Adaptive Observation Cost Control for Variational Quantum Eigensolvers
Authors: Christopher J. Anders, Kim Andrea Nicoli, Bingting Wu, Naima Elosegui, Samuele Pedrielli, Lena Funcke, Karl Jansen, Stefan Kühn, Shinichi Nakajima
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiment, We demonstrate the performance of our adaptive cost control method, following the same experimental setup as in Nicoli et al. (2023a). Our Python implementation based on Qiskit (Abraham et al., 2019), one of the most widely used library for developing and simulating quantum programs, is available at https://github.com/angler-vqe/subscore. [...] Figure 3 shows the achieved energy (18) and the fidelity (19) as the difference from the ground truth with the cumulative number of measurement shots in the horizontal axis. |
| Researcher Affiliation | Academia | 1Berlin Institute for the Foundations of Learning and Data (BIFOLD) 2Technische Universit at Berlin, Germany 3Transdisciplinary Research Area (TRA) Matter, University of Bonn, Germany 4Helmholtz Institute for Radiation and Nuclear Physics (HISKP), University of Bonn, Germany 5CQTA, Deutsches Elektronen-Synchrotron (DESY), Zeuthen, Germany 6RIKEN Center for AIP, Japan. |
| Pseudocode | Yes | Algorithm 1 (Subs Co Re Algorithm) and Algorithm 2 (Subs Co Re Subroutine to identify the number of shots) in Appendix D.2. |
| Open Source Code | Yes | Our Python implementation based on Qiskit (Abraham et al., 2019), one of the most widely used library for developing and simulating quantum programs, is available at https://github.com/angler-vqe/subscore. |
| Open Datasets | No | The paper simulates a quantum system based on quantum Heisenberg Hamiltonian and Ising Hamiltonian, which are theoretical models, not datasets in the traditional sense that are publicly accessible. No specific dataset source or access information is provided. |
| Dataset Splits | No | The paper simulates a quantum system and does not use or mention traditional datasets with train/validation/test splits. |
| Hardware Specification | Yes | All experiments were conducted on Intel Xeon Silver 4316 @ 2.30GHz CPUs. |
| Software Dependencies | No | The paper mentions Our Python implementation based on Qiskit (Abraham et al., 2019), but it does not specify version numbers for Python or Qiskit. |
| Experiment Setup | Yes | Table 1. Algorithm-specific choice of parameters for EMICo Re and Subs Co Re for all experiments (unless specified otherwise). and Table 2. Default choice of circuit parameters and hyperparameter optimization (for EMICo Re and Subs Co Re) in all experiments (unless specified otherwise). These tables list specific hyperparameter values and experimental settings. |