Quantum dataset: entanglement witnesses obtained via optimization for random HEA circuits of 10 qubits.
Description of the Change:
Adds the qdata/witness folder.
This folder has the following structure:
witness
└── 10_qubits
├── 2_layers
│ ├── 0
│ │ ├── input.qasm (QASM for producing the state)
│ │ ├── output.qasm (QASM for producing the witness)
│ │ └── metadata.json (Other information: currently state and witness overlap, and von Neumann entropy associated to the input state partition.)
│ │ ⋮
│ └── 999
│ └── <likewise>
├── 5_layers
│ └── <likewise>
├── 10_layers
│ └── <likewise>
└── 15_layers
└── <likewise>
Benefits:
New dataset; entanglement witnesses are closely related to the task of entanglement detection, for which classical machine learning has been attempted, but suffers strongly from the curse of dimensionality. Thus it is a good candidate for QML approaches.
Possible Drawbacks:
Many files. The original dataset is stored in an hdf5 folder and can easily be compressed to some other "container" format.
Context:
Quantum dataset: entanglement witnesses obtained via optimization for random HEA circuits of 10 qubits.
Description of the Change:
Adds the
qdata/witnessfolder.This folder has the following structure:
Benefits:
New dataset; entanglement witnesses are closely related to the task of entanglement detection, for which classical machine learning has been attempted, but suffers strongly from the curse of dimensionality. Thus it is a good candidate for QML approaches.
Possible Drawbacks:
Many files. The original dataset is stored in an
hdf5folder and can easily be compressed to some other "container" format.Related GitHub Issues:
None.