Speaker
Description
The performance of trapped-ion quantum processors is fundamentally limited by noise arising from both control electronics and the trapping environment. While experimental techniques such as Ramsey and Hahn-echo sequences provide important information about noise spectra, translating these measurements into quantitative predictions of errors in trapped-ion quantum operations for a specific ion species, transition, and control scheme remains challenging.
Here, we present IonLab, a modular simulation framework designed to bridge this gap by enabling noise-aware modelling of trapped-ion dynamics at the Hamiltonian level. Unlike general-purpose libraries such as QuTiP, IonLab is specifically designed for trapped-ion systems, enabling realistic simulation of a Yb-171 io qubit under experimentally relevant conditions. The framework incorporates configurable noise channels, experimental parameters, and decoherence mechanisms within a unified architecture.
IonLab supports both spin-only and spin-motion Hamiltonians, enabling the simulation of single- and multi-ion systems. Its flexible and extensible design also allows integration with external trap-modelling tools, providing a pathway to incorporate trap-specific parameters such as mode frequencies, coupling strengths, and field gradients. This facilitates predictive simulations that connect measured noise spectra to observable dynamics, using QuTiP-based master-equation solvers for both spin-only Hamiltonians and spin-motion models in a truncated Fock basis.
We present representative simulations of coherence dynamics and phase accumulation in the presence of frequency-dependent noise, highlighting how low-frequency noise can dominate control infidelity in typical experimental regimes. Future developments will extend the framework toward experimentally relevant control sequences, including benchmarking protocols in trapped-ion systems, providing a route to connect measured gate performance with underlying noise processes in future experiments, while also exploring more efficient numerical approaches to enable simulations of larger Hilbert spaces and longer timescales. In addition, its transparent structure makes it a useful platform for teaching and onboarding new researchers in trapped-ion quantum control and simulation.
| Academic level | PhD student |
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