Hybrid algorithm for predicting stock prices

[MaldoAlberto]

Description

The proposal of this project is to generate a quantum circuit that can represent a Quantum RNN and make the connection with a classical model with the purpose of making a prediction of sotck prices. For this purpose we consider as reference the papers ref1, ref2.

Deliverables

Develop the implementation of an RNN in Qiskit.
Prepare a draft for possible submission to a journal.

Mentors details

• Mentor
  • Name: Alberto Maldonado
  • GitHub ID: @MaldoAlberto
  • What they do: PhD Student

Number of mentees

1

Type of mentees

• Mentee
  • Required:
    • Interest in Variational quantum Circuit
    • Proficiency in Python and Qiskit
  • Nice to have:
    • Experience using the QML module of Qiskit

[robertloredo]

@MaldoAlberto @HuangJunye perhaps we can combine efforts here as there might be some similarity/overlaps based on project. Please see issue #29 which I created.

[robertloredo]

@anamariarojas123 Hi Ana, here is the other issue I mentioned that might be a good collaborative link. See my message above. :)

[SiddharthaMorales]

Hi, I'm a mentee in this project

[HuangJunye]

@robertloredo @MaldoAlberto @SiddharthaMorales @jamesphysics Is this project now merged with #29 ? If so can you please close one of the issue and upload the presentation slides for checkpoint 1? Thank you.

[SiddharthaMorales]

Sorry about the slides, here they are.
Hybrid algorithm for predicting stock prices #38.pdf

@HuangJunye We haven't been able to set up a meeting and talk with james about joining project, could you contact him?

[HuangJunye]

@SiddharthaMorales Can you please provide your checkpoint 2 updates? Instructions are given in the slack channel.

[SiddharthaMorales]

Project Update May 8

We managed to reproduce the famous example of a LSTM neural network:
https://towardsdatascience.com/a-quantum-enhanced-lstm-layer-38a8c135dbfa

in qiskit.

Now we will move forward to use different QNN models and not only the expectation value of Z operators.

We will explore different encodings, optimizators and ansatzes.