Macroscopic Noise Amplification by Asymmetric Dyads in Non-Hermitian Optical Systems for Generative Diffusion Models

Area of Science:

• Non-Hermitian optics
• Quantum information science
• Machine learning hardware

Background:

• Non-Hermitian systems exhibit unique phenomena like noise amplification.
• Asymmetric dyads in optical systems present opportunities for novel functionalities.
• Current digital machine learning models face limitations in speed and energy consumption.

Purpose of the Study:

• To investigate noise amplification in asymmetric dyads within non-Hermitian optical systems.
• To explore the potential of these systems for applications in sensing and random number generation.
• To propose the use of integrated light emission for analog all-optical degenerative diffusion models.

Main Methods:

• Theoretical study of noise amplification in freely expanding non-Hermitian optical systems.
• Analysis of modifications to pumping strengths to counteract hardware bias.
• Investigation of coupling effects between dyads on statistical distributions.

Main Results:

• Pumping strength modifications can effectively counteract system hardware bias.
• Couplings between dyads result in nonuniform statistical distributions.
• Asymmetric non-Hermitian dyads demonstrate potential for efficient sensors and ultrafast random number generators.

Conclusions:

• Asymmetric non-Hermitian dyads are promising for advanced technological applications.
• Integrated light emission offers a pathway for efficient analog all-optical machine learning.
• This research could overcome digital limitations in machine learning processing speed and energy use.