---
license: cc-by-4.0
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - quantum-computing
  - benchmarking
  - code-generation
  - qiskit
  - llm-evaluation
size_categories:
  - n<1K
---

# QC-Stark Benchmark

**QC-Stark** is a multi-task benchmark for evaluating large language models on
quantum computing (QC) tasks. It covers the full practitioner workflow—circuit
construction, debugging, compilation, verification, simulation, and error
correction—with systematic difficulty scaling and contamination resistance via
procedural generation.

## Key facts

| Property | Value |
|----------|-------|
| Tasks | 11 |
| Difficulty levels per task | 5 (Textbook → Homework → Exam → Research → Open) |
| Seeds per (task, level) | 5 |
| **Total instances** | **275** |
| Auto-verification | Yes (Qiskit execution) |
| Contamination resistance | Seed-based procedural generation |

## Task list

| Task ID | Name | Workflow Stage | Mean accuracy (10 models) |
|---------|------|----------------|--------------------------|
| T1 | State Preparation | Construct | 0.440 |
| T2 | Trotterization | Construct | 0.252 |
| T3 | Oracle Synthesis | Construct | 0.412 |
| T4 | Debugging | Understand | 0.020 |
| T5 | Noise Discrimination | Understand | 0.272 |
| T6 | Reverse Engineering | Understand | 0.448 |
| T7 | Equivalence Checking | Verify | 0.824 |
| T8 | Hardware Routing | Compile | 0.184 |
| T9 | Noise Fidelity | Simulate | 0.516 |
| T10 | VQE | Simulate | 0.592 |
| T11 | QEC Decoding | Error Correction | 0.440 |

## Schema

Each instance has the following fields:

- `task_id` (string): T1–T11
- `task_code` (string): internal code (e.g. `B1_debugging`)
- `task_name` (string): human-readable task name
- `workflow_stage` (string): QC workflow stage
- `level` (int): difficulty level 1–5
- `difficulty` (string): Textbook / Homework / Exam / Research / Open
- `seed` (int): procedural generation seed 1–5
- `prompt` (string): the full problem statement given to the model

## Evaluation

Responses are verified by executing model-generated Qiskit code against
auto-generated test cases. No human grading. Verification metrics vary
by task (state fidelity, functional equivalence, syndrome correctness, etc.).

## Citation

```bibtex
@misc{gupta2026qcstark,
  title={QC-Stark: A Multi-Task Benchmark Revealing Capability Dissociations
         in LLMs for Quantum Computing Tasks},
  author={Gupta, Pranav},
  year={2026},
  institution={Cisco Collaboration AI}
}
```