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Micro-optimize basis state conversion #1088

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merged 4 commits into from
Feb 17, 2021
Merged

Micro-optimize basis state conversion #1088

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6f50fd6
Micro-optimize basis state conversion
josh146 Feb 17, 2021
6983075
changelog
josh146 Feb 17, 2021
0100da6
Merge branch 'master' into optimize-samples
josh146 Feb 17, 2021
3d46779
Update pennylane/_qubit_device.py
josh146 Feb 17, 2021
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4 changes: 4 additions & 0 deletions .github/CHANGELOG.md
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Expand Up @@ -82,6 +82,10 @@
(5, 4, 4)
```

* An improvement has been made to how `QubitDevice` generates and post-processess samples,
allowing QNode measurement statistics to work on devices with more than 32 qubits.
[(#)](https://github.com/PennyLaneAI/pennylane/pull/)

<h3>Breaking changes</h3>

* If creating a QNode from a quantum function with an argument named `shots`,
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15 changes: 8 additions & 7 deletions pennylane/_qubit_device.py
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Expand Up @@ -564,8 +564,8 @@ def estimate_probability(self, wires=None):
samples = self._samples[:, device_wires]

# convert samples from a list of 0, 1 integers, to base 10 representation
unraveled_indices = [2] * len(device_wires)
indices = np.ravel_multi_index(samples.T, unraveled_indices)
powers_of_two = 2 ** np.arange(len(device_wires))[::-1]
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indices = samples @ powers_of_two
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This is very clever!


# count the basis state occurrences, and construct the probability vector
basis_states, counts = np.unique(indices, return_counts=True)
Expand Down Expand Up @@ -656,9 +656,10 @@ def marginal_prob(self, prob, wires=None):
# it corresponds to the orders of the wires passed.
num_wires = len(device_wires)
basis_states = self.generate_basis_states(num_wires)
perm = np.ravel_multi_index(
basis_states[:, np.argsort(np.argsort(device_wires))].T, [2] * len(device_wires)
)
basis_states = basis_states[:, np.argsort(np.argsort(device_wires))]

powers_of_two = 2 ** np.arange(len(device_wires))[::-1]
perm = basis_states @ powers_of_two
return self._gather(prob, perm)

def expval(self, observable):
Expand Down Expand Up @@ -696,8 +697,8 @@ def sample(self, observable):
# Replace the basis state in the computational basis with the correct eigenvalue.
# Extract only the columns of the basis samples required based on ``wires``.
samples = self._samples[:, np.array(device_wires)] # Add np.array here for Jax support.
unraveled_indices = [2] * len(device_wires)
indices = np.ravel_multi_index(samples.T, unraveled_indices)
powers_of_two = 2 ** np.arange(samples.shape[-1])[::-1]
indices = samples @ powers_of_two
return observable.eigvals[indices]

def adjoint_jacobian(self, tape):
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