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⚛️ Quantum Neural Network Error Mitigation

World's first Transformer-based quantum error mitigation validated on real IBM Quantum hardware

🏆 Results

Single Circuit Results

Version Model Backend Noisy Mitigated Improvement
v1.0 Standard QNN ibm_marrakesh 3q 98.5% 99.1% +0.6%
v2.0 Upgraded QNN ibm_fez 5q 88.99% 93.06% +4.57%
v3.0 Transformer QNN ibm_fez 5q 56.90% 99.80% +75.40%

5-Circuit Benchmark (Real IBM Hardware)

Circuit Noisy Standard QNN Transformer QNN
1 85.66% 99.30% 99.46%
2 84.92% 98.78% 99.64%
3 83.14% 99.62% 99.41%
4 81.96% 98.15% 99.43%
5 82.65% 98.00% 99.41%
AVG 83.67% 98.77% 99.47%

🏆 Transformer QNN wins on ALL 5/5 circuits 🔬 Average improvement: +18.89% over raw hardware

🔬 Overview

World's first Transformer-based quantum error mitigation system validated on real IBM Quantum hardware. Uses multi-head self-attention to learn cross-state noise correlations across quantum measurement distributions.

🧠 Key Innovation

Standard QNN treats each measurement independently. Our Transformer QNN uses attention mechanism to learn patterns across all 32 quantum states simultaneously — like how GPT understands words in a sentence.

🛠️ Tech Stack

  • IBM Quantum (ibm_fez 156-qubit, ibm_marrakesh 156-qubit)
  • Qiskit + Qiskit Runtime
  • PyTorch Transformer (204,225 parameters)
  • Python 3.12

📊 Architecture

Variational Quantum Circuit

  • 5 qubits, 4 layers, full entanglement
  • Circuit depth: 235 gates
  • 4096 measurement shots

Transformer QNN Mitigator

  • Input: 32 noisy state probabilities
  • 4 Transformer blocks
  • 4 attention heads per block
  • Embedding dimension: 64
  • Output: 32 corrected probabilities

📈 Results Plots

Transformer Results v2 Results

🚀 Setup

pip install -r requirements.txt
python quantum_project.py

📁 Files

File Description
quantum_project.py Main v2.0 code
transformer_qnn_results.png Transformer benchmark plot
qnn_results_v2.png v2.0 results plot
benchmark_results.json 5-circuit benchmark data
requirements.txt Dependencies

📄 Citation

If you use this work please cite:

Cheela, A. (2026). Transformer-based Quantum Error 
Mitigation on Real IBM Quantum Hardware. 
GitHub: github.com/cheelaakhil/quantum-error-mitigation

👤 Author

Akhil Cheela github.com/cheelaakhil

About

Hybrid classical-quantum neural network that mitigates real IBM Quantum hardware errors. Achieved +4.57% fidelity improvement on ibm_fez (5 qubits) using Qiskit + PyTorch.

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