Data for benchmarking machine learning models, generated for an upcoming paper: Typical Machine Learning Datasets as Low-Depth Quantum Circuits.
Description of the dataset
The CIFAR-10 dataset contains 60,000 32×32 color images across 10 categories (e.g., airplanes, cars, birds, and cats), with 6,000 images per category. Here, we provide circuit parameters that approximate the Multi-Channel Representation of Quantum Images (MCRQI) of each image in the CIFAR-10 dataset.
Additional details
qml.state()
outputs a state vector. This state vector must be processed to recover the original image.exact_state
entry contains a list of numpy arrays representing MCRQI states that exactly encode Imagenette images. This significantly increases the file size and can be omitted during download if not needed.Example usage
import pennylane as qml
import jax
[dataset_params] = qml.data.load("low-depth-cifar-10")
def get_circuit(circuit_layout):
dev = qml.device("default.qubit", wires=13)
@jax.jit
@qml.qnode(dev)
def circuit(params):
counter = 0
for gate, wire in circuit_layout:
if gate == "RY":
qml.RY(params[counter], wire)
counter += 1
elif gate == "CNOT":
qml.CNOT(wire)
return qml.state()
return circuit
# Example for running the circuit with depth 4
circuit_layout_d4 = dataset_params.circuit_layout_d4
circuit_d4 = get_circuit(circuit_layout_d4)
state_d4 = circuit_d4(dataset_params.params_d4[0])
# Example for running the circuit with depth 8
circuit_layout_d8 = dataset_params.circuit_layout_d8
circuit_d8 = get_circuit(circuit_layout_d8)
state_d8 = circuit_d8(dataset_params.params_d8[0])
Florian Kiwit, Bernhard Jobst, Andre Luckow, Frank Pollmann, Carlos Riofrío
version 0.1 : initial public release