Noise Scaling for LRE

mitiq/lre/__init__.py

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+# Copyright (C) Unitary Fund
+#
+# This source code is licensed under the GPL license (v3) found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Methods for scaling noise in circuits by layers and using multivariate extrapolation."""

mitiq/lre/multivariate_scaling/layerwise_folding.py

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+# Copyright (C) Unitary Fund
+#
+# This source code is licensed under the GPL license (v3) found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Functions for layerwise folding of input circuits to allow for multivariate
+extrapolation."""
+
+import itertools
+from copy import deepcopy
+from typing import Any, Callable, List, Optional, Tuple
+
+import numpy as np
+from cirq import Circuit
+
+from mitiq import QPROGRAM
+from mitiq.utils import _append_measurements, _pop_measurements
+from mitiq.zne.scaling import fold_gates_at_random
+from mitiq.zne.scaling.folding import _check_foldable
+
+
+def _get_num_layers_without_measurements(input_circuit: Circuit) -> int:
+    """Checks if the circuit has non-terminal measurements and returns the
+    number of layers in the input circuit without the terminal measurements.
+
+        Args:
+            input_circuit: Checks whether this circuit is able to be folded.
+
+        Raises:
+            UnfoldableCircuitError:
+                * If the circuit has intermediate measurements.
+                * If the circuit has non-unitary channels which are not
+                terminal measurements.
+
+        Returns:
+            num_layers: the number of layers in the input circuit without the
+            terminal measurements.
+    """
+    circuit = deepcopy(input_circuit)
+    _check_foldable(circuit)
+    _pop_measurements(circuit)
+    num_layers = len(circuit)
+    return num_layers
+
+
+def _get_chunks(
+    input_circuit: Circuit, num_chunks: Optional[int] = None
+) -> List[Circuit]:
+    """Splits a circuit into approximately equal chunks.
+
+    Adapted from:
+    https://stackoverflow.com/questions/2130016/splitting-a-list-into-n-parts-of-approximately-equal-length
+
+        Args:
+            input_circuit: Circuit of interest.
+            num_chunks: Number of desired approximately equal chunks
+            * when num_chunks == num_layers, the original circuit is returned
+            * when num_chunks == 1, the entire circuit is chunked into 1 layer
+
+
+        Returns:
+            split_circuit: Circuit of interest split into approximately equal
+            chunks
+    """
+    num_layers = _get_num_layers_without_measurements(input_circuit)
+    if num_chunks is None:
+        num_chunks = num_layers
+
+    if num_chunks == 0:
+        raise ValueError("The number of chunks should be >= to 1.")
+
+    if num_chunks > num_layers:
+        raise ValueError(
+            "Number of chunks > the number of layers in the circuit."
+        )
+
+    k, m = divmod(num_layers, num_chunks)
+    split_circuit = (
+        input_circuit[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)]
+        for i in range(num_chunks)
+    )
+    return list(split_circuit)
+
+
+def _get_scale_factor_vectors(
+    input_circuit: Circuit,
+    degree: int,
+    fold_multiplier: int,
+    num_chunks: Optional[int] = None,
+) -> List[Tuple[Any, ...]]:
+    """Returns the patterned scale factor vectors required for multivariate
+    extrapolation.
+
+        Args:
+            input_circuit: Circuit to be scaled
+            degree: Degree of the multivariate polynomial
+            fold_multiplier: Scaling gap required by unitary folding
+
+        Returns:
+            scale_factor_vectors: Multiple variations of scale factors for each
+            layer in the input circuit
+    """
+
+    circuit_chunks = _get_chunks(input_circuit, num_chunks)
+    num_layers = len(circuit_chunks)
+
+    # find the exponents of all the monomial terms required for the folding
+    # pattern
+    pattern_full = []
+    for i in range(degree + 1):
+        for j in itertools.combinations_with_replacement(range(num_layers), i):
+            pattern = [0] * num_layers
+            # get the monomial terms in graded lexicographic order
+            for index in j:
+                pattern[index] += 1
+            # use fold multiplier on the folding pattern to figure out which
+            # layers will be scaled
+            pattern_full.append(tuple(fold_multiplier * np.array(pattern)))
+
+    # get the scale factor vectors
+    # the layers are scaled as 2n+1 for unitary folding
+    return [
+        tuple(2 * num_folds + 1 for num_folds in pattern)
+        for pattern in pattern_full
+    ]
+
+
+def multivariate_layer_scaling(
+    input_circuit: Circuit,
+    degree: int,
+    fold_multiplier: int,
+    num_chunks: Optional[int] = None,
+    folding_method: Callable[
+        [QPROGRAM, float], QPROGRAM
+    ] = fold_gates_at_random,
+) -> List[Circuit]:
+    """Defines the noise scaling function required for Layerwise Richardson
+    Extrapolation."""
+    circuit_copy = deepcopy(input_circuit)
+    terminal_measurements = _pop_measurements(circuit_copy)
+
+    scaling_pattern = _get_scale_factor_vectors(
+        circuit_copy, degree, fold_multiplier, num_chunks
+    )
+
+    chunks = _get_chunks(circuit_copy, num_chunks)
+
+    multiple_folded_circuits = []
+    for scale_factor_vector in scaling_pattern:
+        folded_circuit = Circuit()
+        for chunk, scale_factor in zip(chunks, scale_factor_vector):
+            if scale_factor == 1:
+                folded_circuit += chunk
+            else:
+                chunks_circ = Circuit(chunk)
+                folded_chunk_circ = folding_method(chunks_circ, scale_factor)
+                folded_circuit += folded_chunk_circ
+        _append_measurements(folded_circuit, terminal_measurements)
+        multiple_folded_circuits.append((folded_circuit))
+
+    return multiple_folded_circuits