Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Equivariant Quantum Graph Circuits
Authors: Peter Mernyei, Konstantinos Meichanetzidis, Ismail Ilkan Ceylan
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify the expressive power of EQGCs through a dedicated experiment on synthetic data, and additionally observe that the performance of EQGCs scales well with the depth of the model and does not suffer from barren plateu issues. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Oxford, Oxford, UK. 2Charm Therapeutics, London, UK. 3Cambridge Quantum Computing and Quantinuum, Oxford, UK. |
| Pseudocode | No | The paper describes algorithms and constructions in prose and mathematical notation but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not include an explicit statement about releasing code for the methodology or a direct link to a source-code repository. |
| Open Datasets | No | The paper mentions creating a 'synthetic dataset of 6 to 10-node graphs' but does not provide any information about its public availability, such as a link, DOI, or a formal citation. |
| Dataset Splits | No | The paper states, '8-cycle graphs were reserved for evaluation, while all others were used for training.' This describes a training and evaluation (test) split, but not a distinct validation split with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cluster specifications) used for running the experiments. It only mentions 'simulating quantum computers classically'. |
| Software Dependencies | No | The paper mentions 'the Adam optimizer was used' but does not specify its version number or any other software dependencies with version information. |
| Experiment Setup | Yes | Each node state was initialized as |+ = 1/√2, then an equal number k ∈ {1, . . . , 14} general node and edge layers were applied alternatingly. After measurement, the fraction of observed |1 s was used to predict the input s class through a learnable nonlinearity. Exact probabilities of possible outcomes were calculated, and the Adam optimizer was used to minimize the expected binary cross-entropy loss for 100 epochs, with an initial learning rate of 0.01 and an exponential learning rate decay with coefficient 0.99 applied at each epoch. |