Topological data analysis on noisy quantum computers
Authors: Ismail Yunus Akhalwaya, Shashanka Ubaru, Kenneth L. Clarkson, Mark S. Squillante, Vishnu Jejjala, Yang-Hui He, Kugendran Naidoo, Vasileios Kalantzis, Lior Horesh
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The algorithm was successfully executed on quantum computing devices, as well as on noisy quantum simulators, applied to small datasets. Preliminary empirical results suggest that the algorithm is robust to noise. |
| Researcher Affiliation | Collaboration | IBM Research, USA and South Africa University of the Witwatersrand, South Africa Royal Institution, UK |
| Pseudocode | Yes | Algorithm 1 NISQ-TDA Algorithm |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions generating 'random sets of n = 64 sample points in three dimensions from Gaussian and exponential distributions' and using 'CMB data', but it does not provide concrete access information (link, DOI, specific repository, or citation with author/year for the specific data used in their experiments) for public availability of these datasets. |
| Dataset Splits | Yes | Therefore, we next employ a Bayesian (learning) package called Bayes TDA (Maroulas et al., 2020) with train (30) and test (20) sample sets |
| Hardware Specification | Yes | For the quantum hardware, we employed the public-cloud accessible H1 12-qubit trapped-ion quantum computer from Quantinuum (powered by Honeywell) (Honeywell, 2022). |
| Software Dependencies | No | The paper mentions using 'the GUDHI (Maria et al., 2014) package' and 'a Bayesian (learning) package called Bayes TDA (Maroulas et al., 2020)', but it does not specify version numbers for these software dependencies. |
| Experiment Setup | Yes | For this we chose complexes with large eigenvalue gaps, and sufficiently many random vectors and shots. The Chebyshev parameters we selected are such that, in the noise-free scenario, the algorithm would calculate the Betti number almost perfectly... We present results from extensive noisy quantum simulations of the non-qubitized version of the algorithm. The right plot shows the mean and the variance (as error bars) of the Betti number estimated as a function of the number of random vectors nv with n = 8 vertices, degree m = 5 and the noise-level: (0.001, 0.01). |