lint

Author: ciaranra

.pre-commit-config.yaml

@@ -44,5 +44,6 @@ repos:
     rev: v0.4.6
     hooks:
     -   id: blackdoc
+        args: [--line-length=120]
         additional_dependencies:
         - black==25.9.0

README.md

@@ -60,14 +60,7 @@ def repetition_code() -> None:
 
 # Run 10 shots with 10% depolarizing noise
 noise = depolarizing_noise().with_uniform_probability(0.1)
-results = (
-    sim(Guppy(repetition_code))
-    .qubits(5)
-    .quantum(state_vector())
-    .noise(noise)
-    .seed(42)
-    .run(10)
-)
+results = sim(Guppy(repetition_code)).qubits(5).quantum(state_vector()).noise(noise).seed(42).run(10)
 print(results["syndrome"])
 # [[1, 1], [0, 1], [0, 0], [1, 1], [0, 0], [0, 1], [1, 1], [0, 0], [0, 1], [0, 1]]
 ```

docs/README.md

@@ -49,14 +49,7 @@ Simulate a distance-3 repetition code with syndrome extraction using [Guppy](htt
 
     # Run 10 shots with 10% depolarizing noise
     noise = depolarizing_noise().with_uniform_probability(0.1)
-    results = (
-        sim(Guppy(repetition_code))
-        .qubits(5)
-        .quantum(state_vector())
-        .noise(noise)
-        .seed(42)
-        .run(10)
-    )
+    results = sim(Guppy(repetition_code)).qubits(5).quantum(state_vector()).noise(noise).seed(42).run(10)
 
     # Extract syndromes from first two measured qubits (s0, s1)
     d = results.to_dict()

docs/development/proposals/slr-ast.md

@@ -776,9 +776,7 @@ class ASTStateValidator(BaseVisitor[None]):
     def visit_gate(self, node: GateOp) -> None:
         # Validate all targets are prepared
         for target in node.targets:
-            state = self.slot_states.get(
-                (target.allocator, target.index), SlotState.UNPREPARED
-            )
+            state = self.slot_states.get((target.allocator, target.index), SlotState.UNPREPARED)
             if state == SlotState.UNPREPARED:
                 self.violations.append(StateViolation(...))
 

docs/user-guide/getting-started.md

@@ -103,14 +103,7 @@ A repetition code encodes a single logical qubit across multiple physical qubits
 
     # Run 10 shots with 10% depolarizing noise
     noise = depolarizing_noise().with_uniform_probability(0.1)
-    results = (
-        sim(Guppy(repetition_code))
-        .qubits(5)
-        .quantum(state_vector())
-        .noise(noise)
-        .seed(42)
-        .run(10)
-    )
+    results = sim(Guppy(repetition_code)).qubits(5).quantum(state_vector()).noise(noise).seed(42).run(10)
 
     # Extract syndromes from first two measured qubits (s0, s1)
     d = results.to_dict()

docs/user-guide/graph-api.md

@@ -206,9 +206,7 @@ The graph itself can store metadata as attributes.
     graph.attrs().insert("version", "1.0").insert("author", "Alice")
 
     # Style 3: Batch update
-    graph.attrs().update(
-        {"date": "2025-01-26", "tags": ["qec", "surface_code"], "validated": True}
-    )
+    graph.attrs().update({"date": "2025-01-26", "tags": ["qec", "surface_code"], "validated": True})
     ```
 
 === ":fontawesome-brands-rust: Rust"

docs/user-guide/hugr-simulation.md

@@ -56,9 +56,7 @@ Let's create a Bell state using Guppy. First, define a quantum function:
 
 
     # Run simulation
-    results = (
-        sim(Guppy(bell_state)).qubits(2).quantum(state_vector()).seed(42).run(1000)
-    )
+    results = sim(Guppy(bell_state)).qubits(2).quantum(state_vector()).seed(42).run(1000)
 
     print(results.to_dict())
     # Results: always correlated (00 or 11)
@@ -157,12 +155,7 @@ If you have HUGR files (compiled from Guppy or other tools), you can run them di
     from pecos_rslib import state_vector
 
     # From file
-    results = (
-        sim(Hugr.from_file("/tmp/pecos-doc-tests/circuit.hugr"))
-        .qubits(2)
-        .quantum(state_vector())
-        .run(1000)
-    )
+    results = sim(Hugr.from_file("/tmp/pecos-doc-tests/circuit.hugr")).qubits(2).quantum(state_vector()).run(1000)
 
     # Or from bytes
     with open("/tmp/pecos-doc-tests/circuit.hugr", "rb") as f:
@@ -224,9 +217,7 @@ One of HUGR's key advantages is native support for control flow based on measure
 
 
     # Run simulation
-    results = (
-        sim(Guppy(conditional_x)).qubits(2).quantum(state_vector()).seed(42).run(1000)
-    )
+    results = sim(Guppy(conditional_x)).qubits(2).quantum(state_vector()).seed(42).run(1000)
 
     # Results: m0 and m1 are always equal!
     # - If m0=0: no X applied, m1=0
@@ -262,9 +253,7 @@ One of HUGR's key advantages is native support for control flow based on measure
         return m0, m1
 
 
-    results = (
-        sim(Guppy(if_else_circuit)).qubits(2).quantum(state_vector()).seed(42).run(1000)
-    )
+    results = sim(Guppy(if_else_circuit)).qubits(2).quantum(state_vector()).seed(42).run(1000)
     # m0 always 0, m1 is 50/50 (H applied)
     ```
 
@@ -408,9 +397,7 @@ Add realistic noise to your Guppy simulations:
         .with_meas_1_probability(0.03)
     )
 
-    results = (
-        sim(Guppy(noisy_bell)).qubits(2).quantum(state_vector()).noise(noise).run(1000)
-    )
+    results = sim(Guppy(noisy_bell)).qubits(2).quantum(state_vector()).noise(noise).run(1000)
     ```
 
 ## HUGR vs QASM: When to Use Each

docs/user-guide/noise-model-builders.md

@@ -69,9 +69,7 @@ noise = (
 
 # Total probability (used internally by the engine)
 noise = (
-    GeneralNoiseModelBuilder()
-    .with_p1_probability(0.00133)  # Total for single-qubit
-    .with_p2_probability(0.0133)
+    GeneralNoiseModelBuilder().with_p1_probability(0.00133).with_p2_probability(0.0133)  # Total for single-qubit
 )  # Total for two-qubit
 ```
 

docs/user-guide/qasm-simulation.md

@@ -178,12 +178,7 @@ lets you build the experiment once and rerun it multiple times:
     """
 
     # Build once, run multiple times
-    experiment = (
-        sim(Qasm(qasm_code))
-        .seed(42)
-        .noise(depolarizing_noise().with_uniform_probability(0.01))
-        .build()
-    )
+    experiment = sim(Qasm(qasm_code)).seed(42).noise(depolarizing_noise().with_uniform_probability(0.01)).build()
 
     # Run with different shot counts
     results_100 = experiment.run(100)
@@ -461,11 +456,7 @@ This example shows how noise affects quantum entanglement:
 
     # Build simulation with depolarizing noise
     experiment = (
-        sim(Qasm(qasm_code))
-        .seed(42)
-        .workers(4)
-        .noise(depolarizing_noise().with_uniform_probability(0.01))
-        .build()
+        sim(Qasm(qasm_code)).seed(42).workers(4).noise(depolarizing_noise().with_uniform_probability(0.01)).build()
     )
 
     # Run multiple times

docs/user-guide/qec-geometry.md

@@ -59,20 +59,10 @@ Surface codes are the most widely studied topological QEC codes. PECOS supports
     ```python
     from pecos.qec.surface import SurfacePatchBuilder, PatchOrientation
 
-    patch = (
-        SurfacePatchBuilder()
-        .with_distance(5)
-        .with_orientation(PatchOrientation.Z_TOP_BOTTOM)
-        .build()
-    )
+    patch = SurfacePatchBuilder().with_distance(5).with_orientation(PatchOrientation.Z_TOP_BOTTOM).build()
 
     # Non-rotated (standard) layout
-    patch = (
-        SurfacePatchBuilder()
-        .with_distance(5)
-        .standard()  # Use standard layout instead of rotated
-        .build()
-    )
+    patch = SurfacePatchBuilder().with_distance(5).standard().build()  # Use standard layout instead of rotated
     ```
 
 ### Accessing Geometry
@@ -91,9 +81,7 @@ print(f"Total qubits: {patch.num_qubits}")  # 11 (9 data + 2 ancilla)
 
 # Stabilizers
 for stab in patch.x_stabilizers:
-    print(
-        f"X stab {stab.index}: qubits={stab.data_qubits}, boundary={stab.is_boundary}"
-    )
+    print(f"X stab {stab.index}: qubits={stab.data_qubits}, boundary={stab.is_boundary}")
 
 for stab in patch.z_stabilizers:
     print(f"Z stab {stab.index}: qubits={stab.data_qubits}, weight={stab.weight}")
@@ -184,9 +172,7 @@ print(f"Stabilizers: {code.num_stabilizers}")
 
 # All stabilizers
 for stab in code.stabilizers:
-    print(
-        f"Stab {stab.index}: color={stab.color}, qubits={stab.qubits}, weight={stab.weight}"
-    )
+    print(f"Stab {stab.index}: color={stab.color}, qubits={stab.qubits}, weight={stab.weight}")
 
 # Filter by color
 red_stabs = code.get_red_stabilizers()