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Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

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Provable bounds for noise-free expectation values computed from noisy samples.

Samantha V Barron¹, Daniel J Egger², Elijah Pelofske^3,4

¹IBM Quantum, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.

Nature Computational Science

|November 2, 2024

Summary

This summary is machine-generated.

Noise in quantum computing challenges accurate results. This study quantifies sampling overhead and uses conditional value at risk to bound noise-free values, validated on 127-qubit systems.

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Area of Science:

Quantum Computing
Computational Science

Background:

Quantum computers offer powerful solutions but are limited by noise.
Noise impedes accurate sampling of bit strings, crucial for applications.

Purpose of the Study:

To investigate the impact of noise on quantum computing sampling.
To develop methods for extracting accurate results from noisy quantum computations.
To explore implications for optimization and machine learning algorithms.

Main Methods:

Formal quantification of sampling overhead in noisy quantum computers.
Relating sampling overhead to layer fidelity for performance assessment.
Utilizing conditional value at risk (CVaR) on noisy samples to derive bounds on noise-free expectation values.

Main Results:

A method to quantify sampling overhead and its relation to layer fidelity was established.
Provable bounds on noise-free expectation values were derived using CVaR.
Experimental validation on up to 127-qubit quantum computers showed strong agreement with theoretical predictions.

Conclusions:

The developed methods provide a way to mitigate noise effects in quantum computations.
These findings are applicable to various quantum algorithms, including optimization and machine learning.
The research advances the practical utility of current noisy quantum hardware.