Quantum Engineering of Nonlinear Matter-Wave Information

Organisation/Company ECE - Paris - Ecole d'ingénieurs Research Field Physics Computer science » Digital systems Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 18 Oct 2026 - 22:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 2 Nov 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Bose–Einstein condensates (BECs) support long-lived, highly tunable nonlinear collective excitations — solitons, vortices, quantum droplets and topological textures — that remain largely unexplored as quantum information carriers. This proposal, aims to establish Matter-Wave Information Engineering: a framework in which nonlinear matter-wave excitations are treated as programmable quantum resources, and artificial intelligence (AI) is used not merely to optimize control but to discover the physical principles governing nonlinear quantum dynamics. I. SCIENTIFIC VISION Conventional quantum architectures — superconducting circuits, trapped ions, colour centres — rely on isolated microscopic systems whose scaling is fundamentally limited by decoherence and control overhead. Ultracold atomic gases offer a distinct route: BECs sustain nonlinear collective excitations with strong coherence properties and highly tunable interactions. Building on recent progress in multi-component soliton control, programmable optical potentials and AI-driven quantum control, this project aims to establish Matter-Wave Information Engineering, in which nonlinear excitations are treated as programmable quantum information carriers, and AI serves not only to optimize control but to identify the physical principles governing nonlinear quantum dynamics. Matter-wave solitons were first generated by phase engineering of a BEC (1), and dark, bright and dark–bright solitons are now well characterized in terms of stability and interaction dynamics (2). Shaukat et al. proposed encoding qubits in dark solitons (3), establishing a direct link between nonlinear matter waves and quantum information processing. The recent observation of dense collisional soliton complexes in two-component BECs (4) and advances in optimal control of nonlinear condensate dynamics (6) confirm that programmable multi-soliton architectures are now experimentally accessible. In parallel, AI methods — reinforcement learning and neural-network-based optimization — have transformed quantum control on superconducting, trapped-ion and spin platforms (7, 8), but remain essentially unexplored for nonlinear matter-wave systems. No framework currently unifies nonlinear quantum dynamics, quantum information theory and AI-driven control for matter-wave platforms; this gap defines the scientific opportunity and dressed by this project, building on prior LyRIDS work on optically controlled soliton dynamics (9, 10). II. SCIENTIFIC HYPOTHESIS We hypothesize that nonlinear collective excitations of ultracold quantum matter possess sufficient coherence, robustness and controllability to support a class of quantum information architectures beyond conventional qubit-based platforms. Rather than treating solitons solely as solutions of the Gross–Pitaevskii equation, we propose to engineer them as programmable resources for encoding, transporting and processing quantum information. AI is employed not merely as a numerical optimizer, but as a framework for discovering control protocols and physically interpretable observables governing nonlinear many-body dynamics. III. RESEARCH PROGRAMME WP1: Quantum Information Encoding in Nonlinear Matter Waves. Evaluate encoding strategies — localized solitons, multi-soliton configurations, phase defects, internal spin degrees of freedom — and quantify their coherence, stability and scalability using state fidelity, entanglement entropy and quantum Fisher information. WP2: Quantum Engineering through Nonlinear Dynamics. Determine whether soliton collisions, nonlinear phase shifts and symmetry-driven interactions can themselves realize quantum functionalities — state transfer, entanglement generation, programmable logic — and identify dynamical principles that generalize across physical realizations. WP3: AI for Autonomous Quantum Control. Combine reinforcement learning, optimal control and Bayesian optimization to discover robust control protocols under realistic experimental imperfections; apply explainable AI to extract interpretable physical strategies from nonlinear quantum dynamics. WP4: Towards Adaptive Quantum Technologies. Apply the framework to quantum memories, atomtronic devices, adaptive quantum sensors and matter-wave interferometry, using AI to improve robustness against decoherence and parameter drift and to enable autonomous calibration. IV. EXPECTED SCIENTIFIC IMPACT This project reframes nonlinear matter-wave excitations as active carriers of quantum information rather than passive manifestations of nonlinear many-body physics. Beyond advancing the fundamental understanding of nonlinear quantum systems, it aims to establish Matter-Wave Information Engineering as an interdisciplinary research direction bridging ultracold atoms, quantum information science and artificial intelligence, with applications to adaptive quantum control, programmable atomtronic architectures and AI-assisted quantum sensing. (1) J. Denschlag et al., Generating Solitons by Phase Engineering of a Bose–Einstein Condensate, Science 287, ). (2) C. Becker et al., Oscillations and interactions of dark and dark–bright solitons in Bose–Einstein condensates, Nature Physics 4, ). (3) M. I. Shaukat, E. V. Castro, and H. Terças, Quantum dark soliton qubits in Bose–Einstein condensates, Phys. Rev. A 95, ). (4) S. M. Mossman et al., Observation of dense collisional soliton complexes in a two-component Bose–Einstein condensate, Commun. Phys. 7, ). (5) L.-Z. Meng, L.-C. Zhao, T. Busch, and Y. Zhang, Controlling dark solitons on the healing length scale, J. Phys. B 57, ). (6) E. Dionis, B. Peaudecerf, S. Guérin, D. Guéry-Odelin, and D. Sugny, Optimal control of a Bose–Einstein condensate in an optical lattice, Front. Quantum Sci. Technol. 4, ). (7) M. Bukov et al., Reinforcement Learning in Different Phases of Quantum Control, Phys. Rev. X 8, ). (8) J. Biamonte et al., Quantum Machine Learning, Nature 549, ). (9) E. Célanie, L. Delisle, and A. Jaouadi, Optically tuned soliton dynamics in Bose–Einstein condensates within dark traps, J. Phys. A: Math. Theor. 57, ). DOI: / /ad8d93 (10) L. Delisle and A. Jaouadi, Symmetry-Driven Multi-Soliton Dynamics in Bose–Einstein Condensates in Reduced Dimensions, Symmetry 17, ).

Amine Jaouadi1 LyRIDS, ECE Engineering SchoolParis – OMNES Education, 10 rue Sextius Michel, 75015 Paris, France