Alternatives
Products that do what dense-evolution does
High-performance JAX + CuPy NISQ quantum circuit simulator
- 1IV
The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
2024 · github.com
- 2FI
2024 · words.filippo.io
- 3AT
A 3.16M-parameter INT4 transformer running entirely in the on-chip memory of a Xilinx Kria KV260. Zero DRAM in the token loop, 59,965 tok/s on the fabric, bit-exact. Chat with it live.
27d ago · mikeayles.com
- 4TV
May 2026 · github.com
- 5QQ
Apr 2026 · github.com
- 6AF
Oct 2025 · qblaze.org
- 7
- 8
- 9AL
2019 · github.com
- 10
- 11QA
Hi! I built this to teach myself how a quantum circuit simulator works. It's about 1000 lines of Python, aiming to be understandable more than being fast or robust. It's not meant to compete with anything else - just a personal project to really get clear on the under-the-hood theory of qubits and quantum gates.
2024 · github.com
- 12ZP
The model uses a 1024-dimensional complex Hilbert space with 32 layers of programmable Mach–Zehnder meshes (Reck architecture) and derives token probabilities directly via the Born rule. Despite using only unitary operations and no attention mechanism, a 1024×32 model achieves coherent TinyStories generation after < 1.8 hours of training on a single consumer GPU. This is Part 1 - the next step is physical implementation with $50 of optics from AliExpress.
Nov 2025 · zenodo.org
- 13EA
2017 · github.com
- 14CQ
Nov 2025 · github.com
- 15ZC
Zero-Knowledge Proofs (ZKPs) let an untrusted proved show that computation was executed correctly without revealing the inputs to the verifier. However to prove anything, the computation first has to be expressed as a circuit: a system of polynomial equations (constraints) over a finite field. Circuits are the assembly language of zk and every constraint costs prover (and sometimes verifier) time, so production circuits are aggressively hand-optimized. Over the last months, we have been experimenting with writing formal specifications instead and letting LLMs produce the circuits: as long as…
Jul 2026 · zk.golf
- 16OS
We’ve just released an open-source library for solving the Maximum Independent Set (MIS) problem with neutral atom quantum computing, running on both quantum processing units (QPUs) and classical hardware, thanks to emulators. This project is the result of collaboration between Pasqal, academic researchers, and industry partners, aiming to make it practical to experiment with quantum approaches to hard combinatorial optimization tasks. The MIS problem appears in real-world scenarios like scheduling, resource allocation, and network optimization, areas where classical solvers often struggle…
2025
- 17NG
Hi everyone, I started working on nanoeuler after the ban of anthropic's fable because my ambition and dream is to work in the AI field in anthropic. The two interesting reasons that led me to create nanoeuler were (1) interfacing with llm does not mean understanding how they are composed and (2), working on llm with a very low-level layer to understand the correlation between parameters and data and growth of the model and how the GPU works and how some layers can be optimized. So I started working on it with a research aspect by making nanoeuler grow more and more but doing one step after…
Jun 2026 · github.com
- 18HA
2018 · github.com
- 19AT
It is based on phase noise, consumes less than 60 LUT4s/FFs and achieves up to 7.99 bits of entropy per byte. Feel free to comment if you have any questions, ideas or thoughts :)
2023 · github.com
- 20QE
Hi HN, We're proud to announce the release of the Quantum Evolution Kernel! It's still an early version, so feedback is very much welcome. # What is it? Quantum evolution kernel is an open-source library designed for anyone interested in applying quantum computing to graph machine learning -- and you don’t even need a quantum computer in your living room to start using it! It has a wide range of graph machine learning applications, including prediction of molecular toxicity, as shown in the tutorial. # Why is it exciting? Quantum computing has huge potential, but it needs to be accessible…
2025 · github.com
- 21
High performance storage engine for efficient LLM inference and GPU Training.
1d ago · theopenlake.com
- 22FA
2016 · ffftp.site
- 235L
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
- 24
Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI. - roandejager/Hillock
7d ago · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →