Tootfinder

#Schwäne auf einem #Binnengewässer an der #Ostsee. Ich müsste echt mal wieder ans Wasser fahren. Wobei ich eher für

The first record of the parable commonly known as the "Blind Men and the Elephant" showed up around 2500 years ago, in a Buddhist text from India. At this point it's pretty commonly known. I assume that most folks reading this will be familiar with some variation of it.
If not, it's essentially a story about people who have limited information making assertions about something: one man grabs a tail and says, "it's like a rope," another the trunk and says, "it's like a snake," a third the leg saying, "it's like a tree," and so on. Later variations are less kind to the men, having them not simply report their findings, but fight over them. The first English translation draws a parallel to religion.
This comes to mind for me, because social media has a tendency to incentivize us to make those hard assertions, to dig in, to argue our perspective as part of a spectacle. Free and federated social media isn't structurally that different from corporate media, so the incentive models built to maximize engagement by maximizing conflict tend to leak through the similarities in the user experience and the conditioning of other platforms.
I didn't really come here to critique the fediverse, but rather to remind everyone, "no one sees the whole fucking elephant." We are limited and there are necessarily some things so big they will never be individually comprehensible. Any system that relies on us all agreeing cannot possibly respond to those big things.
We can see that concretely if we look at the difference between "democratic unity" and "diversity of tactics." In the first model, everyone either agrees or gets forced to behave as though they agree (at least on some issues). That's how liberal democracy works. Diversity of tactics (which systematically described, for example, in the "St Paul Principles"), on the other hand, let's people align on goals without needing to agree on exactly how to achieve them. The reality being that often there are multiple "correct" paths, and sometimes those paths can only work if multiple paths are taken in unison.
No one sees the whole elephant. We won't even see the elephant later. People will still be arguing about the elephant long after it's gone. But if you want to get rid of the elephant, you're gonna need to accept that you can't comprehend it all and you're gonna have to figure out a way to work together that doesn't require a unified approach.

An iterative Ising decoder for quantum error correction codes
Yuanqi Liu, Weilei Zeng, Peixiang Li, Yantong Liu, Guangyao Huang, Yingwen Liu, Dongyang Wang, Junjie Wu, Lingling Lao
https://arxiv.org/abs/2606.12301 https://arxiv.org/pdf/2606.12301 https://arxiv.org/html/2606.12301
arXiv:2606.12301v1 Announce Type: new
Abstract: The Ising framework maps the decoding problem in quantum error correction onto ground-state optimization of a classical Hamiltonian, in which $X$-$Z$ error correlations enter as cross terms. Under phenomenological depolarizing noise, the exact joint formulation contains up to 8-body interactions for the toric code and 10-body for the $6.6.6$ color code. These high-order terms degrade solver convergence, inflate runtime, and raise the auxiliary spin overhead when embedding into native 2-body Ising hardware. In this work, we propose the iterative low-order decoding (ILOD) algorithm, which alternates between $X$- and $Z$-type sub-Hamiltonians, approximating cross-type correlations through Bayesian priors that reweight each type's couplings using the other type's inferred error configuration. This halves the maximum body count of interaction terms in the Hamiltonian, accelerating the solver, restoring convergence at larger code distances, and reducing the total spin count for 2-body embedding by a factor of $2.5$. For the toric code, ILOD attains a threshold of $4.73%$ versus $4.83%$ for the joint formulation, with the empirical runtime ratio scaling as $(0.81)^d$. For the $6.6.6$ color code, their thresholds agree within statistical uncertainty for small code distances, and ILOD remains convergent for larger distances where the joint formulation fails to converge despite a larger annealing budget.
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