arXiv subjects

Lucas Lassablière

Publications and source records attributed to Lucas Lassablière.

10 recordsLinked to original sources

arXiv2026

Exploring the Relaxation Landscape of a 2D Quantum Magnet on a 256-Qubit Processor

How quantum matter relaxes far from equilibrium is a central open problem in many-body physics, and one for which analog quantum simulators are well positioned to move from confirming theory to discovering new physics. Here, we use a two-dimensional Rydberg atom array of 256 qubits to map the relaxation landscape of the two-dimensional transverse-field Ising model across its phase diagram. Beyond the expected rapid thermalization, we identify two further regimes. The first is a prethermal regime whose dynamics are governed by an effective XY model. The second, and most unexpected, is a crossover regime characterized by a slowdown in relaxation. This slowdown occurs precisely where state-of-the-art classical tensor-network methods lose control at late times, whereas the quantum simulation remains consistent across system sizes. These results establish Rydberg atom arrays as a platform for scientific discovery in nonequilibrium quantum many-body dynamics.

quant-ph↗

arXiv2026

Noise-aware emulation and cross-device validation of neutral atom analog quantum processing units

Analog quantum processors based on Rydberg atom arrays are a powerful platform for many-body quantum simulation, combinatorial optimization, and graph machine learning. As these devices become increasingly accessible, establishing confidence in their outputs requires predictive models that quantitatively connect microscopic hardware imperfections to empirical results. Here, we present a noise-aware emulation framework that propagates the dominant noise mechanisms throughout the full computation cycle to predict device behavior. We validate the framework by benchmarking two representative protocols, quantum annealing and post-quench dynamics, on three Pasqal quantum processors where classical simulations still provide ground truth. Across all three devices, the measured observables fall within the uncertainty envelopes predicted by the emulator. Beyond reproducing the data, the framework isolates which physical mechanism dominates in each operating regime, provides quantitative guidance for algorithm design and hardware improvements, and establishes a foundation for verifying analog processors in regimes beyond classical reach.

quant-ph↗

arXiv2026

Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor

We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel methods. The algorithm is implemented on an atom-based logical quantum processor, both at the physical and logical levels. We show that the kernel estimated from the logical implementation performs better than its physical counterpart on relevant metrics. We observe how such performance improvement can be traced back to specific noise-induced errors detected by the chosen encoding. We apply the computed quantum kernel to the task of solving differential equations, confirming how the superior performance of a logical quantum kernel is retained also at an end-to-end applicative level. Our findings show that experimental validation of end-to-end protocols can already highlight the positive impact of fault-tolerant implementations despite their higher quantum resource count, and guide application-informed architectural choices.

quant-ph↗

arXiv2026

One-to-one quantum simulation of a frustrated magnet with 256 qubits

Analog quantum simulators offer a powerful microscopic probe of quantum many-body systems, yet have largely been benchmarked against model Hamiltonians rather than real materials. Here, we use a 256-qubit Rydberg simulator to implement the effective Hamiltonian of the frustrated triangular-lattice magnet TmMgGaO$_4$. Simulated magnetization curves agree quantitatively with susceptibility measurements on single crystals, and both platforms consistently determine the antiferromagnetic phase transition. Snapshot-resolved analysis confirms that quantum fluctuations, rather than disorder, govern the intermediate paramagnetic regime. Having established this correspondence, we access non-equilibrium dynamics following a sudden quench, a regime at picosecond material timescales where entanglement growth places the problem beyond classical reach. The simulator reveals thermalization of local observables, demonstrating that analog quantum simulation can reproduce and extend the physics of a real material.

quant-ph↗

arXiv2024

Analog Counterdiabatic Quantum Computing

We propose analog counterdiabatic quantum computing (ACQC) to tackle combinatorial optimization problems on neutral-atom quantum processors. While these devices allow for the use of hundreds of qubits, adiabatic quantum computing struggles with non-adiabatic errors, which are inevitable due to the hardware's restricted coherence time. We design counterdiabatic protocols to circumvent those limitations via ACQC on analog quantum devices with ground-Rydberg qubits. To demonstrate the effectiveness of our paradigm, we experimentally apply it to the maximum independent set (MIS) problem with up to 100 qubits and show an enhancement in the approximation ratio with a short evolution time. We believe ACQC establishes a path toward quantum advantage for a variety of industry use cases.

quant-ph↗

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

Model for two-body collisions between ultracold dipolar molecules around a Förster resonance in an electric field

We propose a one-channel, simple model to describe the dynamics of ultracold dipolar molecules around a Förster resonance. Slightly above a specific electric field, a collisional shielding can take place, suppressing the molecular losses in a gas. The overall description of the quantum physical mechanism comes back to the dynamics on a unique energy surface, which depends on the relative distance and angular approach of the molecules. This surface enables to interpret how the dipole moments of the molecules are induced and interlocked by the electric field and the dipole-dipole interaction during the process, especially when the shielding is triggered. Averaging the relative angular motion over a unique partial wave (the lowest one when the ultracold regime is reached), the model reproduces well the behaviour of the rate coefficients observed experimentally and predicted theoretically [Matsuda et al., Science 370, 1324 (2020); Li et al., Nat. Phys. 17, 1144 (2021)]. This economic model encapsulates the main physics of the quantum process. Therefore, it can be used as an alternative to a full quantum dynamical treatment and is promising for future studies of collisions involving more bodies.

cond-mat.quant-gas↗

arXiv2021

Self-bound dipolar droplets and supersolids in molecular Bose-Einstein condensates

We numerically study the many-body physics of molecular Bose-Einstein condensates with strong dipole-dipole interactions. We observe the formation of self-bound droplets, and explore phase diagrams that feature a variety of exotic supersolid states. In all of these cases, the large and tunable molecular dipole moments enable the study of unexplored regimes and phenomena, including liquid-like density saturation and universal stability scaling laws for droplets, as well as pattern formation and the limits of droplet supersolidity. We discuss a realistic experimental approach to realize both the required collisional stability of the molecular gases and the independent tunability of their contact and dipolar interaction strengths. Our work provides both a blueprint and a benchmark for near-future experiments with bulk molecular Bose-Einstein condensates.

cond-mat.quant-gas↗

arXiv2021

Tuning of dipolar interactions and evaporative cooling in a three-dimensional molecular quantum gas

Ultracold polar molecules possess long-range, anisotropic, and tunable dipolar interactions, providing the opportunities to probe quantum phenomena inaccessible with existing cold gas platforms. However, experimental progress has been hindered by the dominance of two-body loss over elastic interactions, which prevents efficient evaporative cooling. Though recent work has demonstrated controlled interactions by confining molecules to a two-dimensional geometry, a general approach for tuning molecular interactions in a three-dimensional (3D), stable system has been lacking. Here, we demonstrate tunable elastic dipolar interactions in a bulk gas of ultracold 40K87Rb molecules in 3D, facilitated by an electric field-induced shielding resonance which suppresses the reactive loss by a factor of thirty. This improvement in the ratio of elastic to inelastic collisions enables direct thermalization. The thermalization rate depends on the angle between the collisional axis and the dipole orientation controlled by an external electric field, a direct manifestation of the anisotropic dipolar interaction. We achieve evaporative cooling mediated by the dipolar interactions in three dimensions. This work demonstrates full control of a long-lived bulk quantum gas system with tunable long-range interactions, paving the way for the study of collective quantum many-body physics.

cond-mat.quant-gas↗

arXiv2018

Controlling the scattering length of ultracold dipolar molecules

By applying a circularly polarized and slightly blue-detuned microwave field with respect to the first excited rotational state of a dipolar molecule, one can engineer a long-range, shallow potential well in the entrance channel of the two colliding partners. As the applied microwave ac-field is increased, the long-range well becomes deeper and can support a certain numbers of bound states, which in turn bring the value of the molecule-molecule scattering length from a large negative value to a large positive one. We adopt an adimensional approach where the molecules are described by a rescaled rotational constant $\tilde{B} = B/s_{E_3} $ where $s_{E_3}$ is a characteristic dipolar energy. We found that molecules with $\tilde{B} > 10^8$ are immune to any quenching losses when a sufficient ac-field is applied, the ratio elastic to quenching processes can reach values above $10^3$, and that the value and sign of the scattering length can be tuned. The ability to control the molecular scattering length opens the door for a rich, strongly correlated, many-body physics for ultracold molecules, similar than that for ultracold atoms.

cond-mat.quant-gas↗