방법 비교

	양자 몬테카를로	양자 위상 추정	쇼어 알고리즘
분야	양자컴퓨팅	양자컴퓨팅	양자컴퓨팅
계열	Machine learning	Machine learning	Machine learning
기원 연도	1953	1995	1994
창시자	Nicholas Metropolis and colleagues	Alexei Kitaev	Peter Shor
유형	Monte Carlo simulation	Subroutine algorithm	Quantum algorithm
원전	Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.	Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026.	Shor, P. W. (1994). Algorithms for quantum computation: discrete logarithms and factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 124–134.
별칭	QMC, variational Monte Carlo, diffusion Monte Carlo	QPE, phase kickback	Shor factorization, quantum factorization
관련	3	3	3
요약	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.	Quantum Phase Estimation (QPE) is a fundamental quantum subroutine that estimates the eigenvalues of a unitary operator. Developed by Alexei Kitaev in 1995, QPE combines controlled unitary evolution with the quantum Fourier transform to extract eigenvalues from quantum states with exponential precision scaling.	Shor's Algorithm is a polynomial-time quantum algorithm for factoring large integers and computing discrete logarithms, problems believed to be intractable on classical computers. Discovered by Peter Shor in 1994, it demonstrated the potential of quantum computers to break widely used cryptographic systems like RSA, marking a landmark in quantum computing theory.