Your team's name (if your team took part in the QHack Coding Challenge, the name here should match)
Project Description:
Project Description:
In general markets, the competitive equilibrium, or more generally, Dynamic Stochastic General Equilibrium (DSGE) is characterized by a set of state variables and the consumption and production plans of each agent to maximize the utility. Such utility maximization problem has been traditionally dealt with Lagrangian methods. In this Hackathon project, we demonstrate a quantum approach to solving utility maximization problem. Specifically, we employed Quantum Reinforcement Learning to train the policy that determines the agent's actions.
Challenges:
Team Name: Pending
Your team's name (if your team took part in the QHack Coding Challenge, the name here should match)
Project Description:
Project Description: In general markets, the competitive equilibrium, or more generally, Dynamic Stochastic General Equilibrium (DSGE) is characterized by a set of state variables and the consumption and production plans of each agent to maximize the utility. Such utility maximization problem has been traditionally dealt with Lagrangian methods. In this Hackathon project, we demonstrate a quantum approach to solving utility maximization problem. Specifically, we employed Quantum Reinforcement Learning to train the policy that determines the agent's actions. Challenges:
Presentation:
https://github.com/FinnyLime/Quantum-DSGE/blob/main/Utility%20Maximization%20with%20QRL.ipynb
Source code:
https://github.com/FinnyLime/Quantum-DSGE
Which challenges/prizes would you like to submit your project for?
Quantum Finance Challenge IBM Qiskit Challenge Hybrid Algorithms Challenge Young Scientist Challenge QAOA Challenge