Gix Internet Reports Quantum X Labs Milestones with NVIDIA, IBM

Gix Internet reports that Quantum X Labs’ quantum error-correction decoder achieved 12 to 13 percent fewer logical errors when tested against Google’s publicly released quantum hardware data using the NVIDIA CUDA-Q platform. The decoder outperformed the platform’s standard PyMatching decoder at code distances 3 and 5, demonstrating its ability to operate within a leading quantum computing software environment. “This milestone demonstrates that QXL’s decoder technology can operate within a leading quantum computing software environment and deliver encouraging results on real experimental data,” said Prof. Nir Sharon, Chief Scientist of Quantum X Labs. Gix holds approximately 13 percent (2,818,585 shares) of Quantum X Labs. QEC Decoder Achieves Error Reduction with NVIDIA CUDA-Q The decoder, now operating on NVIDIA CUDA-Q, demonstrated this improvement during tests on a dataset of 10,000 runs spanning five error-correction rounds, indicating a potential pathway to more reliable quantum computations. At code distance 5, the QXL decoder further reduced logical errors by 12 percent. A separate evaluation, using a 25-round streaming approach, revealed an approximately 6 percent lower logical error rate at distance 3 and a 9 percent reduction at distance 5, suggesting the decoder’s efficacy in real-time error correction pipelines. The company intends to integrate the decoder with an NVIDIA NVQLink connection to facilitate real-time quantum error correction. Beyond decoder performance, Quantum X Labs’ LayerQake technology significantly improved quantum cybersecurity metrics on three IBM quantum processors, ibm_fez, ibm_kingston and ibm_marrakesh. LayerQake reduced measurement bias from approximately 11 percent to below 6 percent across all three processors, a substantial improvement achieved through its problem-independent calibration approach. This calibration method mitigates hardware-induced inaccuracies without requiring problem-specific tailoring, offering a potentially scalable solution for quantum systems. LayerQake demonstrated a 15- to 21-fold reduction in maximum cumulative error during pre-registered, reproducible tests, indicating a robust enhancement in data integrity. This milestone demonstrates that QXL’s decoder technology can operate within a leading quantum computing software environment and deliver encouraging results on real experimental data. Prof. Nir Sharon, Chief Scientist of Quantum X Labs Source: https://www.globenewswire.com/news-release/2026/10/22/3386319/0/en/gix-internet-quantum-x-labs-achieved-key-milestones-with-nvidia-cuda-q-decoder-and-ibm-quantum-hardware.html More like thisQuantum Computing Business NewsSEALSQ’s Moreira to Keynote IQT Quantum + AI 2026Quantum HardwareA digital link speeds up quantum error correction, SEEQC showsQuantum Error CorrectionNVIDIA’s Ising Decoding cuts quantum error correction time on AWSQuantum Computing Business NewsNVIDIA’s CUDA-Q platform joins SUSE’s AI FactoryStay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals. Tags: Ivy Delaney Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing.

For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release. Latest Posts by Ivy Delaney: Aegiq’s quantum computer reduces engineering time with NVIDIA AI October 22, 2026 Multi-mode beams offer a new way to test quantum operations October 22, 2026 Qblox Advances Hybrid Quantum Computing with NVIDIA Integration October 22, 2026