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Related Experiment Video

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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

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Experimental quantum natural gradient optimization in photonics.

Yizhi Wang, Shichuan Xue, Yaxuan Wang

|July 14, 2023

Summary

This summary is machine-generated.

This study demonstrates the first experimental estimation of quantum natural gradient (QNG) on a photonic chip. This optimization method enhances variational quantum algorithms (VQAs) for practical quantum applications.

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Area of Science:

Quantum Computing
Quantum Chemistry
Photonic Quantum Technologies

Background:

Variational quantum algorithms (VQAs) are promising for near-term quantum applications.
VQA performance is critically dependent on the chosen optimization method.
Existing methods like gradient descent can be slow and prone to local minima.

Purpose of the Study:

To experimentally estimate the quantum natural gradient (QNG) using a photonic chip.
To demonstrate the outperformance of QNG optimization for VQAs in a real-world setting.
To validate QNG's potential for practical quantum applications in the NISQ era.

Main Methods:

Utilized a fully programmable photonic chip for quantum computations.
Employed the quantum natural gradient (QNG) optimization technique.
Calculated the dissociation curve of the He-H+ cation.

Main Results:

Achieved the first experimental estimation of QNG in photonics.
Obtained chemical accuracy for the He-H+ dissociation curve.
Demonstrated superior performance of QNG over other optimization methods on a photonic device.

Conclusions:

QNG optimization offers faster convergence and better local minima avoidance for VQAs.
Photonic quantum devices are suitable for implementing advanced QNG methods.
This work paves the way for practical quantum applications using QNG in photonics.