Open Dataset · Two-Level Systems

Coherent Control of Two-Level Systems

QTLS — a dataset of measured ring-downs and matched numerical simulations of coupled two-level-system defects under pulsed microwave control.

15Experiments
8Measured
7Simulated
15Experiments
120M+Long-format rows
PKL · CSVRelease formats
10 mKBase temperature
01 — The dataset

A single-particle window into the noise of quantum hardware

Two-level systems (TLS) are microscopic defects that behave like tiny quantum switches, and a leading source of decoherence in superconducting quantum processors. QTLS packages the raw ring-down measurements and matched numerical simulations that probe how those defects respond to — and can be steered by — shaped microwave drives.

Measured

Homodyne ring-downs

Raw in-phase (I) and quadrature (Q) samples acquired exactly as measured — nothing cropped or post-processed. Magnitude, phase and spectra are all recoverable from I/Q.

Simulated

Lindblad dynamics

Numerical evolution of coupled TLS ensembles under a Lindblad master equation, capturing the collective excitation ⟨σ⁺σ⁻⟩ as it rings down after pulsed driving.

Long format

One row per sample

Every dataset is tall and thin — one row per time sample, with the drive settings repeated on each row, and a machine-readable JSON data dictionary alongside.

02 — Experiments

Every knob, swept and recorded

The full release, in order. Each experiment isolates one control axis and records the complete ring-down at every setting — select any card to open its documentation.

03 — Access

Reproducible, top to bottom

Every dataset regenerates from a single deterministic script with a pinned physics module. The data files are rebuilt on demand; the code is the deliverable.

# build a simulated dataset (deterministic) python experiment_8_dataset_creation.py --workers 16 # load a released table import pickle, pandas as pd with open("experiment_8_dataset_long.pkl", "rb") as fh: payload = pickle.load(fh) df = pd.DataFrame(payload["data"], columns=payload["columns"])