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qiskit-advocate / qamp-spring-23

Qiskit advocate mentorship program (QAMP) Spring 23 cohort (April - July 2023)
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Quantum inspired variational algorithm #20

Open sonaQC opened 1 year ago

sonaQC commented 1 year ago

Description

In this project, we aim to use extend and run a new class of variational algorithms in IBM quantum hardware.

Deliverables

benchmarking the algorithm for different data and study the effect of noise

Mentors details

@https://github.com/sonaQC

Number of mentees

1

Type of mentees

Graduate student familiar with quantum many body physics and variational algorithms

ichen17 commented 1 year ago

Hi, Sona. I'm interested in this topic. I hope I can work on that.

Qcatty commented 1 year ago

Hi, Sona. I am interested too in this project!

deveshq commented 1 year ago

Hi @sonaQC, I'm interested in this project. I'm currently working with variational algorithms. Hope to dive deeper with this project. Would like to discuss about the project.

PaulaGarciaMolina commented 1 year ago

Hi @sonaQC! I am currently working on quantum-inspired algorithms using tensor networks. I would like to know more about this project. Are there any references available or could we discuss a little bit about your proposal?

BStar14 commented 1 year ago

Hi @GemmaDawson, I joined this issue!

BStar14 commented 1 year ago

Checkpoint 1 PPT B.Kim, MBL hidden Born machine, QAMP Spring 23 CP1.pptx

BStar14 commented 1 year ago

Checkpoint 2 I have followed up on previous works done on the reference article: W. Zhong, X. Gao, S. F. Yelin, and K. Najafi. Having this reference, our project will first focus on its benchmark and the analysis of algorithm complexity or circuit depth. I worked on implementing the Qiskit runtime code to compute this algorithm on the QASM simulator. The job result is presented below in the figure. I also skimmed through the variational quantum eigensolver (VQE) algorithm and theories on the unitary coupled cluster with single and double excitations (UCCSD) ansatz, comparing it with the implementation of the many-body localized (MBL) Hamiltonian. Now the next step would be a complexity analysis of the MBL Hamiltonian simulation while benchmarking its performance on real quantum devices. Then we may compare it with other ansatzes such as UCCSD, which are applied to VQE. mbl_checkpoint2

BStar14 commented 9 months ago

Uploading the final PPT. Meanwhile, we are continuing this research afteron, so the notebook and codes are still in a private repo. Please get in touch if you are interested in this project :) B.Kim, MBL hidden Born machine, QAMP Spring 23 Final.pptx