QuantumQuill

Third-party integrations

Use the ArcTN Python interface to optimize contraction paths and pass paths or trees to existing tensor-network tools.

On this page

Using an opt_einsum PathOptimizer

Reuse the three arrays from the quick start. The expression "ab,bc,cd->ad" connects the three inputs by their indices and retains a and d in the result. Commas separate inputs, and the right-hand side of -> specifies output axes:

python
import opt_einsum as oe
from arctn import ArcTNOptimizer

equation = "ab,bc,cd->ad"
optimizer = ArcTNOptimizer(
    preset="heavy", seed=0, max_time=30,
    flops_weight=1, read_write_weight=64,
)
result = oe.contract(equation, *arrays, optimize=optimizer)

opt_einsum calls ArcTNOptimizer.__call__ directly. The PathOptimizer interface returns only a path, not slicing indices. A non-None memory_limit, or a target_size configured in ArcTNOptimizer, makes this call raise NotImplementedError. For slicing, use ArcTNOptimizer.search() through Cotengra/Quimb to obtain a tree, or call arctn_tree / arctn_contract directly.

Returning a Cotengra ContractionTree

Reuse inputs, output, size_dict, and arrays from the quick start. Generate a tree and execute it directly:

python
from arctn import arctn_tree

tree, info = arctn_tree(
    inputs, output, size_dict,
    preset="heavy", seed=0,
    target_size=2**24, slicing_mode="fixed",
    return_info=True,
)
result = tree.contract(arrays)

With return_info=True, the result is (tree, info), where info is a dictionary of path metrics and slicing information. If arctn_plan has already produced a plan, convert it directly instead of searching again with arctn_tree:

python
tree = plan.to_tree()
result = tree.contract(arrays)

ArcTNOptimizer.search() and arctn_tree() run path optimization. plan.to_tree() reuses the existing path and slicing indices without searching again. All of these interfaces can produce a Cotengra ContractionTree representing the path and slicing information.

Using Quimb

Quimb obtains a contraction tree through ArcTNOptimizer.search(). Pass the optimizer to optimize to use ArcTN for circuit tasks or tensor-network contractions.

Explicit array-backend selection

With arctn_contract, choose native or an explicit array backend. When executing through Quimb or a Cotengra tree, use the execution interface of the corresponding library.

Download source Markdown ↓