"Exploring Variational Quantum Algorithms for Quantum Computing"

View profile for Sundeep B Singh

Senior Project Manager at Government of India

⚡🤝 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

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