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New ML decoder PRISM accelerates quantum computing with low latency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PRISM, a novel machine-learning decoder designed for fusion-based quantum computing. This transformer-based decoder is optimized to handle high erasure rates common in photonic architectures, matching the accuracy of traditional decoders while significantly improving speed. An implementation on an AMD VP1902 FPGA demonstrated real-time decoding capabilities, achieving low latency crucial for fault-tolerant quantum computation. AI

IMPACT This development could significantly speed up fault-tolerant quantum computation by providing faster and more accurate decoding.

RANK_REASON The cluster contains a research paper detailing a new machine-learning decoder for quantum computing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New ML decoder PRISM accelerates quantum computing with low latency

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The cluster contains a research paper detailing a new machine-learning decoder for quantum computing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Daniel Barter, Jacob C. Bridgeman, Chris Dawson, Terry Farrelly, Shane Fleming, Mercedes Gimeno-Segovia, Paul Hartke, Timothy Hein, Sean Howell, Gregory Hyegang Jun, Siddhartha Kamalakara, Yehua Liu, Jose M. Monsalve Diaz, Eddie Richter, Sam Roberts ·

    Machine-learning decoders for fusion-based quantum computing in real time

    arXiv:2611.13630v1 Announce Type: cross Abstract: Fault-tolerant quantum computation requires decoders that combine accuracy, throughput, low latency, and scalability to large numbers of physical qubits. In photonic fusion-based architectures, erasures from photon loss and probab…

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