Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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🌐 Website | 🤗 Dataset | 🛠️ GitHub | 🚀 Interactive Demo

QSBench Depolarizing Demo v1.0.0

Quantum Machine Learning dataset for noise robustness and error prediction. Includes paired ideal and noisy expectation values under depolarizing noise.

Keywords: quantum dataset, noisy quantum circuits, depolarizing noise, QML benchmark, expectation value prediction.

5000 synthetic quantum circuits with depolarizing noise — demo subset of the QSBench Noise Pack.

Designed for researchers and engineers working on noise-aware quantum ML, robustness analysis, and error mitigation.

Why this dataset?

Real quantum hardware is noisy. Depolarizing noise is one of the most widely studied noise models in quantum computing. This dataset allows you to:

Compare ideal vs noisy outputs
Train models that predict or correct noise effects
Benchmark robustness of ML models
Study error distributions in quantum circuits

Use Cases

Noise robustness benchmarking
Error mitigation research
Predicting noisy expectation values
Learning error correction models
Comparing clean vs noisy quantum outputs

Dataset Overview

Samples: 5000
Qubits: 6
Depth: 4
Circuit Families: Mixed (HEA, RealAmplitudes, QFT, Efficient SU(2), Random)
Entanglement: Full
Noise: Depolarizing (p = 0.02–0.03 range)
Observables: Z, X, Y in mixed mode (global + per-qubit)
Shots: 512
Splits: Train / Validation / Test — deterministic hash-based

What's Inside Each Sample

Each sample in the Parquet files contains:

Raw and transpiled QASM representations
Circuit adjacency matrix
Gate statistics (CX, H, RX, RY, RZ, etc.)
Structural metrics: Gate entropy + Meyer-Wallach entanglement
Ideal expectation values
Noisy expectation values (after depolarizing noise)
Explicit error targets: error_<label> = ideal - noisy
Circuit metadata and generation parameters
Deterministic split label

Key Learning Signals

For every observable, the dataset provides: ideal_expval_*, noisy_expval_*, error_*, sign_ideal_*, sign_noisy_*. This enables complex regression and classification tasks for noise modeling.

QSBench-Depolarizing: Quantum Error Prediction

You don't need a PhD in Quantum Physics to use this dataset. Think of this as a classic Predictive Maintenance or Regression problem. We have a machine (the quantum computer) that makes errors. We give you the blueprints of the tasks it ran, and the magnitude of the errors it made.

The ML Mission: Supervised Regression

Your goal is to predict the error without running expensive physics simulations. Can you train a Gradient Boosting model (XGBoost/LightGBM) or a Neural Network to predict the output error based purely on the circuit's structural features?

Dataset Anatomy (Features & Targets)

Use the structural features as X, and the errors as y.

Group	Column Name	What is it for ML?
Features (X)	`depth`, `gate_entropy`, `cx_count`	The structural complexity of the task.
Features (X)	`noise_prob`, `shots`	The environmental conditions (error probability and sampling rate).
Target (y)	`error_Z_global`	The Main Target. The continuous error value you want to predict.
Target (y)	`sign_ideal_Z`, `sign_noisy_Z`	For Classification. Did the noise flip the final answer? (Binary target).

Quick Start Idea

Build a robust XGBoost regressor using total_gates, depth, and noise_prob to predict error_Z_global. What is your Mean Absolute Error (MAE)?

Load the Dataset

The dataset is stored in Parquet format inside the data/shards/ folder. You can load it directly using the Hugging Face datasets library:

from datasets import load_dataset

# Load the depolarizing demo dataset
dataset = load_dataset("QSBench/QSBench-Depolarizing-v1.0.0-demo", split="train")

# Inspect the first sample with noise data
print(dataset[0])

Repository Structure

The dataset is stored in the main branch and contains only the data files to ensure the Dataset Viewer works correctly:

QSBench-Depolarizing-v1.0.0-demo/
├── README.md # This file
└── data/ # Parquet shards (main data)
    └── shards/
        └── *.parquet
        └── *.csv

All metadata files (coverage.json, schema.json, meta.json, etc.) are located in a separate branch called meta.

👉 browse meta branch

Related QSBench Datasets

Part of the QSBench Family

This is a small public demo version. Full-scale datasets (20k–150k+ samples), specialized noisy versions, and custom hardware packs are available.

Repository | Website & Full Catalog

Email: QSBench@gmail.com