विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| वैरिएशनल क्वांटम आइजेनसाॅल्वर× | क्वांटम मोंटे कार्लो× | |
|---|---|---|
| क्षेत्र | क्वांटम कंप्यूटिंग | क्वांटम कंप्यूटिंग |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2014 | 1953 |
| प्रवर्तक≠ | Alberto Peruzzo | Nicholas Metropolis and colleagues |
| प्रकार≠ | Hybrid quantum-classical algorithm | Monte Carlo simulation |
| मौलिक स्रोत≠ | Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213. DOI ↗ | Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092. DOI ↗ |
| उपनाम≠ | VQE, hybrid quantum-classical | QMC, variational Monte Carlo, diffusion Monte Carlo |
| संबंधित≠ | 4 | 3 |
| सारांश≠ | The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to find the lowest eigenvalue (ground state energy) of a quantum Hamiltonian. Introduced by Peruzzo et al. in 2014, it exploits the variational principle to combine the power of quantum circuits with classical optimization to solve chemistry and materials science problems on near-term quantum devices. | Quantum Monte Carlo (QMC) is a stochastic computational method for computing ground state properties of quantum many-body systems. Combining classical Monte Carlo sampling with quantum mechanics, QMC approaches are among the most accurate methods available for electronic structure and condensed matter physics, achieving sub-percent accuracy for many systems. |
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