quantum phase estimation algorithm. In contrast, few have assessed the performance of near-term quantum algorithms, which include the variational quantum eigensolver (VQE) algorithm. Recently, a large-scale benchmark study [Gonthier et al. 2020] found evidence that the performance of the variational quantum eigensolver for a set of industry-relevant molecules may be too inefficient to be of practical use. This motivates the need for developing and assessing methods that improve the efficiency of VQE. In this work, we predict the runtime of the energy estimation subroutine of VQE when using robust amplitude estimation (RAE) to estimate Pauli expectation values. Under conservative assumptions, our resource estimation predicts that RAE can reduce the runtime over the standard estimation method in VQE by one to two orders of magnitude. Despite this improvement, we find that the runtimes are still too large to be practical. These findings motivate two complementary efforts towards quantum advantage: 1) the investigation of more efficient near-term methods for ground state energy estimation and 2) the development of problem instances that are of industrial value and classically challenging, but better suited to quantum computation.

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