arXiv subjects

Louis-Paul Henry

Publications and source records attributed to Louis-Paul Henry.

At least 19 recordsLinked to original sources

arXiv2026

On the Expressive Power of the Transverse-Field Ising Model for Graph Learning

We study the quantum evolution induced by graph-indexed Ising Hamiltonians as a source of structural signal for graph learning. Graph automorphisms preserve symmetries of the Hamiltonian, and these symmetries constrain the quantum evolution in a way that turns time-dependent local measurements into informative probes of graph structure. Leveraging this idea, we introduce QDAGer, a quantum-inspired graph-pair Transformer that injects quantum-dynamical features from time series of node occupations and connected two-point correlators directly into the attention mechanism. We apply QDAGer to learning Graph Edit Distance (GED), an NP-hard similarity measure, using either a direct permutation-invariant embedding discrepancy or an alignment-based surrogate loss. Experiments on multiple GED benchmarks under different edit cost settings show that the proposed dynamical features provide a stronger inductive bias than classical structural alternatives under the same training protocol. In addition, we report ablations where the dynamical signal is replaced by standard random-walk and heat-kernel features while keeping the architecture fixed, highlighting that the gain comes from the injected dynamics rather than model capacity alone.

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arXiv2026

Satellite Mission Planning with Rydberg Atoms

Quantum computers relying on cold atoms are being built and promise a high flexibility in the way information in encoded into the physical system. In particular, the analog mode is spiking interest in the field of optimization as a classically intractable number of configurations can be tackled. In this work, we investigate a problem that requires every-day scheduling of critical tasks involving a large number of actors. Namely, fixing the planning for a Earth Observation satellite fleet composed of several of units exposed to a high density of targets to be scanned. We explore numerical schemes that convert the formulated problem into a cold-atoms friendly setup. We begin by a naive formulation of the Satellite Mission Planning problem without taking account for the agility of the satellites. We then extend the problem to take it into account based on the literature. By formulating the planning problem as a Maximum Independent Set problem, we are able to solve the problem with a QPU based on Rydberg atoms. We explore two ways of solving the MIS problem on the QPU, one relying on the graphs and on the Quadratic Unconstrained Binary Optimization Framework (QUBO). We show that the QUBO methodology is the most relevant and explore it more deeply with numerical experiments. We conclude on the potential utility of using a QPU to solve the Satellite Mission Planning problem in an operational context.

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arXiv2025

Local Rydberg blockade regimes for disk graph embedding and quantum optimization

Rydberg atom arrays are a powerful platform for solving combinatorial optimization problems, owing to the Rydberg blockade mechanism, which imposes effective constraints on simultaneous atomic excitations. These constraints have enabled the encoding of the Maximum Independent Set (MIS) problem on unit disk graphs, where atoms interact within a fixed, globally defined blockade radius. However, this restriction limits the class of addressable problems. A natural extension is to consider disk graphs, which generalize unit disk graphs by allowing arbitrary disk radii and correspond to the intersection graphs of disks in the plane. Embedding such graphs in Rydberg systems requires moving beyond the standard, globally uniform blockade model. In this work, we introduce a local Rydberg blockade regime, which emerges when local drives are applied to different pairs of atoms involved in a potential interaction. We develop a general theoretical framework for this regime and propose two novel metrics, the correlation matrix and the maximum independence violation, to quantify the quality of the embedding. Using these metrics, we demonstrate that disk graphs can be meaningfully embedded into Rydberg atom arrays under local drive schemes, thereby expanding the landscape of quantum-addressable optimization problems. Finally, when evaluating approximate solutions of the MIS problem, characterized by near-optimal independent sets, local drive approaches exhibit significantly improved performance over global ones. These results highlight the practical advantage of local blockade engineering for approximate combinatorial optimization and open a path toward leveraging the analog capabilities of Rydberg platforms beyond conventional geometric constraints.

quant-ph↗

arXiv2025

Experimental differentiation and extremization with analog quantum circuits

Solving and optimizing differential equations (DEs) is ubiquitous in both engineering and fundamental science. The promise of quantum architectures to accelerate scientific computing thus naturally involved interest towards how efficiently quantum algorithms can solve DEs. Differentiable quantum circuits (DQC) offer a viable route to compute DE solutions using a variational approach amenable to existing quantum computers, by producing a machine-learnable surrogate of the solution. Quantum extremal learning (QEL) complements such approach by finding extreme points in the output of learnable models of unknown (implicit) functions, offering a powerful tool to bypass a full DE solution, in cases where the crux consists in retrieving solution extrema. In this work, we provide the results from the first experimental demonstration of both DQC and QEL, displaying their performance on a synthetic usecase. Whilst both DQC and QEL are expected to require digital quantum hardware, we successfully challenge this assumption by running a closed-loop instance on a commercial analog quantum computer, based upon neutral atom technology.

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arXiv2025

Multiparticle quantum walks for distinguishing hard graphs

Quantum random walks have been shown to be powerful quantum algorithms for certain tasks on graphs like database searching, quantum simulations etc. In this work we focus on its applications for the graph isomorphism problem. In particular we look at how we can compare multi-particle quantum walks and well known classical WL tests and how quantum walks can be used to distinguish hard graphs like CFI graphs which k-WL tests cannot distinguish. We provide theoretical proofs and empirical results to show that a k-QW with input superposition states distinguishes k-CFI graphs. In addition we also prove that a k-1 QW with localized input states distinguishes k-CFI graphs. We also prove some additional results about strongly regular graphs (SRGs).

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arXiv2024

Quantum Positional Encodings for Graph Neural Networks

In this work, we propose novel families of positional encodings tailored to graph neural networks obtained with quantum computers. These encodings leverage the long-range correlations inherent in quantum systems that arise from mapping the topology of a graph onto interactions between qubits in a quantum computer. Our inspiration stems from the recent advancements in quantum processing units, which offer computational capabilities beyond the reach of classical hardware. We prove that some of these quantum features are theoretically more expressive for certain graphs than the commonly used relative random walk probabilities. Empirically, we show that the performance of state-of-the-art models can be improved on standard benchmarks and large-scale datasets by computing tractable versions of quantum features. Our findings highlight the potential of leveraging quantum computing capabilities to enhance the performance of transformers in handling graph data.

quant-ph↗

arXiv2024

Graph Algorithms with Neutral Atom Quantum Processors

Neutral atom technology has steadily demonstrated significant theoretical and experimental advancements, positioning itself as a front-runner platform for running quantum algorithms. One unique advantage of this technology lies in the ability to reconfigure the geometry of the qubit register, from shot to shot. This unique feature makes possible the native embedding of graph-structured problems at the hardware level, with profound consequences for the resolution of complex optimization and machine learning tasks. By driving qubits, one can generate processed quantum states which retain graph complex properties. These states can then be leveraged to offer direct solutions to problems or as resources in hybrid quantum-classical schemes. In this paper, we review the advancements in quantum algorithms for graph problems running on neutral atom Quantum Processing Units (QPUs), and discuss recently introduced embedding and problem-solving techniques. In addition, we clarify ongoing advancements in hardware, with an emphasis on enhancing the scalability, controllability and computation repetition rate of neutral atom QPUs.

quant-ph↗

arXiv2024

A Rydberg-atom approach to the integer factorization problem

The task of factoring integers poses a significant challenge in modern cryptography, and quantum computing holds the potential to efficiently address this problem compared to classical algorithms. Thus, it is crucial to develop quantum computing algorithms to address this problem. This study introduces a quantum approach that utilizes Rydberg atoms to tackle the factorization problem. Experimental demonstrations are conducted for the factorization of small composite numbers such as $6 = 2 \times 3$, $15 = 3 \times 5$, and $35 = 5 \times 7$. This approach involves employing Rydberg-atom graphs to algorithmically program binary multiplication tables, yielding many-body ground states that represent superpositions of factoring solutions. Subsequently, these states are probed using quantum adiabatic computing. Limitations of this method are discussed, specifically addressing the scalability of current Rydberg quantum computing for the intricate computational problem.

quant-ph↗

arXiv2023

Enhancing Graph Neural Networks with Quantum Computed Encodings

Transformers are increasingly employed for graph data, demonstrating competitive performance in diverse tasks. To incorporate graph information into these models, it is essential to enhance node and edge features with positional encodings. In this work, we propose novel families of positional encodings tailored for graph transformers. These encodings leverage the long-range correlations inherent in quantum systems, which arise from mapping the topology of a graph onto interactions between qubits in a quantum computer. Our inspiration stems from the recent advancements in quantum processing units, which offer computational capabilities beyond the reach of classical hardware. We prove that some of these quantum features are theoretically more expressive for certain graphs than the commonly used relative random walk probabilities. Empirically, we show that the performance of state-of-the-art models can be improved on standard benchmarks and large-scale datasets by computing tractable versions of quantum features. Our findings highlight the potential of leveraging quantum computing capabilities to potentially enhance the performance of transformers in handling graph data.

quant-ph↗

arXiv2023

Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery

We introduce quantum algorithms able to sample equilibrium water solvent molecules configurations within proteins thanks to analog quantum computing. To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model (3D-RISM), an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an anti-ferromagnetic Ising model. Its solution, an NP-hard problem in classical computing, is embodied into a Rydberg atom array Quantum Processing Unit (QPU). Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach (VQA) uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM (Q-3D-RISM) algorithms open a new route towards the application of analog quantum computing in molecular modelling and drug design.

quant-ph↗

arXiv2023

Exploring the impact of graph locality for the resolution of MIS with neutral atom devices

In the past years, many quantum algorithms have been proposed to tackle hard combinatorial problems. In particular, the Maximum Independent Set (MIS) is a known NP-hard problem that can be naturally encoded in Rydberg atom arrays. By representing a graph with an ensemble of neutral atoms one can leverage Rydberg dynamics to naturally encode the constraints and the solution to MIS. However, the classes of graphs that can be directly mapped ``vertex-to-atom" on standard devices with 2D capabilities are currently limited to Unit-Disk graphs. In this setting, the inherent spatial locality of the graphs can be leveraged by classical polynomial-time approximation schemes (PTAS) that guarantee an $ε$-approximate solution. In this work, we build upon recent progress made for using 3D arrangements of atoms to embed more complex classes of graphs. We report experimental and theoretical results which represent important steps towards tackling combinatorial tasks on quantum computers for which no classical efficient $\varepsilon$-approximation scheme exists.

quant-ph↗

arXiv2023

Quantum pricing-based column-generation framework for hard combinatorial problems

In this work, we present a complete hybrid classical-quantum algorithm involving a quantum sampler based on neutral atom platforms. This approach is inspired by classical column generation frameworks developed in the field of Operations Research and shows how quantum procedures can assist classical solvers in addressing hard combinatorial problems. We benchmark our method on the Minimum Vertex Coloring problem and show that the proposed hybrid quantum-classical column generation algorithm can yield good solutions in relatively few iterations. We compare our results with state-of-the-art classical and quantum approaches.

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arXiv2022

Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor

Using a quantum processor to embed and process classical data enables the generation of correlations between variables that are inefficient to represent through classical computation. A fundamental question is whether these correlations could be harnessed to enhance learning performances on real datasets. Here, we report the use of a neutral atom quantum processor comprising up to $32$ qubits to implement machine learning tasks on graph-structured data. To that end, we introduce a quantum feature map to encode the information about graphs in the parameters of a tunable Hamiltonian acting on an array of qubits. Using this tool, we first show that interactions in the quantum system can be used to distinguish non-isomorphic graphs that are locally equivalent. We then realize a toxicity screening experiment, consisting of a binary classification protocol on a biochemistry dataset comprising $286$ molecules of sizes ranging from $2$ to $32$ nodes, and obtain results which are comparable to those using the best classical kernels. Using techniques to compare the geometry of the feature spaces associated with kernel methods, we then show evidence that the quantum feature map perceives data in an original way, which is hard to replicate using classical kernels.

quant-ph↗

arXiv2022

Emergent XY* transition driven by symmetry fractionalization and anyon condensation

Anyons in a topologically ordered phase can carry fractional quantum numbers with respect to the symmetry group of the considered system, one example being the fractional charge of the quasiparticles in the fractional quantum Hall effect. When such symmetry-fractionalized anyons condense, the resulting phase must spontaneously break the symmetry and display a local order parameter. In this paper, we study the phase diagram and anyon condensation transitions of a $\mathbb{Z}_2$ topological order perturbed by Ising interactions in the Toric Code. The interplay between the global Ising symmetry and the lattice space group symmetries results in a non-trivial symmetry fractionalization class for the anyons, and is shown to lead to two characteristically different symmetry-broken phases. To understand the anyon condensation transitions, we use the recently introduced critical torus energy spectrum technique to identify a line of emergent 2+1D XY* transitions ending at a fine-tuned (Ising$^2$)* critical point. We provide numerical evidence for the occurrence of two symmetry breaking patterns predicted by the specific symmetry fractionalization class of the anyons in the explored phase diagram. In combination with quantum Monte Carlo simulations we measure unusually large critical exponents for the scaling of the correlation function at the anyon condensation transitions, and we identify lines of (weakly) first order transitions in the phase diagram. As an important result, we discuss the phase diagram of a resulting 2+1D Ashkin-Teller model, where we demonstrate that torus spectroscopy is capable of identifying emergent XY/O(2) critical behaviour, thereby solving some longstanding open questions in the domain of the 3D Ashkin-Teller models. To establish the generality of our results, we propose a field theoretical description which is in excellent agreement with the numerical results.

cond-mat.str-el↗

arXiv2022

Efficient protocol for solving combinatorial graph problems on neutral-atom quantum processors

On neutral atom platforms, preparing specific quantum states is usually achieved by pulse shaping, i.e., by optimizing the time-dependence of the Hamiltonian related to the system. This process can be extremely costly, as it requires sampling of the final state in the quantum processor many times. Hence, determining a good pulse, as well as a good embedding, to solve specific combinatorial graph problems is one of the most important bottlenecks of the analog approach. In this work, we propose a novel protocol for solving hard combinatorial graph problems that combines variational analog quantum computing and machine learning. Our numerical simulations show that the proposed protocol can reduce dramatically the number of iterations to be run on the quantum device. Finally, we assess the quality of the proposed approach by estimating the related Q-score, a recently proposed metric aimed at benchmarking QPUs.

quant-ph↗

arXiv2022

Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays

Programmable arrays of hundreds of Rydberg atoms have recently enabled the exploration of remarkable phenomena in many-body quantum physics. In addition, the development of high-fidelity quantum gates are making them promising architectures for the implementation of quantum circuits. We present here \textit{Pulser}, an open-source Python library for programming neutral-atom devices at the pulse level. The low-level nature of Pulser makes it a versatile framework for quantum control both in the digital and analog settings. The library also contains simulation routines for studying and exploring the outcome of pulse sequences for small systems.

quant-ph↗

arXiv2021

Quantum evolution kernel : Machine learning on graphs with programmable arrays of qubits

The rapid development of reliable Quantum Processing Units (QPU) opens up novel computational opportunities for machine learning. Here, we introduce a procedure for measuring the similarity between graph-structured data, based on the time-evolution of a quantum system. By encoding the topology of the input graph in the Hamiltonian of the system, the evolution produces measurement samples that retain key features of the data. We study analytically the procedure and illustrate its versatility in providing links to standard classical approaches. We then show numerically that this scheme performs well compared to standard graph kernels on typical benchmark datasets. Finally, we study the possibility of a concrete implementation on a realistic neutral-atom quantum processor.

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arXiv2020

Programmable quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms

Quantum simulation using synthetic systems is a promising route to solve outstanding quantum many-body problems in regimes where other approaches, including numerical ones, fail. Many platforms are being developed towards this goal, in particular based on trapped ions, superconducting circuits, neutral atoms or molecules. All of which face two key challenges: (i) scaling up the ensemble size, whilst retaining high quality control over the parameters and (ii) certifying the outputs for these large systems. Here, we use programmable arrays of individual atoms trapped in optical tweezers, with interactions controlled by laser-excitation to Rydberg states to implement an iconic many-body problem, the antiferromagnetic 2D transverse field Ising model. We push this platform to an unprecedented regime with up to 196 atoms manipulated with high fidelity. We probe the antiferromagnetic order by dynamically tuning the parameters of the Hamiltonian. We illustrate the versatility of our platform by exploring various system sizes on two qualitatively different geometries, square and triangular arrays. We obtain good agreement with numerical calculations up to a computationally feasible size (around 100 particles). This work demonstrates that our platform can be readily used to address open questions in many-body physics.

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