Citation notice #570 · 2026-07-11 03:18:50.352378+00:00

Quantum Injection Pathways for Implicit Graph Neural Networks

cites Parameterized quantum circuits as machine learning models , volume=, which carries a correction notice dated 2019-12-04. One-hop deterministic notice: the citation edge exists in the Pith bibliography graph; no model judged whether the citation was load-bearing.

This is not a judgment on the citing paper.

Citing paper Event page Original DOI Notice DOI File a formal challenge All reference changes

01Evidence

Raw extraction · citation context · bibliography index 12

In parallel to these developments in classical implicit mod- eling, quantum machine learning has developed a broad family of hybrid quantum-classical models built on theencode- unitary-measurepattern, in which classical data are encoded into a quantum state by a data-dependent unitary, processed by a parametrized quantum circuit (PQC), and read out through measurements of chosen observables [11], [12]. Applied to graph-structured data, this template has been instantiated as quantum graph kernels, quantum graph neural networks, hy- brid graph classifiers, and graph-generation pipelines [13]- [16]. Recent work has also used graph neural networks to an- alyze parameterized quantum circuits themselves [17]. These models, however, remainexplicit finite-depth architectures.

Parser render (TeX stripped for reading; raw above is the evidence)

In parallel to these developments in classical implicit mod- eling, quantum machine learning has developed a broad family of hybrid quantum-classical models built on theencode- unitary-measurepattern, in which classical data are encoded into a quantum state by a data-dependent unitary, processed by a parametrized quantum circuit (PQC), and read out through measurements of chosen observables [11], [12]. Applied to graph-structured data, this template has been instantiated as quantum graph kernels, quantum graph neural networks, hy- brid graph classifiers, and graph-generation pipelines [13]- [16]. Recent work has also used graph neural networks to an- alyze parameterized quantum circuits themselves [17]. These models, however, remainexplicit finite-depth architectures

02Event

Type

Correction

Source

Crossref

Original DOI

10.1088/2058-9565/ab4eb5

Notice DOI

10.1088/2058-9565/ab5944

Date

2019-12-04

Title

Erratum: Parameterized quantum circuits as machine learning models (2019 <i>Quant. Sci. Tech.</i> <b>4</b> 043001)

Reasons

['Correction']

Work

Parameterized quantum circuits as machine learning models , volume= (2058) Quantum Science and Technology

Schema constants (for re-runners): correction · crossref

03Dispute this notice

If this citation does not depend on the flagged claim, or the event is wrong, say so. Disputes are public. For a signed challenge against the paper itself, use the formal challenge form.