QSBench/QSBench-Readout-Demo-v1.0.0

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QSBench Readout Error Demo v1.0.0

Measurement noise dataset — focuses on readout (measurement) errors, one of the most critical and impactful noise sources in quantum expectation value estimation.

This demo uses the dedicated readout noise model with asymmetric flip probabilities (p0 and p1).

2048 high-quality synthetic quantum circuits with realistic readout errors.

Designed for researchers and engineers working on readout error mitigation, accurate expectation value prediction, and noise-aware quantum machine learning.

Why this dataset?

Readout errors often dominate the total error budget in quantum computations. Unlike symmetric depolarizing noise, readout errors are asymmetric (different probability of flipping 0→1 and 1→0). This dataset allows you to:

• —Train and evaluate readout error mitigation techniques
• —Study how measurement noise distorts different observables (Z, X, Y)
• —Benchmark robustness of QML models to realistic measurement errors
• —Compare the impact of readout noise versus relaxation noise

Use Cases

• —Readout error mitigation research
• —Accurate expectation value prediction under measurement noise
• —Benchmarking noise robustness of quantum classifiers and regressors
• —Feature engineering for measurement-aware quantum ML
• —Comparing different error mitigation strategies

Dataset Overview

• —Samples: 2048
• —Qubits: 8
• —Depth: 6
• —Circuit Families: Mixed (HEA, RealAmplitudes, QFT, Efficient SU(2), Random)
• —Entanglement: Full
• —Noise: Readout Error (p0 = 0.02, p1 = 0.015)
• —Observables: Z, X, Y in mixed mode (global + per-qubit)
• —Shots: 1024
• —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 readout errors)
• —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 both regression tasks and binary classification of sign flips caused by measurement errors.

QSBench-Readout: Asymmetric Measurement Noise

You don't need a PhD in Quantum Physics to use this dataset. This dataset represents the real-world measurement imperfections you encounter when running circuits on actual quantum hardware.

The ML Mission: Complex Tabular Regression

Your goal is to build models that can understand and compensate for asymmetric readout errors, which behave differently depending on the prepared quantum state.

Dataset Anatomy (Features & Targets)

Quick Start Idea

Investigate whether highly entangled states suffer more from readout errors or if the effect is mostly state-independent.

Load the Dataset

from datasets import load_dataset
# Load the readout error demo dataset
dataset = load_dataset("QSBench/QSBench-Readout-Demo-v1.0.0", split="train")
print(dataset[0])

Repository Structure

The dataset is stored in the main branch and contains only the data files:

QSBench-Readout-Demo-v1.0.0/
├── README.md
└── data/
    └── shards/
        └── *.parquet

All metadata files (meta.json, schema.json, coverage.json, etc.) are in the metadata branch. 👉 browse metadata branch

Related QSBench Datasets

• —Core Pack (2k samples)
• —Depolarizing Pack (5k samples)
• —Amplitude Damping Pack (5k samples)
• —Transpilation Pack (5k samples)
• —Thermal Relaxation (2k samples)
• —Realistic hardware-mimic (2k samples)

Part of the QSBench Family

his is a small public demo version. Full-scale Readout Noise Pack and other specialized releases are available on the QSBench website.

Email: QSBench@gmail.com

Notes

• —Fully synthetic dataset generated with Qiskit Aer

• —No real-world or personal data

• —License: CC BY-NC 4.0 (Personal & Research Use)

Questions or custom requests? Visit QSBench website or open an issue on GitHub.

Support QSBench

You can support the project directly on this Giveth page: [https://giveth.io/project/qsbench](https://giveth.io/project/qsbench)

Your donations help us generate larger datasets, cover GPU costs, and continue developing new realistic noise models.

Generated with QSBench Generator v5.1.0