Simple machine learning techniques can cut costs for quantum error mitigation while maintaining accuracy

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Recent research highlights the potential of machine learning (ML) techniques to significantly reduce costs associated with quantum error mitigation (QEM) while maintaining accuracy. By training models on noisy expectation values from quantum circuits, researchers demonstrated that simpler ML models, like random forests, can effectively predict noise-free outcomes. This approach not only streamlines the error mitigation process but also minimizes the need for additional mitigation circuits, thus reducing runtime overheads.

The study conducted by IBM Quantum showcases how ML can tackle the challenges posed by quantum noise, which has historically hindered the performance of quantum computers. The findings indicate that ML techniques can achieve comparable accuracy to traditional QEM methods at a fraction of the cost, making complex quantum experiments more accessible. This innovative approach opens new avenues for integrating AI into quantum computing, potentially transforming the landscape of quantum error mitigation.

Key AI Highlights in this Article

• ML techniques can reduce quantum error mitigation costs while maintaining accuracy.

• Random forest models outperform complex models in quantum error mitigation tasks.

Key AI Terms Mentioned in this Article

Quantum Error Mitigation (QEM)

QEM refers to techniques aimed at reducing errors in quantum computations, crucial for reliable quantum computing.

Machine Learning (ML)

ML involves algorithms that enable computers to learn from data, applied here to enhance quantum error mitigation.

Random Forest

Random forest is a machine learning model that proved effective in predicting noise-free outcomes in quantum circuits.

Companies Mentioned in this Article

IBM Quantum

IBM Quantum focuses on advancing quantum computing technologies and has pioneered the use of ML for error mitigation.

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