Christian de Correc | Searcharxiv

arXiv2026

Emulation of large-scale qubit registers with a phase-space approach

A phase-space approach is used and benchmarked for the simulation of the continuous-time evolution of large registers of qubits. It is based on a statistical ensemble of independent mean-field trajectories, where mean field is introduced at the level of the qubits, substituting quantum fluctuations/correlations with classical ones. The approach only involves at worse a quadratic cost in the system size, allowing to simulate up to several thousands of qubits on a classical computer. It provides qualitatively accurate description of one-qubit observables evolutions, making it a useful reference in comparison to techniques limited to small qubit numbers. The predictive power is, however, less robust for multi-qubits observables. We benchmark the method on the $k$-local transverse-field Ising model, considering a large variety of systems ranging from local to all-to-all interactions, and from weak to strong coupling regimes, with up to 2000 qubits. To showcase the versatility of the approach, simulations on 2D and 3D Ising models are also made.

quant-ph↗

arXiv2025

Approximate combinatorial optimization with Rydberg atoms: the barrier of interpretability

Analog quantum computing with Rydberg atoms is seen as an avenue to solve hard graph optimization problems, because they naturally encode the Maximum Independent Set (MIS) problem on Unit-Disk (UD) graphs, a problem that admits rather efficient approximation schemes on classical computers. Going beyond UD-MIS to address generic graphs requires embedding schemes, typically with chains of ancilla atoms, and an interpretation algorithm to map results back to the original problem. However, interpreting approximate solutions obtained with realistic quantum computers proves to be a difficult problem. As a case study, we evaluate the ability of two interpretation strategies to correct errors in the recently introduced Crossing Lattice embedding. We find that one strategy, based on finding the closest embedding solution, leads to very high qualities, albeit at an exponential cost. The second strategy, based on ignoring defective regions of the embedding graph, is polynomial in the graph size, but it leads to a degradation of the solution quality which is prohibitive under realistic assumptions on the defect generation. Moreover, more favorable defect scalings lead to a contradiction with well-known approximability conjectures. Therefore, it is unlikely that a scalable and generic improvement in solution quality can be achieved with Rydberg platforms -- thus moving the focus to heuristic algorithms.

quant-ph↗