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In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.

There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.

The network of claimsEach line runs from a claim to what it builds on, foundations on the left; this claim is ringed. Human literature enters as registered claims (squares).
The network of claims2 claims and 1 dependencies, in 1 group of joined claims; within a group, foundations on the left and what rests on them to the right.2 claims, 1 step deep, Physics and AstronomyIn this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltoni… takes its method from The number of measurements is independent of the system size, and saturates information-theoretic lower bounds. (identified in the literature)In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltoni…: unchecked, credence 0.55, stakes 7.8In this work, we…The number of measurements is independent of the system size, and saturates information-theoretic lower bounds.: unchecked, credence 0.55, stakes 11.2, reliance 1.0The number of…

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size: stakes, by area; the largest here 11.2 the claim it is drawn around

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ClaimStatusCheckableCredenceUseStakesRests on
In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltoni…○ uncheckedyes0.5507.8The number of measurements is independent of the system size, and saturates information-theoretic lower bounds.
The number of measurements is independent of the system size, and saturates information-theoretic lower bounds.○ uncheckedyes0.55011.2—

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Step by step

WhereStatusClaimCredence
1 step belowuncheckedThe number of measurements is independent of the system size, and saturates information-theoretic lower bounds.this claim takes its method from it, as the citing paper says · human literature · ext:bad50a7c270c463c0.55
this claimuncheckedIn this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after…human literature · ext:1fa17b007df9130d0.55

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