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Classical hardness of Hamiltonian function learning proven

By PulseAugur Editorial · [1 sources] ·

Researchers have rigorously proven the average-case classical hardness for specific Hamiltonian function learning problems. This work builds upon a quantum machine learning problem proposed by Morohoshi, Nakayama, Manabe, and Mitarai, where the goal is to predict quantities related to an unknown function of a Hamiltonian. The new findings demonstrate that an efficient classical learner for these problems would imply a classical polynomial-time algorithm for factoring random RSA moduli, assuming the average-case hardness of that task. AI

RANK_REASON Academic paper detailing theoretical hardness proofs for a quantum machine learning problem. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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Classical hardness of Hamiltonian function learning proven

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Academic paper detailing theoretical hardness proofs for a quantum machine learning problem. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sota Hashimoto, Akinori Kawachi ·

    Classical Hardness of Learning Functions of Hamiltonians

    arXiv:2610.01141v1 Announce Type: cross Abstract: Morohoshi, Nakayama, Manabe, and Mitarai proposed a physically motivated quantum machine learning problem in which the goal is to predict quantities of the form $\operatorname{Tr}[f(H)\rho]$ from classical descriptions of a Hamilt…

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