Quantum Computing Architecture: Classical Control Layer

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 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

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

🔐 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

Infleqtion secured a contract through Defense Advanced Research Projects Agency (DARPA)'s Heterogeneous Architectures for Quantum (HARQ) program to build Multistaq, a compiler designed for systems that combine multiple qubit modalities. Today's quantum compilers are built for one qubit type. As hardware roadmaps increasingly mix modalities, writing circuits that run efficiently across all of them becomes an engineering challenge. Multistaq extends the write-once, target-all model behind Infleqtion's existing Superstaq compiler, adding cross-modality and cross-layer optimization for heterogeneous architectures. Nothing is available to developers yet; this is a 24-month research contract. But if you're already targeting multiple QPU backends through Superstaq, this is the direction that platform is heading. Full breakdown on the QFrontline blog. → Read the full article: https://lnkd.in/gUAJWQjy → Subscribe to our weekly newsletter for quantum dev news straight to your inbox: https://lnkd.in/gBft6x77 #QuantumComputing #Infleqtion #DARPA #QuantumSoftware #QuantumDev

What if your optimizer wasn't software — but physics? We built PROMETHEUS: a 1,024-channel Langevin dynamics engine on a Xilinx UltraScale+ FPGA that solves portfolio optimisation by physical convergence to a Boltzmann equilibrium — not by iterating a numerical algorithm. In our latest paper, we put it head-to-head against four conventional solvers (Python analytical, SciPy SLSQP, OR-Tools QP, and our own Quantitative Finance MCP server) on a live six-asset minimum-variance portfolio. The result: 0.0006 percentage-point hardware error against the analytical target, convergence in 256 ms, and $3,759/day in VaR savings per $1M versus equal-weighting. The key insight: the coupling matrix K is the program. Loading a new K into URAM is programming the computer. Switching between two Hamiltonians takes 5 ns on-chip — O(1) in problem size — which is what makes Jarzynski and Crooks free energy protocols physically valid on hardware but fundamentally limited in software. All hardware access goes through governed MCP tool calls via the Querex gateway. No shell, no filesystem, no direct hardware access. Enterprise-grade governance by design. If you want to read more: https://lnkd.in/ev-J-vcG #FPGA #QuantitativeFinance #PortfolioOptimization #StatisticalMechanics #MCP #ThermodynamicComputing

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

-- Quantum ML Series 2 | Post 04 of 11 #QFD2 -- Everyone talks about quantum circuits. Nobody explains what variational actually means. Here is the developer friendly version. A variational quantum circuit is a quantum circuit with tunable parameters. Rotation angles that you can adjust during training. That is it. The word variational just means parameterized and optimizable. A fixed quantum circuit always does the same thing. A variational quantum circuit learns. The parameters are rotation angles theta. The optimization is still gradient descent. The training loop still feels like PyTorch. The physics is different. The developer experience is closer than you think. The circuit has three parts: State prep -> qubits initialized to zero state Ansatz -> the parameterized gate structure that actually learns. ( This is the most important design decision in all of QML.) Measurement -> quantum state collapses to classical output The ansatz design involves a real tradeoff. More depth means more expressibility. More depth also means gradients that get exponentially smaller. At a certain depth the gradient landscape goes completely flat. This is the barren plateau problem. It is unsolved. It is a genuine constraint on how deep these circuits can go right now. One honest note I am carrying from one of my reader on Post 2. Everything above about expressibility and deep circuits is based on theoretical properties. On NISQ hardware today, noise compounds with circuit depth. The practical operating zone is significantly shallower than theory suggests. That gap between theory and hardware is real. I am keeping it visible in every post. Full breakdown of ansatz design, the expressibility versus trainability tradeoff and what NISQ hardware actually limits in the article. https://lnkd.in/gYWNTAHB Article link in first comment 👇 One more thing. I am currently open to full stack development and quantum computing opportunities. If your team is building in either space and you think I could be a good fit, feel free to reach out or drop a comment. Always happy to have a conversation. #QuantumComputing #QuantumML #MachineLearning #LearnInPublic #Developer #PennyLane #QML #Coding #FutureTech #QuantumMLSeries #OpenToWork #FullStackDeveloper #QuantumComputing

📡 Paper accepted in IEEE Open Journal of the Communications Society (𝐎𝐉𝐂𝐎𝐌𝐒)! Our paper 𝐃𝐓𝐌𝐀𝐏: 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧-𝐆𝐮𝐢𝐝𝐞𝐝 𝐀𝐈 𝐏𝐚𝐭𝐡 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐯𝐢𝐭𝐲-𝐀𝐰𝐚𝐫𝐞 𝐌𝐨𝐛𝐢𝐥𝐢𝐭𝐲 has been accepted in 𝐈𝐄𝐄𝐄 𝐎𝐉𝐂𝐎𝐌𝐒. This extends 𝐂𝐓𝐌𝐚𝐩 (accepted at 𝐈𝐄𝐄𝐄 𝐈𝐂𝐂 2026) into a full multi-objective framework. The figure shows what 𝐃𝐓𝐌𝐀𝐏 does: it takes a wireless digital twin of the environment, buildings, geometry, and signal strength maps, feeds candidate routes and a routing objective into a fine-tuned LLM, and outputs a path that isn't just short but stays connected. The green path on the right isn't the fastest route. It's the one that keeps you in coverage. 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 6𝐆/𝐗𝐑: moving through a city while streaming high-bandwidth content means dead zones kill the experience. DTMAP learns to navigate around them. 𝐓𝐞𝐜𝐡 𝐬𝐭𝐚𝐜𝐤 𝐚𝐧𝐝 𝐋𝐋𝐌𝐬 𝐮𝐬𝐞𝐝:  NVIDIA Sionna (ray tracing), Blender, OpenStreetMap, Python, PyTorch, Dijkstra/A* (oracle and baseline), GPT-4o,LLaMA 3.1/3.3-70B, Qwen-2.5-72B This research was conducted under the supervision of Dr. Md. Parwez at Computer Science and Engineering at UT Arlington. #MachineLearning #LLM #DigitalTwin #6G #WirelessCommunications #IEEE #OJCOMS #PathPlanning #DeepLearning #AIResearch #mmWave #Networking #MLSystems #AppliedAI #PyTorch

Linker's More of what i think you should know conclusions and future work. Download: Download high-res image (191KB) Download: Download full-size image Fig. 1. Study roadmap highlighting the structure and progression of the research work. 2. Background QC has evolved from foundational theory to practical systems, introducing a transformative capacity to solve problems that cannot be solved with classical machines. This technological evolution has generated the need for QSE as a discipline devoted to the specification, design, implementation, and deployment of software for quantum computers. Unlike classical software engineering, QSE requires considering quantum-specific mechanics such as superposition, entanglement, and quantum decoherence, which introduce novel engineering challenges and security implications. 2.1. Quantum computing QC is a revolutionary novel computational theory and practice. Classical computers use classical bits; information is stored with the help of binary states 0 or 1. Alternatively, quantum bits are used in quantum compute