Fidelity-based Deep Adiabatic Scheduling
Authors: Eli Ovits, Lior Wolf
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark our approach on random QUBO problems, Grover search, 3-SAT, and MAX-CUT problems and show that our approach outperforms, by a sizable margin, the linear schedules as well as alternative approaches that were very recently proposed. |
| Researcher Affiliation | Academia | Eli Ovits & Lior Wolf Tel Aviv University |
| Pseudocode | No | The paper describes steps for solving the Schr odinger equation in Appendix C but does not present a formal pseudocode block or algorithm labeled as such. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | In order to train the QUBO problem model, we produced a training dataset of 10,000 random QUBO instances for each problem size: n = 6, 8, 10. The QUBO problems were generated by sampling independently, from the normal distribution, each coefficient of the problem matrix Q. |
| Dataset Splits | No | The paper mentions 'Training loss Validation loss' in Figure 12 in Appendix G, implying a validation set was used, but it does not specify the explicit split percentages or how the validation set was created within the main text. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and SELU activation function, along with batch normalization, but does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | The training was performed using the Adam optimizer (Kingma & Ba, 2014), with batches of size 200. Batch normalization (Ioffe & Szegedy, 2015) was applied during training. A uniform dropout value of 0.1 is employed for all layers during the model training. |