Alternatives
Products that do what Open-Source Quantum Solver for Maximum Independent Set Problems does
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…
- 1QJ
2020 · quantumjavascript.app
- 2QS
2016 · github.com
- 3
- 4
Q3AS, deployment & execution of quantum algorithms by Aqora
2024
- 5HA
2018 · github.com
- 6LQ
2013 · qrand-6081.onmodulus.net
- 7TF
2019 · madeddu.xyz
- 8DO
I’ve developed a new public-key encryption scheme (DIAC) that uses multidimensional, high-precision complex keyspaces and a modular trapdoor function for ultra-high entropy and quantum resistance. The code, benchmarks, and research paper (PDF) are all open-source: https://osf.io/mvkcq/ Would love feedback, questions, or cryptanalysis!
2025 · osf.io
- 9PC
2024 · pqc.club
- 10IO
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
- 11LQ
2018 · demo.quantumlab.cc
- 12CQ
Nov 2025 · github.com
- 13PA
Hello Hacker News! I am Bertrand from Pruna AI. With my associates, John, Rayan, and Stephan, we are fellow researchers in AI efficiency and reliability coming from TUM. We are building an optimization engine that combines compression methods (e.g. quantization, pruning, compilation, batching…) in the aim of saving compute power when running AI models. This optimization engine take one base model as input and returns a compressed model as output. It aims to help for two things: - Make various AI models faster and/or smaller for various hardware (because they can require significant…
2024
- 14KP
2019 · qucontrol.github.io
- 15SA
Hi everyone, I’m a student with a strong interest in computer science and complexity theory. Recently, I worked on a manuscript attempting to prove that P ≠ NP. I know how this sounds — it’s one of the hardest and most debated problems in CS, and many have tried and failed. I don’t claim to have the final answer, but I believe the approach I used might at least offer some fresh perspective or provoke useful critique. The idea involves geometric separation between deterministic and nondeterministic computation, using high-dimensional lattice constructions and some physics-inspired intuition.…
2025 · zenodo.org
- 16E0
2017 · github.com
- 17SA
Hey HN, My co-author and I just published a paper on arXiv that formalises a framework we've been using successfully in probabilistic logic for many years. We decided it was time to properly lay out its foundations for classical propositional logic. We cast propositional logic into a formal algebra where logical formulas are represented by sparse matrices. State Algebra isn't an algorithm in itself, but rather a language for manipulating Boolean functions. It provides a new set of tools that lets you express and reformulate existing optimization heuristics (like those from modern SAT…
Sep 2025 · arxiv.org
- 18QM
2020 · iris.entropicalabs.io
- 19
Aggregator of free resources to get started in quantum
Aug 2026 · freequantumcomputing.com
- 20S1
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
- 21AO
Hey hackers, the world needs more AI researchers with good taste, and hardcore software folks have some of the best. Many software friends mentioned they learn better from implementations than from papers, but existing open-source examples rarely go beyond basic nanoGPT-level demos. To help bridge that gap, I spent the last two months full-time reimplementing and open-sourcing a self-contained implementation of every major modern deep learning technique from scratch. The result is beyond-nanoGPT, containing 20k+ lines of handcrafted, minimal, and extensively annotated PyTorch code. I'd love…
2025 · github.com
- 22R0
I built a tiny physics solver LLM that performs surprisingly well on easy-to-medium difficulty physics problems. Most LLMs today still struggle with physics QA (as PhyBench recently highlighted), so I wanted to see how far I could push a small model with careful data and minimal compute. Model: Qwen3-1.7B Supervised Finetuning: ~1500 curated examples spanning kinematics, EM, acoustics, and more RL Fine-tuning: GRPO, 1-shot RLVR style (single example, 70 steps) Total cost: ~$5 on H100 It started with a cold-start SFT (~3 epochs, loss to 0.3), then I ran RL with accuracy reward that climbed…
2025 · huggingface.co
- 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
- 24MO
If AI got lucky or not, AI is surely going to assist in solving serious math problems. If you want to spend your resources/weekends contributing, this repository is full of math problems where you can add your thoughts (or proofs - for you Good Will Hunting type people) for others to build on it and we all together can try to change the world of math. I have already extended the research with manual proof of Jacobian Conjecture that was posted on Twitter (with attribution).
Jul 2026 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →