added mm_einsum

mrmustard/physics/mm_einsum.py

@@ -0,0 +1,211 @@
+# Copyright 2024 Xanadu Quantum Technologies Inc.
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from numba import njit
+import numpy as np
+import itertools
+
+
+@njit
+def _CV_flops(nA: int, nB: int, m: int) -> int:
+    """Calculate the cost of contracting two tensors with CV indices.
+    Args:
+        nA: Number of CV indices in the first tensor
+        nB: Number of CV indices in the second tensor
+        m: Number of CV indices involved in the contraction
+    """
+    cost = (
+        m * m * m  # M inverse
+        + (m + 1) * m * nA  # left matmul
+        + (m + 1) * m * nB  # right matmul
+        + (m + 1) * m  # addition
+        + m * m * m
+    )  # determinant of M
+    return cost
+
+
+def new_indices_and_flops(
+    idx1: frozenset[int], idx2: frozenset[int], fock_size_dict: dict[int, int]
+) -> tuple[frozenset[int], int]:
+    """Calculate the cost of contracting two tensors with mixed CV and Fock indices.
+
+    This function computes both the surviving indices and the computational cost (in FLOPS)
+    of contracting two tensors that contain a mixture of continuous-variable (CV) and
+    Fock-space indices.
+
+    Args:
+        idx1: Set of indices for the first tensor. CV indices are integers not present
+            in fock_size_dict.
+        idx2: Set of indices for the second tensor. CV indices are integers not present
+            in fock_size_dict.
+        fock_size_dict: Dict mapping Fock index labels to their dimensions. Any index
+            not in this dict is treated as a CV index.
+
+    Returns:
+        tuple[frozenset[int], int]: A tuple containing:
+            - frozenset of indices that survive the contraction
+            - total computational cost in FLOPS (including CV operations,
+              Fock contractions, and potential decompositions)
+
+    Example:
+        >>> idx1 = frozenset({0, 1})  # 0 is CV, 1 is Fock
+        >>> idx2 = frozenset({1, 2})  # 2 is Fock
+        >>> fock_size_dict = {1: 2, 2: 3}
+        >>> new_indices_and_flops(idx1, idx2, fock_size_dict)
+        (frozenset({0, 2}), 9)  # Example values
+    """
+
+    # Calculate index sets for contraction
+    contracted_indices = idx1 & idx2  # Indices that get contracted away
+    remaining_indices = idx1 ^ idx2  # Indices that remain after contraction
+    all_fock_indices = set(fock_size_dict.keys())
+
+    # Count CV and get Fock shapes
+    num_cv_contracted = len(contracted_indices - all_fock_indices)
+    fock_contracted_shape = [fock_size_dict[idx] for idx in contracted_indices & all_fock_indices]
+    fock_remaining_shape = [fock_size_dict[idx] for idx in remaining_indices & all_fock_indices]
+
+    # Calculate flops
+    cv_flops = _CV_flops(
+        nA=len(idx1) - num_cv_contracted, nB=len(idx2) - num_cv_contracted, m=num_cv_contracted
+    )
+
+    if len(fock_contracted_shape) > 0:
+        fock_flops = np.prod(fock_contracted_shape) * np.prod(fock_remaining_shape)
+    else:
+        fock_flops = 0
+
+    # Try decomposing the remaining indices
+    new_indices, decomp_flops = attempt_decomposition(remaining_indices, fock_size_dict)
+
+    # pretending that we call the ansatz with the remaining indices
+    call_flops = np.prod([fock_size_dict[idx] for idx in new_indices if idx in fock_size_dict])
+
+    total_flops = int(cv_flops + fock_flops + decomp_flops + call_flops)
+    return new_indices, total_flops
+
+
+def attempt_decomposition(
+    indices: set[int], fock_size_dict: dict[int, int]
+) -> tuple[set[int], int]:
+    """Attempt to reduce the number of indices by combining Fock indices when possible.
+    Only possible if there is only one CV index and multiple Fock indices.
+
+    Args:
+        indices: Set of indices to potentially decompose
+        fock_size_dict: Dictionary mapping indices to their sizes
+
+    Returns:
+        Tuple of (decomposed indices, cost of decomposition)
+    """
+    fock_indices_shape = [fock_size_dict[idx] for idx in indices if idx in fock_size_dict]
+    cv_indices = [idx for idx in indices if idx not in fock_size_dict]
+
+    if len(cv_indices) == 1 and len(fock_indices_shape) > 1:
+        new_index = max(fock_size_dict) + 1  # Create new index with size = sum of Fock index sizes
+        decomposed_indices = {cv_indices[0], new_index}
+        fock_size_dict[new_index] = sum(fock_indices_shape)
+        decomp_flops = np.prod(fock_indices_shape)
+        return frozenset(decomposed_indices), decomp_flops
+    return frozenset(indices), 0
+
+
+def optimal(
+    inputs: list[frozenset[int]],
+    fock_size_dict: dict[int, int],
+    info: bool = False,
+) -> list[tuple[int, int]]:
+    """Find the optimal contraction path for a mixed CV-Fock tensor network.
+
+    This function performs an exhaustive search over all possible contraction orders
+    for a tensor network containing both continuous-variable (CV) and Fock-space tensors.
+    It uses a depth-first recursive strategy to find the sequence of pairwise contractions
+    that minimizes the total computational cost (FLOPS).
+
+    CV indices are represented by integers not present in fock_size_dict, while Fock
+    indices must be keys in fock_size_dict. The algorithm caches intermediate results,
+    skips outer products (contractions between tensors with no shared indices), and
+    prunes the search when partial paths exceed the current best cost.
+
+    Args:
+        inputs: List of index sets representing tensor indices
+        fock_size_dict: Mapping from Fock index labels to dimensions
+        info: If True, prints cache size diagnostics
+
+    Returns:
+        tuple[tuple[int, int], ...]: The optimal contraction path as a sequence of pairs.
+            Each pair (i, j) indicates that tensors at positions i and j should be
+            contracted together. The resulting tensor is placed at position len(inputs).
+
+    Example:
+        >>> inputs = [frozenset({0, 1}), frozenset({1, 2}), frozenset({2, 3})]
+        >>> fock_size_dict = {1: 2, 2: 2}  # indices 0 and 3 are CV indices
+        >>> optimal(inputs, fock_size_dict)
+        ((0, 1), (2, 3))
+
+    Reference:
+        Based on the optimal path finder in opt_einsum:
+        https://github.com/dgasmith/opt_einsum/blob/master/opt_einsum/paths.py
+    """
+    best_flops: int = float("inf")
+    best_path: tuple[tuple[int, int], ...] = ()
+    result_cache: dict[tuple[frozenset[int], frozenset[int]], tuple[frozenset[int], int]] = {}
+
+    def _optimal_iterate(path, remaining, inputs, flops):
+        nonlocal best_flops
+        nonlocal best_path
+
+        if len(remaining) == 1:
+            best_flops = flops
+            best_path = path
+            return
+
+        # check all remaining paths
+        for i, j in itertools.combinations(remaining, 2):
+            if i > j:
+                i, j = j, i
+
+            # skip outer products
+            if not inputs[i] & inputs[j]:
+                continue
+
+            key = (inputs[i], inputs[j])
+            try:
+                new_indices, flops_ij = result_cache[key]
+            except KeyError:
+                new_indices, flops_ij = result_cache[key] = new_indices_and_flops(
+                    *key, fock_size_dict
+                )
+
+            # sieve based on current best flops
+            new_flops = flops + flops_ij
+            if new_flops >= best_flops:
+                continue
+
+            # add contraction and recurse into all remaining
+            _optimal_iterate(
+                path=path + ((i, j),),
+                inputs=inputs + (new_indices,),
+                remaining=remaining - {i, j} | {len(inputs)},
+                flops=new_flops,
+            )
+
+    _optimal_iterate(
+        path=(), inputs=tuple(map(frozenset, inputs)), remaining=set(range(len(inputs))), flops=0
+    )
+
+    if info:
+        print("len(fock_size_dict)", len(fock_size_dict), "len(result_cache)", len(result_cache))
+    return best_path