NEXOUScl’s Post

Quantum hardware is accelerating — but middleware is becoming the bottleneck. Many startups building quantum systems are constrained not by physics, but by a lack of engineering bandwidth to design robust middleware layers. At the same time, AI agents already have a de facto interaction standard: the Model Context Protocol (MCP). MCP isn’t magic. Under the hood, it’s simply structured communication over familiar RESTful APIs — which means it can be implemented as a lightweight Python service, co-located with quantum hardware control systems. This creates a pragmatic opportunity: → Expose quantum capabilities via MCP → Enable immediate compatibility with AI agent ecosystems → Skip building custom orchestration layers from scratch The result: out-of-the-box integration with agentic workflows, faster iteration cycles, and reduced middleware complexity. The quantum stack doesn’t need more abstraction — it needs better interfaces. #quantum #software #computing #ai

Quantum error correction shouldn’t be manual. But today, it is. Most quantum stacks like Qiskit and Q# push error correction into libraries, forcing developers to hand-build syndrome circuits with no compile-time guarantees. So I built something different: QSHL (Quantum Self-Healing Language) A programming language where error correction is built in—not bolted on. What’s different: → Heal Blocks Define error correction once. The compiler generates the full syndrome + correction loop automatically. → Linear Qubit Ownership Prevents use-after-measurement and invalid quantum states at compile time. → Coherence-Aware Compilation If your program exceeds the hardware’s coherence window, it fails before it runs. → Rust-native pipeline No Python overhead. Low-latency classical control for real-time decoding. Results: In simulation, QSHL achieved a 55.6% error “heal rate” at a 5% gate error, completing correction cycles within the coherence window using a Sparse Blossom decoder. We’ve filed a provisional patent on the architecture at Korelis Labs. I’m currently exploring QIR / OpenQASM 3.0 integrations and early pilot opportunities with teams working on real quantum systems. If you’re in the quantum space—let’s talk. #QuantumComputing #QEC #RustLang #DeepTech #KorelisLabs

The OS for the Quantum Age: Takeaways from the Milimo Quantum Architecture The true impact of Milimo isn't just in its 121 Python modules or its 21 agents; it’s in the collapse of the barrier to entry. When the complexity of quantum experimentation is reduced to a natural language dialogue, we enter the era of the autonomous scientist. What happens to the pace of global discovery when every researcher has a private, autonomous quantum laboratory sitting on their desk? We are about to find out... #milimo #quantum

Pivoting from Web3 to Quantum Computing I spent quite a while in Web3, and it was a solid experience. But at some point, I realised I didn't have the curiosity to go deeper into protocol-level details or node internals. I found myself drawn to quantum computing, especially by how it’s set to redefine everything we know about cryptography and the shift felt natural. It’s pretty difficult, unfamiliar, and I don’t fully understand it yet. That’s exactly why I want to work on it. As for the tools I picked Rust because it forces precision. The compiler doesn't let me ignore how memory is managed or how data is structured. It slows me down a bit, but in return, I get a much clearer understanding of what’s going on under the hood. For quantum mechanics, that rigor is exactly what's needed. While Python has great libraries, I chose Rust because it doesn't allow hand-waving. It forces me to be explicit about memory and data structures, which is essential when you're dealing with the mathematical rigor of quantum states. I’m building quantum simulator written from scratch to master quantum mechanics through the rigor of code to put these principles into practice. Key milestones achieved this week: - Implemented a system based on Born’s Rule and cumulative probability distribution for N-qubit systems. - Developed a flexible tensor product implementation supporting both state vectors and gate matrices. - Built a state-to-binary mapping to visualize outcomes (e.g., converting indices to readable states like |01>). - Successfully simulated and statistically verified the Bell State, confirming perfect quantum correlations within the engine. This is the first of my weekly updates on this journey. Stay tuned for more and check out the project on GitHub: 👨🏻💻 https://lnkd.in/dQbSQwE2 #RustLang #QuantumComputing #DeepTech #LearningInPublic #SystemsEngineering

Enzyme performs automatic differentiation (AD) for Julia by using LLVM compilation. That enforces quite disciplined programming. Or Enzyme compiles all your project. In huge and complex neural networks projects that becomes quite the problem. Julia currently cannot compile all program like Golang does. Julia uses JIT compilation. HPC on pinned CPU cores is one nasty topic too. Add in here Enzyme, and you have a problem. Scientists are not experienced programmers. They will fall in the trap for sure. After few years I will release some source code. Currently it is about forking Julia a little as well. Its multi threading needs HPC modification.

Quantum Computing And AI Research:Future Impacts (2026) https://lnkd.in/dHeAMeeE #AI #QuantumComputing #TechTrends #CUDA #Qiskit #Python #DataScience #QuantumAI #HybridComputing #GenerativeAI #Convergence #Technews #AIresearch #LLM #AImpacts #Nvidia #GoogleWillow #QuantinuumHelios #QuEra

Coder Dhruv Rathee but with Mustache. Let me tell you about Qiskit too. Meet Qiskit — an open-source framework by IBM that lets you build and run quantum programs using Python. It’s basically like a Virtual Private Computing (VPC) setup—but with a quantum twist, hence VPQC (Virtual Private Quantum Computing). One more It’s like AI turning Ben into Upgrade—but powered by quantum computing, like Ultimatrix, unlocking the ultimate form of Upgrade. 👉 Why should you care? • Quantum computing is the future (still early, but powerful) • Qiskit makes it accessible for developers like us • You can simulate quantum circuits right from your laptop 👉 What can you do with it? • Learn quantum concepts hands-on • Build quantum circuits • Run experiments on real quantum hardware I’m starting to explore this space 🚀 Anyone else curious about quantum computing? #Qiskit #QuantumComputing #Python #LearningInPublic #Developers

🔐 Secure Message Transmission using Superdense Coding I am pleased to share my recent project on Superdense Coding, a fundamental quantum communication protocol that demonstrates how two classical bits can be transmitted using a single qubit, leveraging the principles of quantum entanglement. 🔬 Project Highlights: Designed and implemented an end-to-end system: Text → Binary → Quantum Encoding → Decoding → Text Developed implementations using multiple quantum frameworks: • PennyLane • Cirq • Qiskit Generated Bell-state entanglement between sender (Alice) and receiver (Bob) Applied quantum gates (Hadamard, Pauli-X, Pauli-Z, CNOT) for encoding and decoding Successfully validated accurate message recovery through simulation 🧠 Key Concepts Applied: Quantum Superposition Quantum Entanglement Quantum Gate Operations Quantum Measurement 📊 Outcome: The system consistently reconstructs the original message after transmission, confirming the correctness of the superdense coding protocol and demonstrating quantum data compression capabilities. 🌍 Significance: This work highlights the potential of quantum communication in enabling: Efficient information transfer Secure communication systems Foundational technologies for the future quantum internet This project provided valuable hands-on experience with multiple quantum computing frameworks and strengthened my understanding of quantum information processing. git link: https://lnkd.in/gh2wYxgK #QpiAI #QuantumComputing #QuantumCommunication #SuperdenseCoding #Qiskit #Cirq #PennyLane #Python #Research #Technology

When Brute Force Discovers "Alien" Math I was testing a symbolic regression engine. The goal: find the most efficient way to compute the Fibonacci sequence using only raw CPU primitives. As a human, I expected the system to converge on standard addition: Fn = Fn-1 + Fn-2. The machine had a different idea. Instead of traditional arithmetic, the top-performing "logic" the system discovered was a ROR 10 (Circular Bit Rotation). Why is this fascinating? We are taught that Fibonacci is about addition and the Golden Ratio 1.618. But in a 64-bit register, the machine found a "bit-hack" where rotating the binary structure of a number by 10 positions produced a closer approximation to the next Fibonacci step than a simple constant addition. The Takeaway: We often force AI to mimic human thought patterns. But when you give a system a "compass" and let it brute-force the optimal math, it finds shortcuts that are: - Hardware-native: Rotation is a single-clock cycle operation. - Counter-intuitive: No textbook would ever suggest bit-rotation for Fibonacci. - Alien: It’s a purely binary logic that only "makes sense" to a processor. This is the beauty of low-level engineering. Sometimes the most "optimal" math isn't the one we were taught in school - it’s the one hidden in the architecture of the chip. Feel free to share you opinion in comments #LowLevel #SoftwareArchitecture #RustLang #Algorithms #Computing #BitHacking

From Submarines to Silicon: Engineering the Deep. 🌊⚙️ During my time on the USS Seawolf, I learned that operating in the deep ocean is all about managing extreme constraints. Today, I apply that exact same principle to Edge AI. I generated this mechanical whale locally on my own hardware using RealVisXL. But the real story isn't the image—it's the infrastructure running silently in the background to make it happen. My personal AI ecosystem, "Clair," runs entirely locally. The challenge? A hard 20GB VRAM ceiling. Running a heavy Large Language Model (Ollama) concurrently with high-fidelity image generation is a guaranteed recipe for an Out of Memory (OOM) crash on consumer hardware. To solve this, I engineered a custom Python arbitration system I call the "Traffic Cop." Here is the technical breakdown of how it works: The Intercept: When a render request hits the server, the system enforces a global lock (is_gpu_busy = True), pausing all concurrent LLM chat requests. The Purge: It fires an API call ({"keep_alive": 0}) to Ollama, instantly evicting the LLM from memory and freeing up ~6GB of VRAM. The Render: RealVisXL takes over the fully cleared runway, generating the image without bottlenecking. The Recovery: The lock releases, the LLM reloads in 1-2 seconds, and the system returns to normal operations. Combined with negative Nice values via Linux systemd to prioritize the AI over host OS tasks, the system is completely autonomous and self-healing. Whether you are tracking sonar contacts or orchestrating VRAM, the mission is the same: build resilient systems that don't fail when the pressure is on. What is the most creative workaround you've engineered to bypass a hardware limitation? Let me know below! 👇 #EdgeAI #SystemsEngineering #DevOps #Python #LocalLLM #NavyVeteran #TechTransition #Linux #VRAM