Quantum Engineering of Nonlinear Matter-Wave Information

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 robust control proto-
cols under realistic experimental imperfections; apply ex-
plainable AI to extract interpretable physical strategies
from nonlinear quantum dynamics.
WP4: Towards Adaptive Quantum Technologies. Ap-
ply the framework to quantum memories, atomtronic de-
vices, adaptive quantum sensors and matter-wave inter-
ferometry, using AI to improve robustness against deco-
herence and parameter drift and to enable autonomous
calibration.
2
IV. EXPECTED SCIENTIFIC IMPACT
This project reframes nonlinear matter-wave excita-
tions as active carriers of quantum information rather
than passive manifestations of nonlinear many-body
physics. Beyond advancing the fundamental under-
standing 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 intel-
ligence, with applications to adaptive quantum control,
programmable atomtronic architectures and AI-assisted
quantum sensing.
[1] J. Denschlag et al., Generating Solitons by Phase Engi-
neering of a Bose–Einstein Condensate, Science 287, 97
(2000).
[2] C. Becker et al., Oscillations and interactions of dark
and dark–bright solitons in Bose–Einstein condensates,
Nature Physics 4, 496 (2008).
[3] M. I. Shaukat, E. V. Castro, and H. Terças, Quantum
dark soliton qubits in Bose–Einstein condensates, Phys.
Rev. A 95, 053618 (2017).
[4] S. M. Mossman et al., Observation of dense collisional
soliton complexes in a two-component Bose–Einstein con-
densate, Commun. Phys. 7, 163 (2024).
[5] L.-Z. Meng, L.-C. Zhao, T. Busch, and Y. Zhang, Con-
trolling dark solitons on the healing length scale, J. Phys.
B 57, 145302 (2024).
[6] E. Dionis, B. Peaudecerf, S. Guérin, D. Guéry-Odelin,
and D. Sugny, Optimal control of a Bose–Einstein con-
densate in an optical lattice, Front. Quantum Sci. Tech-
nol. 4, 1540695 (2025).
[7] M. Bukov et al., Reinforcement Learning in Different
Phases of Quantum Control, Phys. Rev. X 8, 031086
(2018).
[8] J. Biamonte et al., Quantum Machine Learning, Nature
549, 195 (2017).
[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, 485701 (2024).
DOI: 10.1088/1751-8121/ad8d93
[10] L. Delisle and A. Jaouadi, Symmetry-Driven Multi-
Soliton Dynamics in Bose–Einstein Condensates in Re-
duced Dimensions, Symmetry 17, 582 (2025).

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

La thèse aura lieu au sein du laboratoire LyRIDS en collbaoration avec  l'université Paris Sacaly. 

Master II en physique quantique, chimie quantique ou informatique.