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  3. Machine Learning Approach Toward Quantum Error Mitigation For Accurate Molecular Energetics.
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  3. Machine Learning Approach Toward Quantum Error Mitigation For Accurate Molecular Energetics.

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Machine learning approach toward quantum error mitigation for accurate molecular energetics.

Srushti Patil1, Dibyendu Mondal2, Rahul Maitra2,3

  • 1NNF Quantum Computing Programme, Niels Bohr Institute, University of Copenhagen, Blegdamsvej 19, 2200 Copenhagen N, Denmark.

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Summary
This summary is machine-generated.

This study introduces a machine learning approach to reduce noise in quantum computing for chemistry. It significantly improves energy predictions for molecules, making quantum algorithms more practical.

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Area of Science:

  • Quantum Computing
  • Computational Chemistry
  • Machine Learning

Background:

  • Hybrid quantum-classical algorithms are limited by hardware noise, hindering practical applications beyond proof-of-principle.
  • Existing error mitigation (EM) techniques struggle with noisy intermediate-scale quantum (NISQ) devices for complex molecular simulations.

Purpose of the Study:

  • To develop a machine learning (ML) architecture for practical error mitigation (EM) of molecular Hamiltonians.
  • To overcome the limitations of current EM techniques and hardware noise in quantum computing for chemistry.

Main Methods:

  • A graph neural network and regression-based ML architecture was devised for EM.
  • The ML model was trained on shallow sub-circuits using ideal or mitigated expectation values.
  • Hardware connectivity was mapped to a directed graph encoding native gate noise profiles for feature generation.

    • Main Results:

    • The proposed ML architecture achieved orders of magnitude improvement in predicted molecular energies.
    • Demonstrated effectiveness across molecules with varying correlation in their dissociation energy profiles.
    • The method avoids the exponential overhead typically associated with EM techniques.

    Conclusions:

    • This ML-driven EM approach offers a practical pathway for utilizing NISQ devices in molecular simulations.
    • The strategy effectively learns and mitigates hardware noise, enhancing the accuracy of quantum computations.
    • The findings pave the way for more reliable quantum chemistry calculations on current quantum hardware.