Emulating the Deutsch-Josza algorithm with an inverse-designed terahertz gradient-index lens

Area of Science:

• Photonics
• Quantum Computing
• Metamaterials

Background:

• Quantum algorithm emulation requires specialized hardware.
• Photonic devices offer potential for quantum information processing.
• Terahertz frequency regime presents unique opportunities for optical control.

Purpose of the Study:

• To investigate an all-dielectric photonic metastructure as a quantum algorithm emulator (QAE).
• To demonstrate the implementation of the Deutsch-Jozsa algorithm using this QAE.
• To optimize the QAE design for enhanced light-metamaterial interaction.

Main Methods:

• Designed a QAE using a gradient-index (GRIN) lens for Fourier transforms and patterned silicon for the oracle.
• Optimized the GRIN lens through numerical analysis.
• Employed inverse design with machine learning to refine the structural geometry.

Main Results:

• Successfully implemented the Deutsch-Jozsa algorithm within the terahertz regime.
• Achieved spectral improvements in the outgoing wave, enhancing light-metamaterial interaction.
• Demonstrated the efficacy of machine learning in optimizing photonic metastructure design.

Conclusions:

• All-dielectric photonic metastructures are viable for quantum algorithm emulation.
• The proposed GRIN lens and patterned silicon design effectively implements quantum algorithms.
• Machine learning-driven inverse design is a powerful tool for optimizing photonic devices for quantum applications.