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🚀 𝗠𝗮𝗸𝗶𝗻𝗴 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗶𝗿𝗰𝘂𝗶𝘁𝘀 𝗦𝗺𝗮𝗿𝘁𝗲𝗿: 𝗟𝗲𝘀𝘀 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗠𝗼𝗿𝗲 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗳𝗼𝗿 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴  Distributed Quantum Computing (DQC) connects multiple quantum processors (QPUs) to solve larger tasks. The challenge: communication between QPUs takes time and resources. Our solution:  In conventional methods, the assignment of qubits to QPUs is typically optimized in order to minimize communication overhead. The circuit itself remains unchanged. In this work, we approach the problem from a different perspective. We use an evolutionary algorithm (EA) that optimizes the quantum circuit itself, i.e., its architecture, before it is executed on multiple distributed QPUs. Results:  ➡️ Up to 89 % fewer remote gates  ➡️ Up to 19 % lower communication costs in networks  ➡️ High accuracy (>90 % fidelity) is maintained  💡 Less communication overhead means faster and more efficient distributed quantum computers The paper “Evolutionary-Based Circuit Optimization for Distributed Quantum Computing” was accepted for IEEE QAI 2025 and can be read as a preprint here: https://lnkd.in/dCtSu42a The authors of the paper are Leo Sünkel, Jonas Stein, Gerhard Stenzel, Michael Kölle, Thomas Gabor und Claudia Linnhoff-Popien. Sponsored by the Bavarian Ministry of Economic Affairs, Regional Development and Energy as part of the 6GQT project, as well as the German Federal Ministry of Research, Technology and Space. #QuantumComputing #DistributedQuantumComputing #QuantumOptimization #QuantumCircuits #EvolutionaryAlgorithms #QuantumResearch #DQC #QAI2025  

Day 18: Bridging the Gap – The Power of Hybrid Quantum-Classical Computing The #21DaysOfQuantum journey with QuCode is exploring one of the most practical and immediate approaches to leveraging quantum power today. We're moving beyond pure quantum algorithms to a collaborative model that combines the best of both worlds. Today’s Focus: Hybrid Quantum-Classical Computing. Instead of waiting for a single, massive quantum computer to solve a problem from start to finish, this approach breaks the work into parts. It uses a quantum computer for what it does best—manipulating complex quantum states and generating probability distributions—and a classical computer for what it does best—orchestrating the process, optimizing parameters, and analyzing results. ▫️ How It Works: A classical computer runs a central optimization loop. For each step, it: 1. Sends a set of parameters to the quantum processor. 2. The quantum computer runs a short circuit (an ansatz) and measures the outcome. 3. The result is sent back to the classical computer. 4. The classical computer analyzes the result and uses an optimization algorithm to update the parameters for the next round. This cycle repeats until the system converges on an optimal solution. ▫️ Why It Matters Now: This model is perfectly suited for the current era of Noisy Intermediate-Scale Quantum (NISQ) hardware. It uses short, shallow circuits that are less vulnerable to decoherence and noise, making it possible to get useful results from today's quantum processors. This hybrid approach is the foundation for promising near-term applications like: + Variational Quantum Eigensolver (VQE): For simulating molecules and materials. + Quantum Approximate Optimization Algorithm (QAOA): For solving complex optimization problems. + Quantum Machine Learning (QML): Where a quantum circuit can be used as a feature map or a trainable model. It's a powerful reminder that the quantum future isn't about replacement, but about integration and collaboration. #QuantumComputing #HybridComputing #NISQ #VQE #QAOA #QuantumMachineLearning #QuantumAlgorithms #LearnInPublic #STEM

“Advanced Quantum Framework for Hybrid Cognitive-Sustainable Systems (HCSS)” — now published on Zenodo: DOI: https://lnkd.in/eQ4nrGQj Key Highlights I propose a realistic, rigorous quantum roadmap (2025-2027) to evolve NISQ devices into a platform capable of demonstrating quantum advantage in targeted problems. The framework integrates: Precise quantum metrics (effective logical qubits, fidelity, circuit depth, coherence time, error overhead, Quantum Volume, CLOPS) Mitigation + error correction hybrid strategy (Zero-Noise Extrapolation, small surface codes, future QLDPC) A concrete VQE protocol for LiH using ZNE + randomized compiling + meta-learning (LSTM/MAML) to reduce iteration count I emphasize verifiable mathematics, reproducibility and scalability, without publishing core proprietary theory. The approach connects technical performance (quantum fidelity, depth, coherence) with economic impact: e.g., simulating catalysts to achieve 1% efficiency gains may translate into multi-million-€ savings for industrial processes. If you’re working in quantum computing, quantum machine learning, hybrid architectures, or looking into scalable quantum systems, I’d be thrilled to discuss synergies or collaborations. Feel free to download the full paper from Zenodo and let me know your thoughts or questions. Let’s push quantum systems closer to real-world impact — one algorithm, one metric, one insight at a time. #QuantumComputing #NISQ #QuantumAlgorithms #VQE #MetaLearning #Zenodo #Research

I keep reading about 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 “𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀” in the news, so I’ve been wondering: 𝘄𝗵𝗲𝗿𝗲 𝗶𝘀 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗿𝗲𝗮𝗹𝗹𝘆 𝗮𝘁, 𝗮𝗻𝗱 𝘄𝗵𝗲𝗻 𝗺𝗶𝗴𝗵𝘁 𝘄𝗲 𝘀𝗲𝗲 𝗮 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹𝗹𝘆 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿? In searching around, I came across an interesting opinion piece by 𝗝𝘂𝗿𝗷𝗲𝗻 𝗕𝗼𝘀 on Medium about the current state of the field. This is how the opinion piece frames it: “The researchers themselves are faced with a hard problem. They get millions of funding, with which they build a device that cannot compute anything, but is called a 'quantum computer' because that’s what the sponsors say it is. Now the sponsors ask them to show what it does, so they have to write an article. But because there are competing research groups, they are expected to have something better than the competition. They have no choice than to blow up their results.” “The actual situation is that there still is no 'quantum computer' in the sense of a machine that can perform a useful computation. The multimillion-dollar machines we see on the news are the equivalents of electronics breadboards for making simple circuits (but not as powerful).” 💡 Curious to hear from anyone in my network: 𝗗𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗮𝗹𝗶𝗴𝗻 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗼𝗿 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗼𝗻 𝘁𝗵𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝘀𝘁𝗮𝘁𝘂𝘀 𝗼𝗳 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴? #ScienceCommunication #SciComm #ScienceMatters #ResearchInsights #TechTrends

We are delighted to share our new paper on Nature: Low-Overhead Transversal Fault Tolerance for Universal Quantum Computation https://buff.ly/RM9S3zd In collaboration with Harvard and Yale, QuEra introduces Algorithmic Fault Tolerance (AFT) — a breakthrough framework that cuts error-correction overhead and accelerates the timeline for practical, large-scale quantum computing. Key Highlights • Reduced runtime overhead: AFT slashes the cost of error correction by a factor of d, often ~30×, with neutral-atom architectures enabling 10–100× faster execution of logical algorithms. • New methodology: By combining transversal operations with correlated decoding, AFT maintains exponential error suppression while dramatically speeding up computations. • Practical impact: A companion study (https://buff.ly/x8t8Xs0 ) applies AFT to Shor’s algorithm, illustrating how these advances translate into real-world efficiency gains. At QuEra, we believe this represents a credible, scalable pathway to practical quantum advantage — reinforcing the structural strengths of neutral-atom platforms: reconfigurability, high connectivity, and room-temperature operation. 🔗 Explore the companion resource study here: Resource Analysis of Low-Overhead Transversal Architectures for Reconfigurable Atom Arrays #QuantumComputing #NeutralAtoms #QuantumErrorCorrection #FaultTolerance #HPC #QuEra #Innovation

Scientists have just taken a major step toward making quantum teleportation more powerful and practical. A recent breakthrough involves the W‑state, a very special form of quantum entanglement. Until now scientists struggled to reliably identify and use this W‑state in entanglement measurements. Now they have created a method (using a photonic quantum circuit) that can distinguish W‑states of multiple photons with high fidelity. This solves a decades‑old problem and opens new doors for quantum teleportation and quantum computing. Here is what that means, in concrete terms: When two or more particles are entangled, they share a link that allows the state of one to depend on the state of the other(s) even when separated. There are various types of entangled states; W‑states are special because they are robust, they remain entangled even if one of the particles is lost or disturbed. This makes them especially useful for real quantum systems, which are always dealing with noise, loss, and imperfections. By being able to identify and measure W‑state entanglement in a stable optical circuit, scientists can now leverage that robustness for more reliable quantum teleportation protocols. That means transmitting quantum information (like the “shape” of a qubit) from one location to another without moving the physical particle itself can be done more reliably. It also enhances possibilities for long‑distance quantum networks, more powerful quantum computing architectures, and better quantum communication. However, this is still early in the journey. While the experiment has demonstrated the ability to carry out accurate entangled measurements of W‑states in three photons, scaling this to larger systems and ensuring low error over long distances will take more work. Stability, coherence, and integration into usable hardware are ongoing challenges. This discovery is a reminder that what often seems like abstract quantum theory can lead to breakthroughs that change how we might transmit information, secure networks, or build computers of the future. #QuantumTeleportation #Entanglement #WState #QuantumComputing #QuantumNetworks #QuantumBreakthrough #InformationScience #FutureTech

💡 Quantum Computing & the Role of Complex Numbers A common question: “Where do complex numbers (a + bi) fit into quantum computing?” 🔹 Each qubit’s state is defined by complex amplitudes (α, β). 🔹 Gates manipulate these amplitudes using unitary operations (complex matrices). 🔹 The imaginary part (i) doesn’t directly appear in measurement but drives phase & interference, which are the core of quantum speedup. 🔹 Finally, measurement collapses the state into classical outcomes, with probabilities given by |α|², |β|². ⚡ In short: Complex numbers are the hidden gears of quantum computation — invisible in the final answer, but essential for making quantum advantage possible. #QuantumComputing #QuantumInformation #ComplexNumbers #Qubits #Learning

Quantum Readout Error Mitigation Introduces Exponentially Growing Systematic Errors with Increasing Qubit Number Researchers demonstrate that commonly used error correction methods in quantum computing overestimate the accuracy of complex calculations and entangled states, and that these errors grow rapidly as the number of qubits increases, necessitating more careful benchmarking of initial state preparation. #quantum #quantumcomputing #technology https://lnkd.in/ecvyCcf7

**Quantum Entanglement Defines the Ultimate Speed Limit of Quantum Computers** Quantum computers hold unprecedented potential to solve complex problems by leveraging quantum phenomena like _entanglement_—a unique correlation linking qubits instantaneously across distances. But how fast can quantum computations really become? Recent research unravels that a quantum computer’s **speed limit is fundamentally governed by the nature and complexity of entanglement required by the problem at hand**. Harder computational problems demand more intricate entanglement patterns among qubits, effectively translating mathematical difficulty into physical constraints on quantum processes. This insight, developed by Achim Kempf and colleagues, establishes **a direct link between entanglement complexity and quantum processing velocity**. Instead of sequentially exploring every possible solution, a quantum computer exploits entangled states to evaluate multiple "valleys" of a solution landscape simultaneously, accelerating computations exponentially compared to classical methods. Key takeaways: - Quantum entanglement acts as both _the resource_ and _the limiting factor_ for quantum computational speed. - The more complex the entanglement needed, the more the quantum system must dynamically evolve to harness it, setting a natural upper bound on speed. - Understanding these limits guides designing more efficient quantum algorithms that _“smooth”_ computational paths, optimizing speed and resource usage. Entanglement is thus **not just a facilitator but a defining metric for quantum computing performance**. As we push quantum hardware forward, this foundational knowledge will steer innovations for maximizing quantum speedup in real-world applications from medicine to logistics. #QuantumComputing #QuantumEntanglement #QuantumSpeedLimit #QuantumAlgorithms #Qubits #QuantumInformation #thequantumcircle See original article here -> https://lnkd.in/eFejq4jn

⚡🛠️ Day 20 – Noise, Error Correction & the NISQ Era Day 20 of my QuCode 21 Days Quantum Computing Challenge – Cohort 3! If Grover, Shor, and QML painted a picture of quantum’s potential, today was a reality check: quantum computers live in a world of noise and fragility. Yet, that’s where the real engineering begins. 🔹 The Fragility of Qubits Unlike classical bits, qubits suffer from bit-flip, phase-flip, and decoherence errors. They can’t be cloned, making classical error correction impossible. The solution? Encode one logical qubit into many physical qubits, detect errors with syndrome measurements, and recover the state without collapsing it. 🔹 Quantum Error Correction (QEC) From Shor’s 9-qubit code to Steane’s 7-qubit code, QEC is the foundation of fault-tolerant quantum computing. It’s the scaffolding that will one day support machines with millions of stable qubits. 🔹 The NISQ Era But we’re not there yet. Today’s devices are Noisy Intermediate-Scale Quantum (NISQ) systems — 50 to 1000 qubits, powerful but error-prone. In this era, we don’t eliminate noise, we work around it: Use hybrid algorithms like VQE and QAOA that tolerate shallow circuits. Apply error mitigation techniques instead of full correction. Design noise-aware ansatz and clever optimizers to squeeze insights out of imperfect machines. ✨ Takeaway Day 20 reminded me that quantum progress is not only about elegant math — it’s about engineering resilience in the face of imperfection. Noise is not the end of the story; it’s the challenge that’s shaping the present and future of quantum computing. The NISQ era may be noisy, but it’s the bridge carrying us toward the quantum advantage we seek. 🌉⚛️ #Day20 #QuCodeChallenge #QuantumComputing #NISQ #QuantumErrorCorrection #FutureOfTech #LearningJourney