Quantum

Google Demonstrates Method to Cut Errors in Quantum Computing’s ‘Magic States’

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 16/10/2026, 09:34:24 UTC.

Briefings

Google researchers successfully cultivated magic states on their Willow quantum processor using reinforcement learning, achieving a 40-fold reduction in state-preparation errors and reporting 99.99% logical fidelity. This method, published in Science, addresses a critical resource bottleneck where magic states historically consume up to 95% of a quantum computer's capacity. The experiment involved transferring states between two error-correcting codes but revealed a storage test error seven times higher than simulations predicted, indicating further scaling challenges remain.

Key points

• Researchers used reinforcement learning to calibrate the Willow processor, reducing state-preparation errors by approximately 40-fold.
• The team achieved 99.99% logical fidelity for T-states, a measure of how closely protected quantum states match their intended form.

Context

Magic states are essential resources for quantum operations, but their preparation historically consumes excessive hardware capacity. This breakthrough moves a theoretical method to practical hardware, though larger-scale tests are needed to fully solve resource problems.

Limitations

A storage test produced errors seven times higher than simulations predicted, and the work does not yet establish that the resource problem is solved at a practical scale. Statistical uncertainty exists in repeated experiments, and the method addresses state preparation rather than a fully solved computing architecture.

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