Pith. Sign in

RECORD

Superpositional Quantum Network Topologies

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Christopher Altman, Jaroslaw Pykacz, Roman Zapatrin

Neurons and Cognition · Submitted 12 Nov 2003

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim As a result, not only classical transition functions, but also topology becomes a subject of training.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv IDq-bio/0311016 (version 3)
Pith IDLBMMZPWM, Pith's permanent record of this paper and its review
CitationChristopher Altman, Jaroslaw Pykacz, Roman Zapatrin. "Superpositional Quantum Network Topologies." arXiv:q-bio/0311016 (2003). https://pith.science/paper/LBMMZPWM
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,864 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.15 · human participants 0.02 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.05 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quantum gravity. The main feature of this model is moving from particular neural topologies to a quantum metastructure which embodies many differing topological patterns. Using quantum parallelism, training is possible on superpositions of different network topologies. As a result, not only classical transition functions, but also topology becomes a subject of training. The main feature of our model is that particular neural networks, with different topologies, are quantum states. We consider high-dimensional dissipative quantum structures as candidates for implementation of the model.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quantum gravity. contribution 0.96 0.75 0.08 asserted 0.99
s1 The main feature of this model is moving from particular neural topologies to a quantum metastructure which embodies many differing topological patterns. contribution 0.81 0.46 0.23 asserted 1.00
s2 Using quantum parallelism, training is possible on superpositions of different network topologies. method 0.84 0.34 0.11 hedged possibility 0.68
s3 As a result, not only classical transition functions, but also topology becomes a subject of training. interpretation 0.38 0.51 0.21 asserted 1.00
s4 The main feature of our model is that particular neural networks, with different topologies, are quantum states. contribution 0.76 0.50 0.26 asserted 1.00
s5 We consider high-dimensional dissipative quantum structures as candidates for implementation of the model. future work 0.43 0.27 0.06 hedged possibility 0.84

What kind of paper the classifier reports this is: method or tool 0.59 · evidence from argument 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v3
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quantum gravity. The main feature of this model is moving from particular neural topologies to a quantum metastructure which embodies many differing topological patterns. Using quantum parallelism, training is possible on superpositions of different network topologies. As a result, not only classical transition functions, but also topology becomes a subject of training. The main feature of our model is that particular neural networks, with different topologies, are quantum states. We consider high-dimensional dissipative quantum structures as candidates for implementation of the model.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.