Qiskit Fall Fest Quandela 2
Use Quantum Machine Learning (QML) to predict future option prices from historical data.
Hosted by
Qiskit Fall Fest
Scoring Criteria
| Name | Description | Weight |
|---|---|---|
| Technical Aspects | How accurate is the model? How complex is the quantum algorithm? Is it well optimized? Can the architecture serve users at a reasonable scale? How accessible is the end user application? Is it easy to use and intuitive for end users? Did the team use any significant parts of the Qiskit SDK, Qiskit Runtime, or other parts of the Qiskit ecosystem? Could the quantum algorithm be implemented with realistic hardware constraints and noise? Did the team compare the quantum algorithm to a classical benchmark? Did the team consider creative use of quantum properties? | 25% |
| Originality and uniqueness | How unique is this project compared to others? How interesting is it? Did the team attempt something new or difficult? | 25% |
| Usefulness and complexity | How useful is the project and how well-designed is it? How functional is it at the time of judging? Can it be used in real-world business applications or serve as a valuable tool for individuals? Are there ways this project could be further built out and refined upon? | 25% |
| Presentation | How well did the team present their project? Were they able to explain their decision? Did the entire team have a chance to speak? Did they tell a cohesive story? | 25% |