update the circuit initialization logic for analogcircuit

Author: refraction-ray

README.md

@@ -27,9 +27,9 @@
 
 TensorCircuit-NG is the next-generation open-source high-performance quantum software framework, built upon tensornetwork engines, supporting for automatic differentiation, just-in-time compiling, hardware acceleration, vectorized parallelism and distributed training, providing unified infrastructures and interfaces for quantum programming. It can compose quantum circuits, neural networks and tensor networks seamlessly with high simulation efficiency and flexibility.
 
-TensorCircuit-NG is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for large-scale simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal, noisy, Clifford, qudit, approximate, analog, and fermionic cases. It also supports quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions.
+TensorCircuit-NG is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for large-scale simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal (`Circuit`), noisy (`DMCircuit`), Clifford (`StabilizerCircuit`), qudit (`QuditCircuit`), approximate (`MPSCircuit`), analog (`AnalogCircuit`), and fermionic (`FGSCircuit`) cases. It also supports quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions.
 
-TensorCircuit-NG is the actively maintained official version and a [fully compatible](https://tensorcircuit-ng.readthedocs.io/en/latest/faq.html#what-is-the-relation-between-tensorcircuit-and-tensorcircuit-ng) successor to TensorCircuit with more new features (stabilizer circuit, multi-card distributed simulation, etc.) and bug fixes (support latest `numpy>2` and `qiskit>1`).
+TensorCircuit-NG is the only actively maintained official version and a [fully compatible](https://tensorcircuit-ng.readthedocs.io/en/latest/faq.html#what-is-the-relation-between-tensorcircuit-and-tensorcircuit-ng) successor to TensorCircuit with more new features (stabilizer circuit, qudit circuit, analog circuit, multi-GPU distributed simulation, etc.) and bug fixes (support latest `numpy>2` and `qiskit>1`).
 
 ## Getting Started
 

tensorcircuit/analogcircuit.py

@@ -1,5 +1,6 @@
 """
 Analog-Digital Hybrid Circuit class wrapper
+only support jax backend
 """
 
 from typing import Any, List, Optional, Callable, Dict, Tuple, Union, Sequence
@@ -21,7 +22,9 @@
 
 @dataclass
 class AnalogBlock:
-    """A data structure to hold information about an analog evolution block."""
+    """
+    A data structure to hold information about an analog evolution block.
+    """
 
     hamiltonian_func: Callable[[Tensor], Tensor]
     time: float
@@ -45,7 +48,18 @@ def __init__(
         """
         Initializes the hybrid circuit.
 
-        :param num_qubits: The number of qubits in the circuit.
+        :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.
+        :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.num_qubits, self._nqubits = nqubits, nqubits
         self.dim = 2**self.num_qubits
@@ -57,7 +71,7 @@ def __init__(
 
         # List of digital circuits, starting with one empty circuit.
         self.digital_circuits: List[Circuit] = [
-            Circuit(self.num_qubits, self.inputs, mps_inputs, split, dim)
+            Circuit(self.num_qubits, inputs, mps_inputs, split, dim)
         ]
 
         # List of analog blocks, each containing the Hamiltonian function, time, and solver options.
@@ -66,18 +80,25 @@ def __init__(
         self._solver_options: Dict[str, Any] = {}
 
     def set_solver_options(self, **kws: Any) -> None:
+        """
+        set solver options globally for this circuit object
+        """
         self._solver_options = kws
 
     @property
     def effective_circuit(self) -> Circuit:
-        """Returns the effective circuit after all blocks have been added."""
+        """
+        Returns the effective circuit after all blocks have been added.
+        """
         if self._effective_circuit is None:
             self.state()
         return self._effective_circuit  # type: ignore
 
     @property
     def current_digital_circuit(self) -> Circuit:
-        """Returns the last (currently active) digital circuit."""
+        """
+        Returns the last (currently active) digital circuit.
+        """
         return self.digital_circuits[-1]
 
     def add_analog_block(
@@ -94,11 +115,14 @@ def add_analog_block(
 
         :param hamiltonian_func: A function H(t) that takes a time `t` (from 0 to `time`)
                                  and returns the Hamiltonian matrix at that instant.
+        :type hamiltonian_func: Callable[[float], np.ndarray]
         :param time: The total evolution time 'T'.
+        :type time: float
         :param index: The indices of the qubits to apply the analog evolution to. Defaults None for
             global application.
-        :param solver_options: Keyword arguments passed directly to `scipy.integrate.solve_ivp`.
-                               (e.g., method='RK45', rtol=1e-6, atol=1e-8)
+        :type index: Optional[List[int]]
+        :param solver_options: Keyword arguments passed directly to `tc.timeevol.ode_evolve`
+        :type solver_options: Dict[str, Any]
         """
         # Create and store the analog block information
         time = backend.convert_to_tensor(time, dtype=rdtypestr)
@@ -152,14 +176,12 @@ def state(self) -> Tensor:
         :return: The final state vector after the full evolution
         :rtype: Tensor
         """
-        psi = self.inputs
-
         # Propagate the state through the alternating circuit blocks
         for i, analog_block in enumerate(self.analog_blocks):
             # 1. Apply Digital Block i
             digital_c = self.digital_circuits[i]
             if i > 0:
-                digital_c.replace_inputs(psi)
+                digital_c.replace_inputs(psi)  # type: ignore
             psi = digital_c.wavefunction()
 
             if analog_block.index is None:
@@ -181,8 +203,11 @@ def state(self) -> Tensor:
             # TODO(@refraction-ray): support more time evol methods
 
         # 3. Apply the final digital circuit
-        self.digital_circuits[-1].replace_inputs(psi)
-        psi = self.digital_circuits[-1].wavefunction()
+        if self.analog_blocks:
+            self.digital_circuits[-1].replace_inputs(psi)
+            psi = self.digital_circuits[-1].wavefunction()
+        else:
+            psi = self.digital_circuits[-1].wavefunction()
         self._effective_circuit = Circuit(self.num_qubits, inputs=psi)
 
         return psi
@@ -382,7 +407,7 @@ def __repr__(self) -> str:
             if i < len(self.analog_blocks):
                 block = self.analog_blocks[i]
                 s += f"--- Analog Block {i} (T={block.time}) ---\n"
-                s += f"  H(t) function: '{block.hamiltonian_func.__name__}'\n"
+                s += f" H(t) function: '{block.hamiltonian_func.__name__}'\n"
 
         s += "=" * 40
         return s