Diagnostic Quantum Circuit Noise Fingerprinting and Mitigation Study
A completed density-matrix study using T1, Ramsey, Bell and GHZ circuits to distinguish amplitude damping, phase damping, depolarising noise and readout error.
Software compatibility
The release uses Qiskit 2.2.3 and Qiskit Aer 0.17.2. It runs locally on a CPU and requires no quantum-cloud account, provider token, or proprietary software.
Project definition
Problem statement
Ideal circuit diagrams do not show how energy relaxation, coherence loss, random Pauli mixing and classical assignment error change measured outcomes.
The physics problem is to implement valid quantum channels, select complementary diagnostic circuits and separate state degradation from readout distortion and finite-shot variation.
Project objectives
- Implement exact density-matrix evolution with tested single-qubit and controlled gates.
- Compare six declared noise profiles across GHZ registers from two to six qubits.
- Use T1 and Ramsey probes to separate population-sensitive and coherence-sensitive behavior.
- Recover four declared quantum-noise parameters from exact diagnostic features.
- Measure finite-shot readout correction and linear zero-noise extrapolation against known ideal values.
Project structure
Project components
Quantum engine
Builds operators, tensor products, states and exact density-matrix evolution with an explicit register convention.
Noise channels
Applies amplitude damping, phase damping and one-qubit or two-qubit Pauli depolarising channels.
Diagnostic circuits
Builds T1, Ramsey, Bell and nearest-neighbour GHZ circuits for complementary physical evidence.
Measurement and mitigation
Simulates finite shots, estimates asymmetric readout probabilities, corrects distributions and fits extrapolated expectations.
Evidence
Retains CSV, JSON, figures, tests, references and complete project documentation.
Methodology
Project workflow
- 01Declare a profile
A validated configuration fixes channel probabilities, register sizes, shot count and seed.
- 02Prepare a probe
The engine constructs the selected T1, Ramsey, Bell or GHZ state.
- 03Apply channels
Declared local channels act after the documented circuit operations.
- 04Measure and compare
Exact and sampled observables are compared with the ideal state and analytical expectations.
- 05Mitigate and verify
Readout correction, extrapolation, Qiskit comparison and automated checks evaluate the release.
Demonstration scenario
The student compares six-qubit GHZ results across four dominant-noise cases, explains why dephasing preserves computational populations while reducing X parity, then shows how readout correction improves measured distributions without restoring the quantum state.
Engineering
Tools and method
- Tools
- The project uses Python 3.12, NumPy, SciPy, Qiskit 2.2.3, Qiskit Aer 0.17.2, Pandas, Matplotlib for subject analysis, simulation, and results.
- Quantum simulation
- Python and NumPy implement transparent dense state and channel calculations for small circuits.
- Parameter recovery
- SciPy performs bounded same-family fitting against the retained diagnostic vector.
- Reference comparison
- Qiskit and Qiskit Aer independently check selected ideal states and amplitude damping.
- Evidence analysis
- Pandas and Matplotlib build retained tables and figures from the completed experiment.
- Reproducibility
- Pinned releases, Docker, exact configuration, automated tests, Word files and PDFs support independent reruns.
Testing
Evaluation
Evaluation measures
- Trace, Hermiticity, positivity and probability normalization
- GHZ fidelity, purity, trace distance, population success and X parity
- T1 and Ramsey fingerprints over six channel depths
- Exact same-family parameter-recovery RMSE
- Finite-shot readout calibration and corrected distribution distance
- Linear zero-noise extrapolation error and dense memory scaling
Project boundaries
- The completed work is a small-circuit controlled simulation, not a measurement from quantum hardware.
- Channels are independent, local and Markovian after declared operations.
- The model excludes leakage, crosstalk, pulse timing, drift, correlated noise, reset error and device topology.
- Exact parameter recovery establishes identifiability only inside the selected model family.
- Mitigation improves selected observables under stated assumptions and does not create fault-tolerant computation.
Included
- 01Complete Python density-matrix simulator
- 02T1, Ramsey, Bell and two to six-qubit GHZ circuits
- 03Six declared quantum-noise profiles
- 04One hundred and ninety-nine retained experiment cases
- 05Six same-family noise-parameter recovery cases
- 06Five independent Qiskit reference comparisons
- 07Eighteen labelled report figures and twenty-one evidence tables
- 08Fifty-four annotated references with literature-image provenance
- 09Complete project files, calculations, results and analysis material in a private GitHub repository
- 1087-page project documentation in PDF and editable Word formats
- 1113-page setup and usage guide in PDF and editable Word formats
- 12Forty-five tests with 99.70 percent branch-aware coverage
Project record
No information is collected on this page.
- Permanent project ID
- GP-PH-1D2U8Y0
- Catalogued
- 21 Aug 2026
- Completed
- 27 Aug 2026
- Verified
- 27 Aug 2026
- Demonstration
- Included in repository
Handover
After purchase
- 01Payment is confirmed
The project is marked unavailable and cannot be purchased again.
- 02Repository access is granted
The buyer's submitted GitHub account receives access to the private repository.
- 03The purchase record is delivered
The certification sheet is prepared from the reviewed buyer details and sent privately by email.