From Laser Pulses to Algorithms: Engineering Trapped-Ion Quantum Systems

 Trapped-ion platforms are among the highest-performing quantum computing technologies today and have already transitioned into industrial systems. In this talk, I outline how we engineer and operate a trapped-ion platform at Duke as a reliable, application-driven system, and how we translate atomic-physics-level control into a software execution stack that automates scheduling, monitoring, and calibration to enable reproducible experiments at scale. 
In the next part, I will focus on the digital regime, where universal gate sets enable programmable circuits and closed-loop hybrid quantum-classical optimization under finite-shot constraints. This gate-based workflow supports a broad range of applications on the same hardware. I will show how it enables (i) Hamiltonian learning, including a symmetry-protected signature that isolates genuine three-body interactions even in the presence of unknown lower-body terms; (ii) molecular energy estimation using CAFQA-initialized variational quantum eigensolver, reducing the amount of on-hardware variational tuning needed; and (iii) quantum machine learning methods that leverage the structure of Hilbert space to learn useful representations from data.  Finally, in contrast to the digital regime, I discuss the analog regime, where we program the device by engineering an effective Hamiltonian and using the ions’ native interactions directly.