Quantum Computing Fidelity Improved via Machine Learning and Error Modelling.

Research demonstrates a refined error mitigation technique for quantum computers, utilising machine learning to improve readout error models. Testing on a simulated seven-qubit system yielded a 6.6% median fidelity improvement, alongside 29.9% and 10.3% reductions in mean-squared error and Hellinger distance respectively, compared to standard approaches.

Quantum computation holds considerable potential across diverse scientific and industrial applications, yet its practical realisation is hampered by the inherent susceptibility of qubits to noise, leading to computational errors. Researchers are actively developing error mitigation techniques to address this challenge, aiming to extract meaningful results from noisy quantum processors. A new approach, detailed in the article ‘Personalized Improvement of Standard Readout Error Mitigation using Low-Depth Circuits and Machine Learning’, focuses on refining the accuracy of readout error models – the process of determining the state of a qubit after computation – by leveraging machine learning and a series of shallow quantum circuits. This work, conducted by Melody Lee from the College of Computing at the Georgia Institute of Technology, demonstrates improvements in fidelity, mean-squared error, and Hellinger distance on a simulated quantum system, bringing nearer the prospect of reliable quantum computation.

Enhancing Quantum Computation via Machine Learning-Driven Error Mitigation

Recent investigations demonstrate improvements in mitigating errors inherent in near-term quantum computers. Qubit susceptibility to noise represents a substantial impediment to realising the potential of quantum algorithms, and current research focuses on refining techniques for numerical readout error mitigation – a crucial step towards more reliable quantum computation. Machine learning is increasingly employed to enhance the accuracy of readout error models, which characterise the probability of incorrectly determining a qubit’s state.

Researchers utilise machine learning algorithms to analyse probability distributions derived from shallow-depth quantum circuits. This analysis enables the prediction and correction of readout errors with greater efficacy. Testing on a simulated seven-qubit system (Perth backend using Qiskit) with a circuit depth of four, the refined method achieved a median fidelity improvement of 6.6% compared to standard error mitigation techniques. Furthermore, the method demonstrated a 29.9% reduction in mean-squared error and a 10.3% improvement in Hellinger distance – a metric quantifying the similarity between two probability distributions.

This work represents a departure from static error models, adapting to the specific characteristics of individual quantum hardware. The field actively investigates and refines techniques for characterising and correcting readout errors, employing methods ranging from matrix inversions and unfolding to machine learning-enhanced modelling. This prioritises a pragmatic approach to improving fidelity and accuracy in the short term.

A significant focus lies on variational quantum algorithms (VQAs) and other algorithms tailored for Noisy Intermediate-Scale Quantum (NISQ) devices. This suggests a strategic emphasis on extracting value from existing hardware while simultaneously developing the algorithmic foundations for future, more powerful quantum computers. The intersection of quantum computing and artificial intelligence is also prominent, with studies exploring the potential of quantum-enhanced machine learning for materials discovery and optimisation problems. This highlights a growing recognition of the synergistic benefits of combining these two technologies.

These results demonstrate the potential of machine learning to dynamically improve error mitigation strategies and continuously refine the readout error model. This unlocks greater computational power from near-term devices and offers the ability to adapt and refine error models in real-time as quantum computers scale up in size and complexity, bringing the field closer to the goal of fault-tolerant quantum computation.