Autoionization-Enhanced Rydberg Dressing by Fast Contaminant Removal

Area of Science:

• Quantum Information Science
• Atomic Physics
• Quantum Computing

Background:

• Rydberg dressing is crucial for generating entanglement in atomic states.
• Collective loss due to blackbody radiation-induced transitions to contaminant Rydberg states limits current techniques.

Purpose of the Study:

• To demonstrate rapid removal of contaminant Rydberg states using autoionization (AI).
• To enhance the lifetime and duty cycle of stroboscopic Rydberg dressing (SRD) for quantum information processing.
• To improve spin-squeezing in atomic qubits using AI-enhanced SRD.

Main Methods:

• Utilized autoionization (AI) transitions in alkaline-earth-like atoms for contaminant removal.
• Integrated AI pulses into a stroboscopic Rydberg dressing (SRD) sequence.
• Applied the AI-enhanced SRD protocol to an array of up to 144 optical clock qubits.

Main Results:

• Achieved an order of magnitude enhancement in Rydberg dressing lifetimes.
• Maintained an order of magnitude larger duty cycle compared to previous methods.
• Demonstrated improved spin-squeezing during early time dressing dynamics.

Conclusions:

• Autoionization effectively removes contaminant Rydberg states, compatible with coherent qubit operations.
• AI-enhanced SRD significantly improves lifetimes and duty cycles, approaching fundamental limits.
• This approach enables previously infeasible Rydberg dressing proposals for quantum technologies.