Qudit infras

tensorcircuit/abstractcircuit.py

@@ -68,6 +68,7 @@
 
 class AbstractCircuit:
     _nqubits: int
+    _d: int = 2
     _qir: List[Dict[str, Any]]
     _extra_qir: List[Dict[str, Any]]
     inputs: Tensor

tensorcircuit/circuit.py

@@ -1,5 +1,7 @@
 """
-Quantum circuit: the state simulator
+Quantum circuit: the state simulator.
+Supports qubit (dim=2) and qudit (3 <= dim <= 36) systems.
+For string-encoded samples/counts, digits use 0–9A–Z where A=10, …, Z=35.
 """
 
 # pylint: disable=invalid-name
@@ -13,8 +15,8 @@
 
 from . import gates
 from . import channels
-from .cons import backend, contractor, dtypestr, npdtype
-from .quantum import QuOperator, identity
+from .cons import backend, contractor, dtypestr, npdtype, _ALPHABET
+from .quantum import QuOperator, identity, _infer_num_sites
 from .simplify import _full_light_cone_cancel
 from .basecircuit import BaseCircuit
 
@@ -23,7 +25,7 @@
 
 
 class Circuit(BaseCircuit):
-    """
+    r"""
     ``Circuit`` class.
     Simple usage demo below.
 
@@ -45,21 +47,26 @@ def __init__(
         inputs: Optional[Tensor] = None,
         mps_inputs: Optional[QuOperator] = None,
         split: Optional[Dict[str, Any]] = None,
+        dim: Optional[int] = None,
     ) -> None:
-        """
+        r"""
         Circuit object based on state simulator.
+        Do not use this class with d!=2 directly, use tc.QuditCircuit instead for qudit systems.
 
         :param nqubits: The number of qubits in the circuit.
         :type nqubits: int
+        :param dim: The local Hilbert space dimension per site. Qudit is supported for 2 <= d <= 36.
+        :type dim: If None, the dimension of the circuit will be `2`, which is a qubit system.
         :param inputs: If not None, the initial state of the circuit is taken as ``inputs``
-            instead of :math:`\\vert 0\\rangle^n` qubits, defaults to None.
+            instead of :math:`\vert 0 \rangle^n` qubits, defaults to None.
         :type inputs: Optional[Tensor], optional
         :param mps_inputs: QuVector for a MPS like initial wavefunction.
         :type mps_inputs: Optional[QuOperator]
         :param split: dict if two qubit gate is ready for split, including parameters for at least one of
             ``max_singular_values`` and ``max_truncation_err``.
         :type split: Optional[Dict[str, Any]]
         """
+        self._d = 2 if dim is None else dim
         self.inputs = inputs
         self.mps_inputs = mps_inputs
         self.split = split
@@ -70,18 +77,19 @@ def __init__(
             "inputs": inputs,
             "mps_inputs": mps_inputs,
             "split": split,
+            "dim": dim,
         }
         if (inputs is None) and (mps_inputs is None):
-            nodes = self.all_zero_nodes(nqubits)
+            nodes = self.all_zero_nodes(nqubits, dim=self._d)
             self._front = [n.get_edge(0) for n in nodes]
         elif inputs is not None:  # provide input function
             inputs = backend.convert_to_tensor(inputs)
             inputs = backend.cast(inputs, dtype=dtypestr)
             inputs = backend.reshape(inputs, [-1])
             N = inputs.shape[0]
-            n = int(np.log(N) / np.log(2))
+            n = _infer_num_sites(N, dim=self._d)
             assert n == nqubits or n == 2 * nqubits
-            inputs = backend.reshape(inputs, [2 for _ in range(n)])
+            inputs = backend.reshape(inputs, [self._d for _ in range(n)])
             inputs = Gate(inputs)
             nodes = [inputs]
             self._front = [inputs.get_edge(i) for i in range(n)]
@@ -178,27 +186,14 @@ def mid_measurement(self, index: int, keep: int = 0) -> Tensor:
 
         :param index: The index of qubit that the Z direction postselection applied on.
         :type index: int
-        :param keep: 0 for spin up, 1 for spin down, defaults to be 0.
+        :param keep: the post-selected digit in {0, ..., d-1}, defaults to be 0.
         :type keep: int, optional
         """
         # normalization not guaranteed
-        # assert keep in [0, 1]
-        if keep < 0.5:
-            gate = np.array(
-                [
-                    [1.0],
-                    [0.0],
-                ],
-                dtype=npdtype,
-            )
-        else:
-            gate = np.array(
-                [
-                    [0.0],
-                    [1.0],
-                ],
-                dtype=npdtype,
-            )
+        gate = np.array(
+            [[0.0] if _idx != keep else [1.0] for _idx in range(self._d)],
+            dtype=npdtype,
+        )
 
         mg1 = tn.Node(gate)
         mg2 = tn.Node(gate)
@@ -479,7 +474,7 @@ def step_function(x: Tensor) -> Tensor:
         if get_gate_from_index is None:
             raise ValueError("no `get_gate_from_index` implementation is provided")
         g = get_gate_from_index(r, kraus)
-        g = backend.reshape(g, [2 for _ in range(sites * 2)])
+        g = backend.reshape(g, [self._d for _ in range(sites * 2)])
         self.any(*index, unitary=g, name=name)  # type: ignore
         return r
 
@@ -680,7 +675,7 @@ def _meta_apply_channels(cls) -> None:
             Apply %s quantum channel on the circuit.
             See :py:meth:`tensorcircuit.channels.%schannel`
 
-            :param index: Qubit number that the gate applies on.
+            :param index: Site index that the gate applies on.
             :type index: int.
             :param status: uniform external random number between 0 and 1
             :type status: Tensor
@@ -737,8 +732,8 @@ def get_quoperator(self) -> QuOperator:
         :return: ``QuOperator`` object for the circuit unitary (open indices for the input state)
         :rtype: QuOperator
         """
-        mps = identity([2 for _ in range(self._nqubits)])
-        c = Circuit(self._nqubits)
+        mps = identity([self._d for _ in range(self._nqubits)])
+        c = Circuit(self._nqubits, dim=self._d)
         ns, es = self._copy()
         c._nodes = ns
         c._front = es
@@ -758,8 +753,8 @@ def matrix(self) -> Tensor:
         :return: The circuit unitary matrix
         :rtype: Tensor
         """
-        mps = identity([2 for _ in range(self._nqubits)])
-        c = Circuit(self._nqubits)
+        mps = identity([self._d for _ in range(self._nqubits)])
+        c = Circuit(self._nqubits, dim=self._d)
         ns, es = self._copy()
         c._nodes = ns
         c._front = es
@@ -772,6 +767,9 @@ def measure_reference(
         """
         Take measurement on the given quantum lines by ``index``.
 
+        Return format:
+        - For d <= 36, the sample is a base-d string using 0–9A–Z (A=10,…).
+
         :Example:
 
         >>> c = tc.Circuit(3)
@@ -800,10 +798,9 @@ def measure_reference(
                 if i != j:
                     e ^ edge2[i]
             for i in range(len(sample)):
-                if sample[i] == "0":
-                    m = np.array([1, 0], dtype=npdtype)
-                else:
-                    m = np.array([0, 1], dtype=npdtype)
+                m = np.array([0 for _ in range(self._d)], dtype=npdtype)
+                m[int(sample[i])] = 1
+
                 nodes1.append(tn.Node(m))
                 nodes1[-1].get_edge(0) ^ edge1[index[i]]
                 nodes2.append(tn.Node(m))
@@ -814,15 +811,13 @@ def measure_reference(
                 / p
                 * contractor(nodes1, output_edge_order=[edge1[j], edge2[j]]).tensor
             )
-            pu = rho[0, 0]
-            r = backend.random_uniform([])
-            r = backend.real(backend.cast(r, dtypestr))
-            if r < backend.real(pu):
-                sample += "0"
-                p = p * pu
-            else:
-                sample += "1"
-                p = p * (1 - pu)
+            probs = backend.real(backend.diagonal(rho))
+            probs /= backend.sum(probs)
+            outcome = backend.implicit_randc(self._d, shape=1, p=probs)
+
+            sample += _ALPHABET[outcome]
+            p *= float(probs[outcome])
+
         if with_prob:
             return sample, p
         else:
@@ -842,6 +837,10 @@ def expectation(
     ) -> Tensor:
         """
         Compute the expectation of corresponding operators.
+        For qudit (d > 2),
+        ensure that operator tensor shapes are consistent with d (each site contributes two axes of size d).
+
+        Noise shorthand (via noise_conf) is qubit-only; for d>2, use explicit operators.
 
         :Example:
 
@@ -883,8 +882,6 @@ def expectation(
         :return: Tensor with one element
         :rtype: Tensor
         """
-        from .noisemodel import expectation_noisfy
-
         if noise_conf is None:
             # if not reuse:
             #     nodes1, edge1 = self._copy()
@@ -899,6 +896,8 @@ def expectation(
                 nodes1 = _full_light_cone_cancel(nodes1)
             return contractor(nodes1).tensor
         else:
+            from .noisemodel import expectation_noisfy
+
             return expectation_noisfy(
                 self,
                 *ops,
@@ -919,9 +918,11 @@ def expectation(
     bra: Optional[Tensor] = None,
     conj: bool = True,
     normalization: bool = False,
+    dim: Optional[int] = None,
 ) -> Tensor:
     """
     Compute :math:`\\langle bra\\vert ops \\vert ket\\rangle`.
+    For qudit systems (d>2), ops must be reshaped with per-site axes of length d.
 
     Example 1 (:math:`bra` is same as :math:`ket`)
 
@@ -966,6 +967,8 @@ def expectation(
     :type ket: Tensor
     :param bra: :math:`bra`, defaults to None, which is the same as ``ket``.
     :type bra: Optional[Tensor], optional
+    :param dim: dimension of the circuit (defaults to 2)
+    :type dim: int, optional
     :param conj: :math:`bra` changes to the adjoint matrix of :math:`bra`, defaults to True.
     :type conj: bool, optional
     :param normalization: Normalize the :math:`ket` and :math:`bra`, defaults to False.
@@ -974,6 +977,7 @@ def expectation(
     :return: The result of :math:`\\langle bra\\vert ops \\vert ket\\rangle`.
     :rtype: Tensor
     """
+    dim = 2 if dim is None else dim
     if bra is None:
         bra = ket
     if isinstance(ket, QuOperator):
@@ -987,7 +991,7 @@ def expectation(
         for op, index in ops:
             if not isinstance(op, tn.Node):
                 # op is only a matrix
-                op = backend.reshape2(op)
+                op = backend.reshaped(op, dim)
                 op = gates.Gate(op)
             if isinstance(index, int):
                 index = [index]
@@ -1011,8 +1015,8 @@ def expectation(
         if conj is True:
             bra = backend.conj(bra)
         ket = backend.reshape(ket, [-1])
-        ket = backend.reshape2(ket)
-        bra = backend.reshape2(bra)
+        ket = backend.reshaped(ket, dim)
+        bra = backend.reshaped(bra, dim)
         n = len(backend.shape_tuple(ket))
         ket = Gate(ket)
         bra = Gate(bra)
@@ -1024,7 +1028,7 @@ def expectation(
         for op, index in ops:
             if not isinstance(op, tn.Node):
                 # op is only a matrix
-                op = backend.reshape2(op)
+                op = backend.reshaped(op, dim)
                 op = gates.Gate(op)
             if isinstance(index, int):
                 index = [index]

tensorcircuit/cons.py

@@ -63,6 +63,7 @@ def sorted_edges(edges: Iterator[tn.Edge]) -> List[tn.Edge]:
 npdtype = np.complex64
 backend: NumpyBackend = get_backend("numpy")
 contractor = tn.contractors.auto
+_ALPHABET = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
 # these above lines are just for mypy, it is not very good at evaluating runtime object
 
 

tensorcircuit/densitymatrix.py

@@ -17,7 +17,7 @@
 from .circuit import Circuit
 from .cons import backend, contractor, dtypestr
 from .basecircuit import BaseCircuit
-from .quantum import QuOperator
+from .quantum import QuOperator, _infer_num_sites
 
 Gate = gates.Gate
 Tensor = Any
@@ -35,9 +35,11 @@ def __init__(
         dminputs: Optional[Tensor] = None,
         mpo_dminputs: Optional[QuOperator] = None,
         split: Optional[Dict[str, Any]] = None,
+        dim: Optional[int] = None,
     ) -> None:
         """
         The density matrix simulator based on tensornetwork engine.
+        Do not use this class with d!=2 directly
 
         :param nqubits: Number of qubits
         :type nqubits: int
@@ -55,6 +57,7 @@ def __init__(
             ``max_singular_values`` and ``max_truncation_err``.
         :type split: Optional[Dict[str, Any]]
         """
+        self._d = 2 if dim is None else dim
         if not empty:
             if (
                 (inputs is None)
@@ -73,9 +76,9 @@ def __init__(
                 inputs = backend.cast(inputs, dtype=dtypestr)
                 inputs = backend.reshape(inputs, [-1])
                 N = inputs.shape[0]
-                n = int(np.log(N) / np.log(2))
+                n = _infer_num_sites(N, self._d)
                 assert n == nqubits
-                inputs = backend.reshape(inputs, [2 for _ in range(n)])
+                inputs = backend.reshape(inputs, [self._d for _ in range(n)])
                 inputs_gate = Gate(inputs)
                 self._nodes = [inputs_gate]
                 self.coloring_nodes(self._nodes)
@@ -94,7 +97,9 @@ def __init__(
             elif dminputs is not None:
                 dminputs = backend.convert_to_tensor(dminputs)
                 dminputs = backend.cast(dminputs, dtype=dtypestr)
-                dminputs = backend.reshape(dminputs, [2 for _ in range(2 * nqubits)])
+                dminputs = backend.reshape(
+                    dminputs, [self._d for _ in range(2 * nqubits)]
+                )
                 dminputs_gate = Gate(dminputs)
                 nodes = [dminputs_gate]
                 self._front = [dminputs_gate.get_edge(i) for i in range(2 * nqubits)]
@@ -217,7 +222,7 @@ def apply_general_kraus(
             dd = dmc.densitymatrix()
             circuits.append(dd)
         tensor = reduce(add, circuits)
-        tensor = backend.reshape(tensor, [2 for _ in range(2 * self._nqubits)])
+        tensor = backend.reshaped(tensor, d=self._d)
         self._nodes = [Gate(tensor)]
         dangling = [e for e in self._nodes[0]]
         self._front = dangling
@@ -255,7 +260,7 @@ def densitymatrix(self, check: bool = False, reuse: bool = True) -> Tensor:
             t = contractor(nodes, output_edge_order=d_edges)
         else:
             t = nodes[0]
-        dm = backend.reshape(t.tensor, shape=[2**self._nqubits, 2**self._nqubits])
+        dm = backend.reshapem(t.tensor)
         if check:
             self.check_density_matrix(dm)
         return dm
@@ -274,7 +279,7 @@ def wavefunction(self) -> Tensor:
         dm = self.densitymatrix()
         e, v = backend.eigh(dm)
         np.testing.assert_allclose(
-            e[:-1], backend.zeros([2**self._nqubits - 1]), atol=1e-5
+            e[:-1], backend.zeros([self._d**self._nqubits - 1]), atol=1e-5
         )
         return v[:, -1]
 
@@ -375,7 +380,7 @@ def apply_general_kraus(
         #     index = [index[0] for _ in range(len(kraus))]
         super_op = kraus_to_super_gate(kraus)
         nlegs = 4 * len(index)
-        super_op = backend.reshape(super_op, [2 for _ in range(nlegs)])
+        super_op = backend.reshape(super_op, [self._d for _ in range(nlegs)])
         super_op = Gate(super_op)
         o2i = int(nlegs / 2)
         r2l = int(nlegs / 4)