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researcher

Michael Walter

55 papers hereh-index 303.1k citations80 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author4
  • middle author13
  • last author37

Across the 55 of 55 papers where every author was matched, so the position is known.

fields
  • quant-ph30
  • hep-th7
  • cs.CC5
  • math.AG3
  • math.OC3
  • cs.DS2
same name
  • Michael Walter — 6 papers, h 2
  • Michael Walter — 6 papers, h 3
  • Michael Walter — 6 papers, h 14
  • Michael Walter — 5 papers, h 3
  • Michael Walter — 4 papers, h 2
  • Michael Walter — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20092023
most citedHolographic duality from random tensor networks

485 citations · 2.2k across the 55 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

quant-ph2022★ 1 cited

(No) Quantum space-time tradeoff for USTCON

Simon Apers, Stacey Jeffery, Galina Pass +1

Undirected st-connectivity is important both for its applications in network problems, and for its theoretical connections with logspace complexity. Classically, a long line of w…

hep-th2022★ 34 cited

Fun with replicas: tripartitions in tensor networks and gravity

Geoff Penington, Michael Walter, Freek Witteveen

We introduce a new correlation measure for tripartite pure states that we call G(A:B:C). The quantity is symmetric with respect to the subsystems A, B, C, invariant under l…

quant-ph2022★ 14 cited

The minimal canonical form of a tensor network

Arturo Acuaviva, Visu Makam, Harold Nieuwboer +4

Tensor networks have a gauge degree of freedom on the virtual degrees of freedom that are contracted. A canonical form is a choice of fixing this degree of freedom. For matrix prod…

quant-ph2022★ 27 cited

Random tensor networks with nontrivial links

Newton Cheng, Cécilia Lancien, Geoff Penington +2

Random tensor networks are a powerful toy model for understanding the entanglement structure of holographic quantum gravity. However, unlike holographic quantum gravity, their enta…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.