1. Overview

The 2nd Quantum AI Online Challenge is a hands-on hackathon where participants design quantum machine learning (QML) models, submit their quantum circuits as JSON, and see their performance evaluated on a hidden test set using a GPU-based grading system. Participants start from tutorials, experiment with quantum circuits in simulation, and compete on a public leaderboard. Finalists will have the opportunity to run their models on a real quantum device (details to be announced).

2. Theme

“From Quantum Circuits to Real-World Inference”
Design, implement, and evaluate a quantum model that performs classification on a given dataset, under fixed resource constraints (qubit count, circuit depth, measurement scheme, etc.).

Both non-developers and AI/ML developers are welcome. Official tutorials will guide complete beginners from basic concepts to submitting a working model.

3. Organizers & Sponsors

Hosts / Organizers
- NORMA
- AIFactory
Sponsors
- School of Quantum at Korea University
- Rigetti Computing
- KT cloud
- KOLON BENIT
Partner / Cooperation
- QISCA
- QUDORA TECHNOLOGIES
- QSL

4. Important Dates

Exact dates will be announced on the official AIFactory challenge page. The overall structure will be:

Registration Period: ~ January 23, 2026, 17:00
Team Building Deadline: TBD
On-site Preliminary Kickoff & Seminar (Korea University) : December 17, 2025 (Korea University Hana Square)
Online 1st Challenge (simulation on AIFactory): December 17, 2025, 17:00 ~ January 23, 2026, 17:00
Announcement of Finalists: TBD
On-site Final Round Kickoff & Seminar : February 3, 2026 (Sejong Convention Center)
Online 2nd Challenge : February 3, 2026 ~ February 20, 2026
Award Ceremony: February 27, 2026 (TBC)

5. Eligibility & Team Formation

Who can join
- AI/ML developers, software engineers, data scientists
- Students and non-CS majors with an interest in quantum computing
Team size: 1–5 members per team
Participants must agree to the competition rules and code of conduct.

6. How to Participate

Click [Join] on the challenge page and complete the registration form.
Form or join a team (up to 5 members).
Complete the pre-challenge tutorials:
- Basics of quantum circuits and the chosen framework (e.g., PennyLane + PyTorch)
- How to encode data into quantum circuits
- How to prepare the final JSON submission
Download the starter kit:
- Example Jupyter notebooks
- Environment setup scripts
- Sample submission JSON file
Design and train your quantum model in your local or cloud environment.
Generate the required JSON submission file and upload it to the AIFactory platform.
Monitor your score and visual diagnostics on the leaderboard page.

7. Tutorials & Learning Materials

8. Task, Data & Constraints

Task: Build a quantum model that classifies data points into given classes.
Data:
- A training set with 16 data is provided in the starter kit.
- A separate hidden test set(2000 data) is stored only on the organizer’s side and is never released to participants.
Constraints (details in the official problem document):
- Maximum number of qubits : 8
- Maximum circuit depth / number of layers : None
- Allowed measurement scheme : 2 qubit probability measure
- Any limits on number of parameters or evaluation time : None
Submissions must follow these constraints to be considered valid.

9. Submission Format (JSON with Quantum Circuit)

In this second edition, participants do not submit prediction CSV files. Instead, each submission is a JSON file describing:

{
  "measurements": [m0, m1],   // integer indices or configuration parameters
  "qasm": "OPENQASM 3.0; ..." // quantum circuit in QASM format
}

qasm

A string containing the quantum circuit in the agreed QASM format
Must compile and run in the official grading environment

measurements

A pair of integers (or a small list) specifying which qubits to measure.

Each upload should contain one JSON file per submission following the above structure. Invalid JSON or circuits that fail to run will receive a score of 0.

10. Submission Limit (Daily)

Each team can submit up to 5 submissions per day (KST, UTC+9).

11. Automatic Server-Based Grading Pipeline

All submissions are evaluated with an automatic grading system running on a GPU or CPU server. The pipeline (corresponding to the diagram you provided) works as follows:

Participant Upload
- The team uploads a JSON file of the form
  {"measurements": (int, int), "qasm": "<QASM string>"} via the AIFactory submission page.
Forward to Grading Service
- The JSON is sent to the Grade Submission function running in a GPU or CPU environment.
Hidden Test Inference
- The grading function:
  - Loads the hidden test dataset stored internally.
  - Builds the quantum circuit from the qasm string.
  - Applies the circuit to the entire hidden test set (using the defined data-encoding and measurement scheme).
  - Produces predicted labels for all test samples.
Metric Computation & Visualization
- Using the ground-truth labels, the system computes:
  - Test accuracy (acc) as the main ranking metric.
  - Phase diagrams for each class or decision region.
  - Confusion matrix and error summary (per-class accuracy, number of errors, etc.).
Leaderboard Update
- The accuracy score is automatically written to the database and immediately reflected on the public leaderboard.
Feedback to Participant
- On the submission result page, each team can see:
- Phase diagram plots showing decision boundaries / sample distribution.
- Score detail panel, including:
  - Overall accuracy
  - Per-class stats
  - Confusion matrix
  - Any warnings (invalid gates, constraint violations, etc.)
This pipeline ensures:
- No leakage of the hidden test set
- Reproducible, fair evaluation for all teams
- Rich visual feedback to help participants debug and improve their circuits.

12. Evaluation & Ranking

1st Challenge

Primary metric: Classification accuracy on the hidden test set.
Submissions are ranked by accuracy in descending order.
Additional checks:
- Constraint violations (qubit count, circuit depth, runtime) may lead to penalties or disqualification.
- In the event of a tie, the submission with the smaller CNOT gate count will be ranked higher. (More about tiebreakers in the detailed problem statement.)
The top 10 teams on the 1st Challenge online leaderboard advance to the 2nd Challenge.

2nd Challenge

Teams who qualified through the 1st Challenge will tackle a new problem under hardware-compatible constraints.
The 2nd Challenge problem will be released during the 2nd Challenge kickoff event on February 3rd 2026 KST.
Circuits will be executed on a real quantum device provided by the sponsor.
Final ranking will be based on criteria such as (TBD):
- Accuracy on hardware runs (averaged across shots)
- Robustness against noise
- Quality of explanation and presentation (if applicable; e.g., short talk or poster)

13. Prizes

Planned awards:

🏆 Grand Prize (Name not yet determined) : $7,000
🥇 Gold Prize (Name not yet determined) : $3,500
🥈 Silver Prize A (Name not yet determined) : $2,000
🥈 Silver Prize B (Name not yet determined) : $2,000
🏅 Bronze Prize A (Name not yet determined) : $1,000
🏅 Bronze Prize B (Name not yet determined) : $1,000
🎖️ Honorable Mention : $700

14. Code of Conduct & IP

Participants must submit original work. Plagiarism or unauthorized copying of other teams’ solutions is strictly prohibited.
Sharing or attempting to reconstruct the hidden test data is not allowed.
By joining, participants agree that anonymized results and circuits may be used for:
- Research publications
- Educational materials
- Future tutorial examples (Specific IP and licensing terms will be clarified in the official rules.)

15. Contact

General inquiries & technical questions:
- AIFactory Q&A board on the challenge page
Sponsorship & partnership inquiries:
- Email: contact@aifactory.page

Rigetti Quantum Hardware Guide - Part A