# QUANTUMesc Quantum Learning Pack

This folder contains original learning notes, exercises, runnable examples, analytical teaching data, and a seeded simulated sensing dataset for studying quantum information.

## Contents

- `QUANTUM-LEARNING-PATH.md` — staged route from mathematics preparation through foundations, algorithms, noise, and specialist topics.
- `QUANTUM-FOUNDATIONS.md` — concise introduction to states, gates, entanglement, algorithms, noise, and cryptography terminology.
- `EXERCISES.md` and `SOLUTIONS.md` — eight practice questions and a separate answer key.
- `examples/bell_state.py` — dependency-free state-vector simulation of a Bell pair.
- `examples/grover_search.py` — dependency-free simulation of Grover search for a four-item example.
- `examples/quantum_teleportation.py` — dependency-free three-qubit state-vector verification of ideal teleportation.
- `examples/ramsey_sensing.py` — standard-library generator for a seeded synthetic Ramsey-fringe teaching dataset.
- `examples/qiskit_bell.py` — Bell-state circuit using the open-source Qiskit SDK.
- `examples/bell_state.qasm` — an OpenQASM 2.0 Bell circuit.
- `datasets/bell-state-ideal.csv` — exact ideal computational-basis probabilities for |00>, a Bell state, and |++>. This is generated theoretical data, not device measurements.
- `datasets/single-qubit-basis-probabilities.csv` — exact ideal Born-rule probabilities for six qubit states measured in the X, Y, and Z bases. This is generated theoretical data, not device measurements.
- `QUANTUM-SENSING-LAB.md` — guided investigation of phase accumulation, dephasing, and shot noise.
- `datasets/ramsey-fringes-synthetic.csv` and `datasets/ramsey-fringes-synthetic-metadata.json` — reproducible simulated shot counts, model probabilities, parameter values, seed, provenance, and explicit non-hardware status.
- `QUANTUM-BENCHMARKS.md` — comparison guide to QED-C, QASMBench, SupermarQ, and MQT Bench, with distinctions between circuit suites and hardware data plus a reproducibility checklist.

## External experimental data

The downloadable pack contains only original examples and generated teaching data. For real experimental measurements, see:

- Zhang et al., [dataset for “Characterization and Optimization of Tunable Couplers via Adiabatic Control in Superconducting Circuits”](https://doi.org/10.5281/zenodo.16791932). The Zenodo record describes raw measurements, processed/calibrated data, and analysis code; it is released under CC BY 4.0. Cite the dataset DOI and paper ([arXiv:2501.13646](https://arxiv.org/abs/2501.13646)) and comply with attribution terms.
- Horgan et al., [QKD Network Characterisation and Coexistence Datasets](https://doi.org/10.5281/zenodo.21792844), version 1. The Zenodo record describes experimental QBER/secure-key-rate measurements, CSV data, JSON summaries, and documentation from the TCD/CONNECT testbed. Its stated license is CC BY 4.0. The record title uses “Standardised”; QUANTUMesc has not verified external standards-body certification, so do not interpret that wording as a universal QKD standard. Cite the version DOI; its concept DOI is [10.5281/zenodo.21132087](https://doi.org/10.5281/zenodo.21132087).

QUANTUMesc links to the Zenodo source records instead of copying external data. These are individual research releases, not general-purpose performance rankings; consult each record and its documentation before analysis.

That release is a specific tunable-coupler study, not a general quantum-device benchmark. Read the paper and dataset files before drawing conclusions from it.

## Suggested use

1. Follow `QUANTUM-LEARNING-PATH.md`; the first stage assumes basic algebra.
2. Read the foundations guide and attempt exercises before reading the answer key.
3. Run the standard-library examples:

```powershell
python examples\bell_state.py
python examples\grover_search.py
python examples\quantum_teleportation.py
python examples\ramsey_sensing.py
```

4. The Qiskit example has an optional dependency:

```powershell
python -m pip install -r requirements.txt
python examples\qiskit_bell.py
```

The Qiskit SDK is licensed separately under Apache-2.0. Consult its current project license before redistributing Qiskit itself.

## Reuse and attribution

Original prose in the learning path, foundations guide, exercises, and solutions is licensed under **Creative Commons Attribution 4.0 International (CC BY 4.0)**. You may share and adapt it with attribution and a link to the license.

Original source code in `examples/` is licensed under the **MIT License**; see `LICENSE-CODE.txt`.

The generated CSV datasets and the Ramsey JSON manifest are dedicated to the public domain under **CC0 1.0**; see `LICENSE-DATA.txt`. The Ramsey dataset is a seeded simulation, not a device experiment.

These licenses cover only QUANTUMesc-authored material in this folder, not external sources, third-party software, or other catalog items. External textbooks and repositories retain their owners' terms. Free access does not automatically mean open-licensed.

## Catalog metadata conventions

Resource detail pages show access, license, level, prerequisites, format, and source-check status consistently. “Not stated” or “not independently checked” means the catalog has not established that fact; it is not a claim that the source has no license or is unavailable. A recorded check date applies only to that resource and should not be read as a guarantee that a page or dataset has not changed since.

## References

- IBM Quantum Learning, [Basics of quantum information](https://quantum.cloud.ibm.com/learning/courses/basics-of-quantum-information)
- IBM Quantum Learning, [Fundamentals of quantum algorithms](https://quantum.cloud.ibm.com/learning/courses/fundamentals-of-quantum-algorithms)
- IBM Quantum Learning, [General formulation of quantum information](https://quantum.cloud.ibm.com/learning/courses/general-formulation-of-quantum-information)
- IBM Quantum Learning, [Foundations of quantum error correction](https://quantum.cloud.ibm.com/learning/courses/foundations-of-quantum-error-correction)
- MIT Open Learning Library, [Quantum Information Science I](https://ocw.mit.edu/courses/8-370x-quantum-information-science-i-spring-2018/)
- Thomas Wong, [Introduction to Classical and Quantum Computing](https://www.thomaswong.net/introduction-to-classical-and-quantum-computing-2e)
- NIST, [Quantum information science](https://www.nist.gov/quantum-information-science)
- QED-C, [Application-oriented benchmark repository](https://github.com/SRI-International/QC-App-Oriented-Benchmarks)
- OpenQASM, [Language specification](https://openqasm.com/language/index.html)
- QED-C, [Application-Oriented Performance Benchmarks](https://doi.org/10.1109/TQE.2023.3253761)
- Li et al., [QASMBench](https://doi.org/10.1145/3550488)
- Tomesh et al., [SupermarQ](https://arxiv.org/abs/2202.11045)
- Quetschlich et al., [MQT Bench](https://doi.org/10.22331/q-2023-07-20-1062)
- Google Quantum AI and collaborators, [Quantum error correction below the surface-code threshold](https://doi.org/10.1038/s41586-024-08449-y)