Graph-based ISA pruning for resource estimation

[msoeken]

Adds an alternative estimation path that builds a provenance graph of ISA instructions and prunes suboptimal candidates before forming the Cartesian product, significantly reducing the combinatorial search space.

Changes

Rust (source/qre/src/)

• isa.rs: Add build_pareto_index() to compute per-instruction-ID Pareto-optimal node sets over (space, time, error). Add query_satisfying() to enumerate ISAs from pruned graph nodes. Extract InstructionConstraint::is_satisfied_by() from inline logic.
• trace.rs: Add estimate_with_graph() — a new parallel estimator that uses the provenance graph with per-slot dominance pruning to skip combinations dominated by previously successful estimates. Add Trace::required_instruction_ids() helper. Add post_process flag to estimate_parallel() to control summary collection.
• result.rs / lib.rs: Expose new types and re-exports.

Python (source/pip/)

• _estimation.py: Add use_graph parameter to estimate() (default True). When enabled, populates the provenance graph and calls the graph-based estimator instead of the flat enumerator.
• _isa_enumeration.py: Add populate() method to ISAQuery and its subclasses to fill the provenance graph without yielding ISA objects.
• _instruction.py: Add InstructionSource utility.
• qre.rs: Expose _estimate_with_graph, ProvenanceGraph bindings, and related Python-facing APIs.
• _qre.pyi: Update type stubs.

Tests

• Add tests verifying graph-based estimation produces Pareto-optimal results consistent with the exhaustive path.

Trade-offs

The graph-based pruning filters ISA instructions by comparing per-instruction space, time, and error independently. Because total qubit counts depend on the interaction between factory space and runtime (copies × factory_space), an instruction dominated on per-instruction metrics can still contribute to a globally Pareto-optimal result. use_graph=False
can be used when completeness of the Pareto frontier is required.