Programmable time-multiplexed squeezed light source
Hiroko Tomoda, Takato Yoshida, Takahiro Kashiwazaki, Takeshi Umeki,, Yutaro Enomoto, Shuntaro Takeda

TL;DR
This paper presents a programmable, software-controlled time-multiplexed squeezed light source capable of producing sequential pulses with variable squeezing levels and phases at high speed, advancing large-scale continuous-variable quantum information processing.
Contribution
The authors develop a flexible, programmable squeezed light source using a waveguide optical parametric amplifier and pump modulation, enabling arbitrary pulse patterns without hardware changes.
Findings
Successfully generated variable squeezing levels and phases
Achieved pulse timing below 100 ns
Demonstrated software-controlled arbitrary pattern generation
Abstract
One of the leading approaches to large-scale quantum information processing (QIP) is the continuous-variable (CV) scheme based on time multiplexing (TM). As a fundamental building block for this approach, quantum light sources to sequentially produce time-multiplexed squeezed-light pulses are required; however, conventional CV TM experiments have used fixed light sources that can only output the squeezed pulses with the same squeezing levels and phases. We here demonstrate a programmable time-multiplexed squeezed light source that can generate sequential squeezed pulses with various squeezing levels and phases at a time interval below 100 ns. The generation pattern can be arbitrarily chosen by software without changing its hardware configuration. This is enabled by using a waveguide optical parametric amplifier and modulating its continuous pump light. Our light source will implement…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptical Network Technologies · Neural Networks and Reservoir Computing · Photonic and Optical Devices
