⚡🤝 Day 18 – Variational Quantum Algorithms (VQAs) Day 18 of my QuCode 21 Days Quantum Computing Challenge – Cohort 3! If the past few days were about quantum’s theoretical breakthroughs (Shor, Grover), today was about its practical survival strategy in the NISQ era: V Variational Quantum Algorithms (VQAs). 🔹 The Hybrid Loop A VQA is not fully quantum — it’s a dance between quantum and classical worlds: A parameterized quantum circuit (PQC) encodes candidate solutions. Measurements extract expectation values. A classical optimizer updates the parameters. The loop repeats, steadily converging toward an answer. 🔹 Why it Matters VQE (Variational Quantum Eigensolver) estimates molecular ground states, crucial for chemistry and materials science. QAOA (Quantum Approximate Optimization Algorithm) tackles combinatorial optimization, from logistics to finance. Hybrid design makes VQAs robust to noise and feasible on today’s shallow circuits. 🔹 Challenges & Insights Smart choice of ansatz (hardware-efficient vs problem-inspired) is critical. Barren plateaus and noise can stall progress, but clever optimizers (SPSA, COBYLA) keep the loop alive. Most importantly, the same framework flexibly adapts to chemistry, ML, and optimization tasks. ✨ Takeaway VQAs show that the future of quantum won’t be purely quantum — it will be hybrid. They remind me that innovation often means compromise: not waiting for perfect hardware, but building algorithms that thrive within constraints. The beauty of VQAs is simple — they don’t just open the quantum door, they hold it open long enough for us to walk through. 🚀 #Day18 #QuCodeChallenge #QuantumComputing #VQE #QAOA #HybridComputing #FutureOfTech #LearningJourney
"Exploring Variational Quantum Algorithms for Quantum Computing"
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⚡🛠️ Day 20 – Noise, Error Correction & the NISQ Era Today’s learning was a reminder that quantum computing is as much about facing fragility as it is about chasing potential. 🔹 The Fragility of Qubits Qubits don’t behave like classical bits. They suffer bit-flip, phase-flip, and decoherence errors — and because qubits can’t be cloned, classical error correction won’t work. The fix? Encode 1 logical qubit into many physical ones, use syndrome measurements, and recover without collapsing the state. 🔹 Quantum Error Correction (QEC) From Shor’s 9-qubit code to Steane’s 7-qubit code, QEC forms the bedrock of fault-tolerant quantum computing. It’s the engineering scaffold that will one day support machines with millions of stable qubits. 🔹 The NISQ Era But for now, we live in the Noisy Intermediate-Scale Quantum (NISQ) world: 50–1000 qubits, powerful but error-prone. In this era: Hybrid algorithms (VQE, QAOA) survive in shallow circuits. Error mitigation stands in for full correction. Clever ansatz design & optimizers extract insights despite imperfections. ✨ Takeaway: Day 20 reminded me that quantum’s story isn’t only elegant math — it’s about resilience engineering in the face of imperfection. Noise isn’t the end; it’s the challenge shaping the present and future. #Day20 #QuantumComputing #NISQ #QEC #FutureOfTech QuCode
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#QuantumComputing #progress isn’t powered by hardware alone. Its acceleration depends on 𝘁𝘄𝗼 𝗯𝗼𝗼𝘀𝘁𝗲𝗿𝘀: #H𝗮𝗿𝗱𝘄𝗮𝗿𝗲 #I𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 #𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 #𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝗮𝘁𝗶𝗼𝗻. Reading a new paper on block encoding this week got me thinking about benchmarking fault-tolerant quantum computing (#FTQC). The paper shows how an #algorithmic #optimisation can slash the number of gates needed to simulate many-body systems. Overnight, what looked “decades away” on hardware grounds alone suddenly looks far more feasible. That reflection reminded me of something important: 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗲𝘀𝘁 #𝘁𝗿𝗮𝗰𝗸 #𝗙𝗧𝗤𝗖 #𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 ? 𝗦𝗶𝗻𝗰𝗲 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗰𝗮𝗻 𝗰𝗼𝗺𝗲 𝗳𝗿𝗼𝗺 𝗯𝗼𝘁𝗵 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗮𝗻𝗱 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 ? The answer is: the #𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 is stable. You might be trying to: ➡️ Simulate a catalyst with chemical accuracy. ➡️ Break RSA-2048. ➡️ Model high-temperature superconductors. Breakthrough for your goal happens there is changes in the #R𝗲𝘀𝗼𝘂𝗿𝗰𝗲E𝘀𝘁𝗶𝗺𝗮𝘁𝗲𝘀 to reach that goal. As algorithms improve, the number of qubits, T-gates, or circuit depth required shifts, sometimes dramatically. This isn’t unique to quantum. In classical computing, early #benchmarks were tied to specific algorithms (like Gaussian elimination). But as algorithms evolved ( FFTs, sparse solvers, deep learning ) those single-algorithm benchmarks became obsolete. The benchmarks that lasted (LINPACK, SPEC, MLPerf) are 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻-𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗮𝗻𝗱 𝗿𝗲𝗴𝘂𝗹𝗮𝗿𝗹𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 to reflect both hardware and algorithm progress. Quantum computing needs the same approach. * 𝗡𝗜𝗦𝗤 𝗲𝗿𝗮: algorithm-specific benchmarks (like QAOA, VQE) make sense, because hardware constraints dominate. * 𝗙𝗧𝗤𝗖 𝗲𝗿𝗮: benchmarks should be application-level and algorithm-agnostic, updated as our methods improve. Because quantum advantage won’t come from qubits alone. It will come where 𝗳𝗶𝘅𝗲𝗱 𝗴𝗼𝗮𝗹𝘀 𝗺𝗲𝗲𝘁 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗽𝗮𝘁𝗵𝘀 : 𝗮𝘁 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗮𝗻𝗱 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀. #BenchmarkingFTQC #QuantumComputing #Benchmarks The block paper mentioned: https://lnkd.in/eQqQrRZU
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⚡ Day 18 of 21 Days Challenge with QuCode – Variational Quantum Algorithms (VQAs) 🚀 Today’s session took us into one of the most practical and promising areas of quantum computing: Variational Quantum Algorithms. 🔹 Core Learnings What VQAs are, their advantages and challenges. Mathematical background behind variational principles. Revisiting Quantum Phase Estimation and its iterative version. Using VQAs to minimize the energy of a system via cost functions. 🔹 Breaking Down VQAs Hamiltonian as a cost function → mapping & reduction. Trial states + variational principles. Optimization with shallow circuits ⚡. Initial states, hardware control, and error mitigation techniques. 🔹 Applications & Complexity Practical applications: Quantum Chemistry & Optimization Problems. Computational complexity considerations → why VQAs are heuristic algorithms. 👉 Takeaway: VQAs are not just math on paper — they’re a bridge between today’s noisy quantum hardware and tomorrow’s powerful quantum solutions. #21DaysChallenge #QuantumComputing #VQA #Optimization #QuantumChemistry #HeuristicAlgorithms
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“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
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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
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Quantum SAT Solving: Real Hardware, Real Results Yesterday I built a Quantum API — today I’ve already put it to work on one of the toughest problems in computer science: Boolean satisfiability (SAT). Instead of running heavy classical solvers, my system uses quantum computing to predict whether a SAT problem is solvable. This saves time and energy by spotting hard cases before they consume huge resources. What’s exciting is that I’m not simulating — I’m running on real quantum hardware. And the results show something you can’t get from classical systems: Quantum optimization (QAOA) gives different outcomes each run, reflecting multiple solution paths. Quantum search (Grover) finds different conflict patterns with high success rates each time. Quantum clustering shows tiny variations across runs while keeping overall structure stable. Even though the quantum measurements vary (because of superposition and randomness at the hardware level), the final predictions stay consistent. That’s exactly the kind of behavior that proves quantum advantage in action today. My approach: Encode SAT constraints directly into quantum circuits. Apply new mathematical analysis to reveal hidden structure in SAT problems. Combine mathematical and quantum results so we get stability plus true quantum insight. This bridges theory and practice — showing that current quantum devices can already help tackle NP-complete problems like SAT. No need to wait for future fault-tolerant machines — quantum advantage is starting now. #QuantumComputing #SATSolving #Innovation #TechDevelopment #ArtificialIntelligence #BytWyze
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Quantum Algorithms, from VQE to SQD, Estimate Ground State Energy by Sampling Determinants Researchers have established a precise mathematical formula to calculate the number of measurements needed for quantum algorithms to efficiently estimate ground state energies, paving the way for more practical quantum computation on near-term processors by overcoming limitations of existing methods. #quantum #quantumcomputing #technology https://lnkd.in/eWX6yvhk
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Day 18/21 of Learning Quantum Computing with QuCode- Hybrid Quantum-Classical Computing . As I continue my quantum journey, today’s focus is on one of the most practical approaches in the NISQ era: Hybrid Quantum-Classical Computing. *Why Hybrid? Current quantum computers are powerful, but they’re still noisy and limited in qubit count. By combining them with classical computers, we get the best of both worlds: - Classical systems handle optimization, memory, and control. -Quantum systems tackle problems where superposition and entanglement give real advantages. This approach powers algorithms like: Variational Quantum Eigensolver (VQE) for chemistry & materials Quantum Approximate Optimization Algorithm (QAOA) for optimization problems Hybrid Machine Learning models blending quantum feature maps with classical neural nets. Each day, I explore concepts that will shape the future of computing and prepare our community to be at the forefront of innovation. #QuantumComputing #HybridComputing #NISQ #VQE #QAOA #QuCode #LearningTogether
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Day-18 of Learning Quantum Computing Qohort-3 of Quantum Learning continues! Today’s Focus: 🔹 Hybrid Quantum–Classical Computing ✨ Key Insights: Today’s session highlighted how the future of computation is hybrid — leveraging the efficiency of classical systems for tasks they excel at, while utilizing the unique power of quantum processors for problems where they provide an advantage. Examples include Variational Quantum Algorithms (VQAs) such as: VQE (Variational Quantum Eigensolver) for chemistry and material simulations. QAOA (Quantum Approximate Optimization Algorithm) for optimization problems. These approaches reflect the practical reality of near-term quantum devices (NISQ era), where quantum and classical resources must work hand in hand. 💡 The future is hybrid — combining the strengths of classical computers with the power of quantum systems to solve real-world problems. Let’s Learn. Build. Connect. Grow. together 💡 #QuantumComputing #HybridComputing #VQE #QAOA #QuantumAlgorithms #QuCode #21DaysQuantumChallenge #FutureTech #STEM #Innovation #LearningJourney
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𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: 𝗠𝗼𝗿𝗲 𝗣𝗼𝘄𝗲𝗿 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 How can several smaller quantum computers be connected so that they work together as one powerful system? This idea is called Distributed Quantum Computing (DQC). It could make it possible to run much larger and more powerful algorithms than a single chip could handle on its own. In our new work, we studied how different architectures of variational quantum circuits (VQCs) behave in such a distributed setting, specifically in the context of a classification task from Quantum Machine Learning (QML) within DQC. Using simulations, we tested how circuits perform when multiple quantum processors are linked, and how much entanglement between them is actually required. The results show that circuits with a smart balance of local and global entanglement are more robust to noise than standard approaches. This suggests that well-designed circuit architectures could enable distributed quantum computing to achieve better results in the near future. Our paper "Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing" has been accepted at IEEE QAI 2025 and is available as a preprint here: 👉 https://lnkd.in/db8bHuzN The paper was authored by Leo Sünkel, Jonas Stein, Jonas Nüßlein, Tobias Rohe, and Claudia Linnhoff-Popien. Supported by the Bavarian Ministry of Economic Affairs (6GQT project) and the German Federal Ministry of Research, Technology and Space (BMFTR). #QuantumComputing #DistributedSystems #VariationalQuantumCircuits #Research #Innovation
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