---
language: en
license: apache-2.0
tags:
- quantum-computing
- signal-processing
- error-correction
- transformer
---

# quantum_noise_denoiser_v1

## Overview
This model is a specialized Transformer designed to identify and suppress stochastic noise in superconducting quantum circuits. It processes raw pulse sequences or qubit telemetry to classify error types and provide a denoised representation of the underlying quantum state.



## Model Architecture
The architecture is based on a **Dense-Encoder Transformer** optimized for 1D signal sequences:
- **Encoding Layers**: 6 Multi-head Self-Attention blocks.
- **Dimensionality**: Reduced 512-hidden size for low-latency inference.
- **Objective**: Minimization of the Mean Squared Error (MSE) between noisy input signals and pure state theoretical values:
$$MSE = \frac{1}{n} \sum_{i=1}^{n} (Y_i - \hat{Y}_i)^2$$

## Intended Use
- **Real-time Error Mitigation**: Integrated into quantum controllers to correct bit-flip and phase-flip errors during gate execution.
- **Telemetry Analysis**: Post-hoc processing of quantum experiment logs to improve readout fidelity.
- **Calibration**: Assisting in the automated tuning of microwave pulse shapes.

## Limitations
- **Hardware Specificity**: Trained primarily on transmon qubit data; performance on ion traps or photonic systems may vary.
- **High-Frequency Noise**: Limited by the sampling rate of the input signal; noise frequencies above 2GHz may result in aliasing artifacts.
- **State Complexity**: Accuracy decreases as the depth of the quantum circuit (number of gates) increases significantly beyond the training horizon.