Data for benchmarking machine learning models, taken from
Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits.
This dataset contains bit strings sampled from a thermal distributions of an Ising Hamiltonian
that has a connectivity given by a scale-free network with 1000 nodes.
Description of the dataset
The dataset contains bit strings of length 1000 that correspond to spin configurations of the Ising distribution.
The graph describing the two-body interactions of the Ising Hamiltonian corresponds to a scale free network, which is
constructed via the Barabasi-Albert algorithm with connectivity parameter 2. The Ising energy has a local bias that
is dependent on the degree of the corresponding node, which biases the values of that bit towards zero.
The data was generated via a Metropolis Hastings algorithm by sampling a million configurations on eight independent
Markov chains, and then selecting 20000 train and test points randomly from equally spaced points on the chain.
Example usage
>>> [ds] = qml.data.load("other", name="scale-free")
>>>
>>> np.shape(ds.train)
(20000, 1000)
>>> np.shape(ds.test)
(20000, 1000)
