🖥️ New interactive demo: Virtual Qubit Showdown
Bruhat-Tits tree encoding vs surface code grid. Side-by-side comparison under identical noise.
🌳 Ultrametric tree: errors geometrically confined
🔲 Euclidean grid: errors spread freely
Try it: qnfo.github.io/ultrametric-game-of-life
Based on published research validating ultrametric error confinement:
📄 doi.org/10.5281/zenodo.20134944
📄 doi.org/10.5281/zenodo.20154557
Scaling up quantum algorithms to tackle high-impact problems in science and industry requires quantum error correction and fault tolerance. While progress has been made in experimentally realizing error-corrected primitives, the end-to-end execution of logical quantum algorithms using only fault-tolerant (FT) components has remained out of reach. We demonstrate the FT and error-corrected execution of two quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA) and the Harrow-Hassidim-Lloyd (HHL) algorithm applied to the Poisson equation, on Quantinuum H2 and Helios trapped-ion quantum processors using the $[[7,1,3]]$ Steane code. For QAOA circuits on 5 and 6 logical qubits, we show performance improvements from increasing the number of QAOA layers and the number of $T$ gates used to approximate logical rotations, despite increased physical circuit complexity. We further show that QAOA circuits with up to 8 logical qubits and 9 logical $T$ gates perform similarly to unencoded circuits. For the largest QAOA circuits we run, with 12 logical (97 physical) qubits and 2132 physical two-qubit gates, we still observe better-than-random performance. Finally, we show that adding active QEC cycles and increasing the repeat-until-success limit of state preparation subroutines can improve the performance of a quantum algorithm, thereby demonstrating critical capabilities of scalable FT quantum computation. Our results are enabled by an FT logical $T$ gate implementation with an infidelity of $\sim 2.6(4)\times10^{-3}$ and dynamic circuits with measurement-dependent feedback. Our work demonstrates near-break-even performance of complex, error-corrected algorithmic quantum circuits using only FT components.
#IBM affirme avoir éliminé le plus gros obstacle à l'ordinateur quantique : la #gestion des #erreurs #quantiques ( #qec #quantum #error #correction )
Et ça change absolument tout !
Une annonce ambitieuse, certes, mais qui s’appuie sur deux nouvelles études publiées début juin sur le serveur de prépublication #arXiv
"Nothing in fault-tolerance makes sense except in light of ZX calculus" -- Craig Gidney in this very nice talk. 😍
https://www.youtube.com/live/SULOaOQ6Uok?t=558s
Lazy people in quantum computing use the term "fault-tolerant" to mean "I don't want to think about errors". Unfortunately for these magical thinkers, QEC will not make error rates go to zero, except in the asymptotic limit. For those of us who have to live with finite numbers like 7 or 144, logical operations on logical qubits will always have errors. If QEC is working correctly, these errors will be rarer than the physical ones, but also weirder. So you'd better understand them if you want your "fault-tolerant" quantum computer to actually work.
Fortunately my #quantinuum colleagues Matt Girling, Ben Criger, and Cristina Cirstoiu have put the effort in to start understanding a problem that many others don't even realise exists. Check it out:
https://arxiv.org/abs/2508.08188
Characterizing how quantum error correction circuits behave under realistic hardware noise is essential for testing the premises that enable scalable fault tolerance. Logical error rates conditioned on syndrome outcomes are needed to enable noise-aware decoding and validate threshold-relevant assumptions. We introduce a protocol to directly estimate the logical Pauli channels (and pure errors) associated with detector regions formed of two or more syndrome extraction gadgets, conditioned on observing a particular parity in the syndrome outcomes. The method is SPAM-robust and most suitable for flag-based syndrome measurement schemes. For classical processing of the experimental data we implement a Bayesian modelling approach. We validate this new protocol on a small error-detecting code using Quantinuum H1-1, a trapped-ion device, and demonstrate that several noise diagnostic tests for fault tolerance improve significantly when using noise tailoring and mitigation strategies, such as swapped measurements for leakage protection, and Pauli frame randomization.
#ITByte: Susceptibility to errors is the single biggest problem holding back #Quantum #Computing from realizing its great promise. Quantum error correction protocols will play a central role in the realisation of quantum computing.
Here is a brief introduction to Quantum Error Correction (#QEC)
Andrea Barontini
Rowan Brad Quni-Gudzinas
Ross Duncan
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