I am pleased to announce the publication of my first arXiv preprint: "Accelerating Quantum State Encoding with SIMD: Design, Implementation, and Benchmarking." Efficient data encoding has long been a bottleneck in hybrid quantum-classical algorithms, with traditional simulators spending significant time converting classical features into quantum rotations. To address this, our team developed the Hybriqu Encoder, a Rust-based, SIMD-aware kernel that focuses exclusively on angle encoding and integrates seamlessly with Python via CFFI. By processing multiple double-precision rotations simultaneously using AVX-class vector lanes, utilizing pre-calculated trigonometric factors, and optimizing for cache performance, we achieved a 5.4% speedup at 64 qubits on Apple Silicon. This performance scales effectively as data exceeds the L1 cache size, demonstrating that vectorization provides consistent improvements for compute-bound encoding tasks. Our benchmarking also identified that kernels applying rotations to the entire state vector remain heavily limited by memory bandwidth rather than compute power. This highlights a clear path for future optimization regarding state updates and processing methods in larger hybrid quantum-classical systems. A huge thank you to my co-authors Irwan Alnarus Kautsar, Gunawan Witjaksono, and Haza Nuzly Bin Abdull Hamed for their collaboration and support on this work. You can read the full preprint here: https://lnkd.in/gKMNt57B #QuantumComputing #MachineLearning #RustLang #HighPerformanceComputing #SIMD #QuantumSimulation #ArXiv

IonQ published a very good and interesting THEORETICAL RESEARCH paper regarding Shor's algorithm. Congrats to Chris Ballance and his #quantumcomputing team. Before people start claiming that Q-Day is tomorrow, I want to point out a few things. The company doesn't have anything close to the 13,000 qubits to factor 1,071,514,531 in less than a day. Moreover, while this is a big jump for what quantum computers can do today with factoring, that number is trivial to factor classically on a desktop machine: Number: 1,071,514,531 Factors: {32719: 1, 32749: 1} Time elapsed: 0.000650 seconds The Python code and a link to the IonQ paper are in the comments. So, this is solid quantum architectural progress with reasonable performance estimates, but RSA and ECCC are safe for a bit longer. Marin Ivezic

Quantum computers don’t run code. They run circuits. That’s the first mental shift you need to make. In classical computing, we write programs step by step instructions executed sequentially in languages like Python or C++. Quantum computing works differently. Instead of code, you design a quantum circuit a sequence of quantum gates applied to qubits. Each gate doesn’t “compute” in the classical sense. It transforms the state of a qubit shaping probabilities, not flipping bits. You’re not telling the computer what to do step by step. You’re constructing a system that evolves according to quantum mechanics. And at the end of the circuit? You measure. That’s when probabilities collapse into an actual result. This is why quantum programming feels less like coding… and more like designing an experiment. From instructions → to transformations From logic → to physics This is Day 2 of understanding how quantum computers actually compute. Next: What exactly is a quantum gate? #QuantumComputing #QuantumAlgorithms #QuantumCircuits #DeepTech #FutureOfComputing #EmergingTech #day2 #computation

What is QSHL (Quantum Self-Healing Language)? QSHL is a quantum programming language that makes error correction automatic, verifiable, and fast enough to actually work on real hardware. Today, quantum error correction is manual, slow, and impossible to verify before execution. QSHL changes that by making error correction a native language feature. Developers declare a “self-healing” policy, and the compiler generates the entire correction system—handling syndrome extraction, qubit management, and recovery automatically. On top of that: • Qubit usage is enforced at compile time • Invalid operations are caught before execution • Circuits that exceed hardware coherence limits are rejected The result: quantum programs that are reliable before they ever touch hardware. This is the direction quantum software needs to go if we want fault-tolerant systems to become real. #QuantumComputing #QEC #ProgrammingLanguages #DeepTech

The version of you that journals every morning is still running legacy code. The Simulation convinced you that writing "I'm grateful for coffee" rewires neural pathways. It doesn't. Real neuroplasticity happens through pattern disruption, not pattern reinforcement. Here's the firmware update: Stop documenting your current reality. Start scripting your next one. Write as the person you're becoming, not the person you were yesterday. Protocol 77 doesn't journal about problems—it architectures solutions. — Ace Richie | Sovereign Synthesis

The biggest bottleneck in #QuantumComputing isn't just the hardware—it's the latency of the software trying to save it. I’m excited to share a major milestone from Korelis Labs LLC: We’ve just hit v0.4.0 of QSHL (Quantum Self-Healing Language), a zero-dependency, Rust-native compiler designed for the "Utility-Scale" era. While the industry targets 0.1% error rates, we decided to push the limits. In our latest stress tests, QSHL’s active Sparse Blossom Decoder achieved a 55.6% heal rate on hardware with a staggering 5% gate error rate. What makes QSHL different? ✅ Fast but Gentle: A Rust-based frontend with an "Ownership Model" for qubits—preventing decoherence at the compiler level. ✅ Real-Time Recovery: Repeated syndrome extraction and MWPM decoding that runs in sub-milliseconds—beating the decoherence clock. ✅ QIR & OpenQASM 3.0 Native: We’ve moved beyond Python. QSHL targets the QIR Adaptive Profile and emits ready-to-run AWS Braket code for IonQ and Rigetti. We’re building this to be the "Quantum Root of Trust" for our upcoming RegenX/OS security architecture. Quantum computing shouldn't just be a lab experiment; it needs to be stable, secure, and fast.

The April 13th edition covers a thermodynamic metric for magic in quantum systems, a benchmark for quantum code generation by LLM and a new quantum-classical compilation framework. Here is the daily selection: 1️⃣ Every Little Thing Heat Does Is Magic 🔗 https://lnkd.in/ecqjwaV2 👨👩 Rafael Macedo, Jonatan Bohr Brask, Rafael Chaves et al. 🔬 It is possible to detect whether a quantum state contains “magic” (a key resource for quantum advantage) using simple thermodynamic measurements, without reconstructing the full state. 2️⃣ QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation 🔗 https://lnkd.in/eXByYNCh 👨👩 Ali Slim, Haydar Hamieh, Jawad Kotaich, Yehya Ghosn, Mahdi Chehimi, Ammar Mohanna, PhD, Hassan Hammoud and Bernard Ghanem 🔬 A new paper introduces a benchmark to fairly evaluate how well LLMs generate quantum code across multiple frameworks (Qiskit, Cirq and Pennylane), separating true quantum understanding from framework-specific knowledge. 3️⃣ The MQT Compiler Collection: A Blueprint for a Future-Proof Quantum-Classical Compilation Framework 🔗https://lnkd.in/eevJwwv7 👨👩 Lukas Burgholzer, Daniel Haag, Yannick Stade, Damian Rovara, Patrick Hopf and Robert Wille 🔬 Future quantum programs are increasingly hybrid, so traditional “quantum-first” compilers are no longer sufficient. MQT Compiler Collection is built on the Multi-Level Intermediate Representation and can better handle full quantum-classical workflows. That’s it for the daily selection. If you enjoyed it, please consider giving us a like or reposting to support our content. Thanks!

Quantum Computing meets Market Analysis: Introducing NEXOUScl V2.0 🌐 The future of trading isn't just about indicators; it's about processing power and predictive accuracy. I am excited to share the latest milestone of the NEXOUScl Quantum Project. By integrating Quantum Computing logic (using Python & pyqpanda) with advanced technical analysis, we’ve developed a system that filters market noise like never before. Key Capabilities: ✅ Quantum Predictive Modeling: Utilizing quantum gates to calculate high-probability trend reversals. ✅ AI Confluence Scoring: A real-time "Brain" that scores every trade from 0-100 based on Volume Flow, Momentum, and Volatility. ✅ Institutional Liquidity Tracking: Identifying where the "Whales" are moving before the price action follows. Our recent backtests on BTC show a 94% Confidence Level in trend identification during peak volatility. 📊 Trading is evolving. We are no longer just following the trend; we are calculating it. #QuantumComputing #AlgorithmicTrading #FinTech #NEXOUScl #Python #TradingView #AI #PredictiveAnalytics "DM me for access requests"

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

I am thrilled to announce that our (Yannis Psaromiligkos) paper, "𝐌𝐨𝐝𝐞𝐥-𝐛𝐚𝐬𝐞𝐝 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐉𝐨𝐢𝐧𝐭 𝐑𝐈𝐒 𝐏𝐡𝐚𝐬𝐞 𝐒𝐡𝐢𝐟𝐭 𝐂𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐖𝐌𝐌𝐒𝐄 𝐁𝐞𝐚𝐦𝐟𝐨𝐫𝐦𝐢𝐧𝐠" has been accepted for publication in the journal: 𝐈𝐄𝐄𝐄 𝐖𝐢𝐫𝐞𝐥𝐞𝐬𝐬 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐋𝐞𝐭𝐭𝐞𝐫𝐬 𝐈𝐄𝐄𝐄 𝐗𝐩𝐥𝐨𝐫𝐞: https://lnkd.in/ewPErAZw 𝐚𝐫𝐗𝐢𝐯: https://lnkd.in/eahUiJyh 𝐃𝐎𝐈: 10.1109/LWC.2026.3683016 𝐂𝐨𝐝𝐞: https://lnkd.in/eZa_5M5s 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭:  A model-based deep learning (DL) architecture is proposed for reconfigurable intelligent surface (RIS)-assisted multi-user communications to reduce the number of bits required for transmitting phase shift information from the access point (AP) to the RIS controller. The AP computes the phase shifts and compresses them into a binary control message that is sent to the RIS controller for element configuration. To help reduce beamformer mismatches caused by phase shift compression errors, the beamformer is updated with the actual (decompressed) RIS phase shifts. By unrolling the iterative weighted minimum mean square error (WMMSE) algorithm within the wireless communication-informed DL architecture, joint phase shift compression and WMMSE beamforming can be trained end-to-end. Simulation results demonstrate that incorporating compression-aware beamforming significantly improves sum-rate performance, even when the number of control bits is lower than the number of RIS elements. #DeepLearning #WirelessCommunications #RIS #IEEE #MachineLearning #SignalProcessing