Quantum Engineering of Nonlinear Matter-Wave Information

Descrizione

Testo originale dell'annuncio (in inglese)

Bose–Einstein condensates (BECs) support long-lived, highly tunable nonlinear collective excita- tions — solitons, vortices, quantum droplets and topological textures — that remain largely unex- ploited 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 — superconduct- ing circuits, trapped ions, colour centres — rely on iso- lated microscopic systems whose scaling is fundamen- tally limited by decoherence and control overhead. Ul- tracold atomic gases offer a distinct route: BECs sus- tain nonlinear collective excitations with strong coher- ence 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 In- formation Engineering, in which nonlinear excitations are treated as programmable quantum information carriers, and AI serves not only to optimize control but to iden- tify the physical principles governing nonlinear quantum dynamics. Matter-wave solitons were first generated by phase en- gineering 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 di- rect 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 par- allel, AI methods — reinforcement learning and neural- network-based optimization — have transformed quan- tum 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 informa- tion theory and AI-driven control for matter-wave plat- forms; this gap defines the scientific opportunity ad- 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 coher- ence, 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 informa- tion. 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 — local- ized 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 Dy- namics. Determine whether soliton collisions, nonlin- ear phase shifts and symmetry-driven interactions can themselves realize quantum functionalities — state trans- fer, entanglement generation, programmable logic — and identify dynamical principles that generalize across phys- ical realizations. WP3: AI for Autonomous Quantum Control. Com- bine reinforcement learning, optimal control and Bayesian optimization to discover ro