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This research establishes a rigorous computational barrier where efficiently predicting properties of quantum systems is as hard classically as breaking RSA encryption, validating the inherent difficulty that necessitates quantum advantage. The findings confirm that classical computers face intrinsic limits in simulating certain quantum dynamics without risking breakthroughs in integer factorization.

Surse care acoperăQuantum Zeitgeist, AI Alignment Forum

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Learning Functions Implies Difficulty with Factoring 2048-Bit RSA Moduli

Quantum Zeitgeist3 octombrie 20264 min de cititArticolul și perspectiva sunt în engleză.

Can predicting energy levels from quantum systems be done efficiently on conventional computers? New results demonstrate this task is as difficult classically as breaking modern encryption, specifically, solving it quickly would imply a fast method to factor large numbers used in RSA security protocols. This rigorously proves inherent computational limits and validates potential speedups offered by quantum machine learning algorithms.

Predicting quantities related to quantum systems accurately with limited classical information is computationally hard for conventional computers in certain cases. Predicting properties, specifically traces of functions applied to Hamiltonians and density matrices, is difficult using standard computing methods when considering cosine and exponential decay as descriptions of system evolution. Predicting certain properties of quantum systems presents computational challenges for standard computers. The focus lies on ‘Hamiltonian function learning’, whereby algorithms predict characteristics based solely on limited classical information concerning a system’s…

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