add more methods for stabilizercircuit

Author: refraction-ray

docs/source/modules.rst

@@ -24,6 +24,7 @@ tensorcircuit
     ./api/results.rst
     ./api/shadows.rst
     ./api/simplify.rst
+    ./api/stabilizercircuit.rst
     ./api/templates.rst
     ./api/torchnn.rst
     ./api/translation.rst

tensorcircuit/stabilizercircuit.py

@@ -31,14 +31,18 @@ class StabilizerCircuit(AbstractCircuit):
         "sd": "S_DAG",
     }
 
-    def __init__(self, nqubits: int, inputs: Tensor = None) -> None:
+    def __init__(
+        self, nqubits: int, inputs: Tensor = None, tableau_inputs: Tensor = None
+    ) -> None:
         """
         ``StabilizerCircuit`` class based on stim package
 
         :param nqubits: Number of qubits
         :type nqubits: int
         :param inputs: initial state by stabilizers, defaults to None
         :type inputs: Tensor, optional
+        :param tableau_inputs: initial state by **inverse** tableau, defaults to None
+        :type tableau_inputs: Tensor, optional
         """
         self._nqubits = nqubits
         self._stim_circuit = stim.Circuit()
@@ -49,6 +53,8 @@ def __init__(self, nqubits: int, inputs: Tensor = None) -> None:
         self.current_sim = stim.TableauSimulator()
         if inputs:
             self.current_sim.set_state_from_stabilizers(inputs)
+        if tableau_inputs:
+            self.current_sim.set_inverse_tableau(tableau_inputs)
 
     def apply_general_gate(
         self,
@@ -123,6 +129,22 @@ def random_gate(self, *index: int, recorded: bool = False) -> None:
         if recorded:
             self._stim_circuit += t.to_circuit()
 
+    def tableau_gate(self, *index: int, tableau: Any, recorded: bool = False) -> None:
+        """
+        Apply a gate indicated by tableau to the circuit.
+        This operation will not record in qir
+
+        :param index: Qubit indices to apply the gate to
+        :type index: int
+        :param tableau: stim.Tableau representation of the gate
+        :type tableau: Any
+        :param recorded: Whether the gate is recorded in ``stim.Circuit``, defaults to False
+        :type recorded: bool, optional
+        """
+        self.current_sim.do_tableau(tableau, index)
+        if recorded:
+            self._stim_circuit += tableau.to_circuit()
+
     def measure(self, *index: int, with_prob: bool = False) -> Tensor:
         """
         Measure qubits in Z basis.
@@ -167,6 +189,27 @@ def cond_measurement(self, index: int) -> Tensor:
 
     cond_measure = cond_measurement
 
+    def cond_measure_many(self, *index: int) -> Tensor:
+        """
+        Measure qubits in Z basis with state collapse.
+
+        :param index: Index of qubit to measure
+        :type index: int
+        :return: Measurement results and probability (if with_prob=True)
+        :rtype: Union[np.ndarray, Tuple[np.ndarray, float]]
+        """
+        # Convert negative indices
+
+        # Add measurement instructions
+        self._stim_circuit.append_from_stim_program_text(
+            "M " + " ".join(map(str, index))
+        )
+        # self.current_sim = None
+        m = self.current_simulator().measure_many(*index)
+        # Sample once from the circuit using sampler
+
+        return m
+
     def sample(
         self,
         batch: Optional[int] = None,
@@ -346,6 +389,12 @@ def current_tableau(self) -> stim.Tableau:
         """
         return self.current_simulator().current_inverse_tableau() ** -1
 
+    def current_inverse_tableau(self) -> stim.Tableau:
+        """
+        Return the current inverse tableau of the circuit.
+        """
+        return self.current_simulator().current_inverse_tableau()
+
     def entanglement_entropy(self, cut: Sequence[int]) -> float:
         """
         Calculate the entanglement entropy for a subset of qubits using stabilizer formalism.

tests/test_stabilizer.py

@@ -185,3 +185,33 @@ def test_depolarize():
         c.h(0)
         r.append(c.expectation_ps(z=[0]))
     assert 10 < np.sum(r) < 20
+
+
+def test_tableau_inputs():
+    c = tc.StabilizerCircuit(2)
+    c.x(1)
+    c.s(1)
+    it = c.current_inverse_tableau()
+    c1 = tc.StabilizerCircuit(2, tableau_inputs=it)
+    c1.s(1)
+    c1.x(1)
+    np.testing.assert_allclose(c1.state()[0], 1, atol=1e-6)
+
+
+def test_mipt():
+    resource = [stim.Tableau.random(2) for _ in range(1000)]
+
+    def ruc(n, nlayer, p):
+        c = tc.StabilizerCircuit(n)
+        status = np.random.choice(1000, size=[n, nlayer], replace=True)
+        for j in range(nlayer):
+            for i in range(0, n, 2):
+                c.tableau_gate(i, (i + 1) % n, tableau=resource[status[i, j]])
+            for i in range(1, n, 2):
+                c.tableau_gate(i, (i + 1) % n, tableau=resource[status[i, j]])
+            mask = np.random.random(n) < p
+            ids = list(np.where(mask)[0])
+            c.cond_measure_many(*ids)
+        return c.entanglement_entropy(list(range(n // 2)))
+
+    print(ruc(50, 10, 0.1))