QuaDiM: A Conditional Diffusion Model For Quantum State Property Estimation
Authors: Yehui Tang, Mabiao Long, Junchi Yan
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Qua Di M on large-scale QPE tasks using classically simulated data on the 1D anti-ferromagnetic Heisenberg model with the system size up to 100 qubits. Numerical results demonstrate that Qua Di M outperforms baseline models, particularly auto-regressive approaches, under conditions of limited measurement data during training and reduced sample complexity during inference. |
| Researcher Affiliation | Academia | Yehui Tang1, Mabiao Long1, Junchi Yan12 1Sch. of Computer Science & Sch. of Artificial Intelligence, Shanghai Jiao Tong University 2Shanghai Artificial Intelligence Laboratory EMAIL |
| Pseudocode | No | The paper describes methods and equations (e.g., in Section 3.2.1, 3.2.2, 3.2.3) but does not contain a clearly labeled pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for Qua Di M, nor does it include any links to a code repository. |
| Open Datasets | No | We classically simulate relatively large-scale quantum systems with up to 100 qubits to generate extensive training and test datasets for evaluation, showing Qua Di M s scalability and practical applicability. |
| Dataset Splits | Yes | For all the methods, we set N tr = 100 and N te = 20, with the number of qubits in the quantum system L {10, 40, 70, 100}. To construct the training set, we perform repeated measurements of Min = 1000 for each ground state. |
| Hardware Specification | Yes | When reducing inference to Tf = 500 diffusion steps on a single GPU (2080Ti), Qua Di M achieves a lower RMSE score compared to the CS while demonstrating an inference speed comparable to LLM4QPE. |
| Software Dependencies | No | The paper mentions machine learning models (RNN, Transformer) and an optimizer (Adam) but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | In this paper, all the experimental results of Qua Di M are reported for a transformer configuration consisting of 4 heads, 4 layers, and 128 hidden dimensions. The maximum denoising time steps is set to T = 2000. ... For all the methods, we set N tr = 100 and N te = 20, with the number of qubits in the quantum system L {10, 40, 70, 100}. To construct the training set, we perform repeated measurements of Min = 1000 for each ground state. ... A grid search is performed to identify the optimal regularization strength, with candidate values uniformly distributed on a logarithmic scale from 0.001 to 100. We employ a 5-fold cross-validation strategy on the training dataset... The model architecture includes a hidden layer with 128 units and is trained using the Adam optimizer. |