Merge branch 'Qiskit:main' into ZZFeatureMapIssue

.github/workflows/wheels.yml

@@ -30,7 +30,7 @@ jobs:
         with:
           components: llvm-tools-preview
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.1
+        uses: pypa/cibuildwheel@v2.19.2
         env:
           CIBW_BEFORE_BUILD: 'bash ./tools/build_pgo.sh /tmp/pgo-data/merged.profdata'
           CIBW_BEFORE_BUILD_WINDOWS: 'bash ./tools/build_pgo.sh /tmp/pgo-data/merged.profdata && cp /tmp/pgo-data/merged.profdata ~/.'
@@ -58,7 +58,7 @@ jobs:
         with:
           components: llvm-tools-preview
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.1
+        uses: pypa/cibuildwheel@v2.19.2
         env:
           CIBW_BEFORE_ALL: rustup target add aarch64-apple-darwin
           CIBW_BUILD: cp38-macosx_universal2 cp38-macosx_arm64
@@ -87,7 +87,7 @@ jobs:
         with:
           components: llvm-tools-preview
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.1
+        uses: pypa/cibuildwheel@v2.19.2
         env:
           CIBW_SKIP: 'pp* cp36-* cp37-* *musllinux* *amd64 *x86_64'
       - uses: actions/upload-artifact@v4
@@ -133,7 +133,7 @@ jobs:
         with:
           platforms: all
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.1
+        uses: pypa/cibuildwheel@v2.19.2
         env:
           CIBW_ARCHS_LINUX: s390x
           CIBW_TEST_SKIP: "cp*"
@@ -167,7 +167,7 @@ jobs:
         with:
           platforms: all
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.1
+        uses: pypa/cibuildwheel@v2.19.2
         env:
           CIBW_ARCHS_LINUX: ppc64le
           CIBW_TEST_SKIP: "cp*"
@@ -201,7 +201,7 @@ jobs:
         with:
           platforms: all
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.19.1
+        uses: pypa/cibuildwheel@v2.19.2
         env:
           CIBW_ARCHS_LINUX: aarch64
       - uses: actions/upload-artifact@v4

README.md

@@ -12,8 +12,8 @@
 
 **Qiskit**  is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.
 
-This library is the core component of Qiskit, which contains the building blocks for creating and working with quantum circuits, quantum operators, and primitive functions (sampler and estimator).
-It also contains a transpiler that supports optimizing quantum circuits and a quantum information toolbox for creating advanced quantum operators. 
+This library is the core component of Qiskit, which contains the building blocks for creating and working with quantum circuits, quantum operators, and primitive functions (Sampler and Estimator).
+It also contains a transpiler that supports optimizing quantum circuits, and a quantum information toolbox for creating advanced operators.
 
 For more details on how to use Qiskit, refer to the documentation located here:
 
@@ -91,12 +91,12 @@ print(f" > Expectation values: {result.values}")
 Running this will give the outcome `4`. For fun, try to assign a value of +/- 1 to each single-qubit operator X and Y 
 and see if you can achieve this outcome. (Spoiler alert: this is not possible!)
 
-Using the Qiskit-provided `qiskit.primitives.Sampler` and `qiskit.primitives.Estimator` will not take you very far. The power of quantum computing cannot be simulated 
-on classical computers and you need to use real quantum hardware to scale to larger quantum circuits. However, running a quantum 
-circuit on hardware requires rewriting them to the basis gates and connectivity of the quantum hardware.
-The tool that does this is the [transpiler](https://docs.quantum.ibm.com/api/qiskit/transpiler) 
-and Qiskit includes transpiler passes for synthesis, optimization, mapping, and scheduling. However, it also includes a
-default compiler which works very well in most examples. The following code will map the example circuit to the `basis_gates = ['cz', 'sx', 'rz']` and a linear chain of qubits $0 \rightarrow 1 \rightarrow 2$ with the `coupling_map =[[0, 1], [1, 2]]`.
+Using the Qiskit-provided `qiskit.primitives.Sampler` and `qiskit.primitives.Estimator` will not take you very far.
+The power of quantum computing cannot be simulated on classical computers and you need to use real quantum hardware to scale to larger quantum circuits.
+However, running a quantum circuit on hardware requires rewriting to the basis gates and connectivity of the quantum hardware.
+The tool that does this is the [transpiler](https://docs.quantum.ibm.com/api/qiskit/transpiler), and Qiskit includes transpiler passes for synthesis, optimization, mapping, and scheduling.
+However, it also includes a default compiler, which works very well in most examples.
+The following code will map the example circuit to the `basis_gates = ['cz', 'sx', 'rz']` and a linear chain of qubits $0 \rightarrow 1 \rightarrow 2$ with the `coupling_map =[[0, 1], [1, 2]]`.
 
 ```python
 from qiskit import transpile

crates/accelerate/src/convert_2q_block_matrix.rs

@@ -10,7 +10,9 @@
 // copyright notice, and modified files need to carry a notice indicating
 // that they have been altered from the originals.
 
+use pyo3::intern;
 use pyo3::prelude::*;
+use pyo3::types::PyDict;
 use pyo3::wrap_pyfunction;
 use pyo3::Python;
 
@@ -20,32 +22,84 @@ use numpy::ndarray::{aview2, Array2, ArrayView2};
 use numpy::{IntoPyArray, PyArray2, PyReadonlyArray2};
 use smallvec::SmallVec;
 
+use qiskit_circuit::bit_data::BitData;
+use qiskit_circuit::circuit_instruction::{operation_type_to_py, CircuitInstruction};
+use qiskit_circuit::dag_node::DAGOpNode;
 use qiskit_circuit::gate_matrix::ONE_QUBIT_IDENTITY;
+use qiskit_circuit::imports::QI_OPERATOR;
+use qiskit_circuit::operations::{Operation, OperationType};
+
+use crate::QiskitError;
+
+fn get_matrix_from_inst<'py>(
+    py: Python<'py>,
+    inst: &'py CircuitInstruction,
+) -> PyResult<Array2<Complex64>> {
+    match inst.operation.matrix(&inst.params) {
+        Some(mat) => Ok(mat),
+        None => match inst.operation {
+            OperationType::Standard(_) => Err(QiskitError::new_err(
+                "Parameterized gates can't be consolidated",
+            )),
+            OperationType::Gate(_) => Ok(QI_OPERATOR
+                .get_bound(py)
+                .call1((operation_type_to_py(py, inst)?,))?
+                .getattr(intern!(py, "data"))?
+                .extract::<PyReadonlyArray2<Complex64>>()?
+                .as_array()
+                .to_owned()),
+            _ => unreachable!("Only called for unitary ops"),
+        },
+    }
+}
 
 /// Return the matrix Operator resulting from a block of Instructions.
 #[pyfunction]
 #[pyo3(text_signature = "(op_list, /")]
 pub fn blocks_to_matrix(
     py: Python,
-    op_list: Vec<(PyReadonlyArray2<Complex64>, SmallVec<[u8; 2]>)>,
+    op_list: Vec<PyRef<DAGOpNode>>,
+    block_index_map_dict: &Bound<PyDict>,
 ) -> PyResult<Py<PyArray2<Complex64>>> {
+    // Build a BitData in block_index_map_dict order. block_index_map_dict is a dict of bits to
+    // indices mapping the order of the qargs in the block. There should only be 2 entries since
+    // there are only 2 qargs here (e.g. `{Qubit(): 0, Qubit(): 1}`) so we need to ensure that
+    // we added the qubits to bit data in the correct index order.
+    let mut index_map: Vec<PyObject> = (0..block_index_map_dict.len()).map(|_| py.None()).collect();
+    for bit_tuple in block_index_map_dict.items() {
+        let (bit, index): (PyObject, usize) = bit_tuple.extract()?;
+        index_map[index] = bit;
+    }
+    let mut bit_map: BitData<u32> = BitData::new(py, "qargs".to_string());
+    for bit in index_map {
+        bit_map.add(py, bit.bind(py), true)?;
+    }
     let identity = aview2(&ONE_QUBIT_IDENTITY);
-    let input_matrix = op_list[0].0.as_array();
-    let mut matrix: Array2<Complex64> = match op_list[0].1.as_slice() {
+    let first_node = &op_list[0];
+    let input_matrix = get_matrix_from_inst(py, &first_node.instruction)?;
+    let mut matrix: Array2<Complex64> = match bit_map
+        .map_bits(first_node.instruction.qubits.bind(py).iter())?
+        .collect::<Vec<_>>()
+        .as_slice()
+    {
         [0] => kron(&identity, &input_matrix),
         [1] => kron(&input_matrix, &identity),
-        [0, 1] => input_matrix.to_owned(),
-        [1, 0] => change_basis(input_matrix),
+        [0, 1] => input_matrix,
+        [1, 0] => change_basis(input_matrix.view()),
         [] => Array2::eye(4),
         _ => unreachable!(),
     };
-    for (op_matrix, q_list) in op_list.into_iter().skip(1) {
-        let op_matrix = op_matrix.as_array();
+    for node in op_list.into_iter().skip(1) {
+        let op_matrix = get_matrix_from_inst(py, &node.instruction)?;
+        let q_list = bit_map
+            .map_bits(node.instruction.qubits.bind(py).iter())?
+            .map(|x| x as u8)
+            .collect::<SmallVec<[u8; 2]>>();
 
         let result = match q_list.as_slice() {
             [0] => Some(kron(&identity, &op_matrix)),
             [1] => Some(kron(&op_matrix, &identity)),
-            [1, 0] => Some(change_basis(op_matrix)),
+            [1, 0] => Some(change_basis(op_matrix.view())),
             [] => Some(Array2::eye(4)),
             _ => None,
         };
@@ -71,8 +125,42 @@ pub fn change_basis(matrix: ArrayView2<Complex64>) -> Array2<Complex64> {
     trans_matrix
 }
 
+#[pyfunction]
+pub fn collect_2q_blocks_filter(node: &Bound<PyAny>) -> Option<bool> {
+    match node.downcast::<DAGOpNode>() {
+        Ok(bound_node) => {
+            let node = bound_node.borrow();
+            match &node.instruction.operation {
+                OperationType::Standard(gate) => Some(
+                    gate.num_qubits() <= 2
+                        && node
+                            .instruction
+                            .extra_attrs
+                            .as_ref()
+                            .and_then(|attrs| attrs.condition.as_ref())
+                            .is_none()
+                        && !node.is_parameterized(),
+                ),
+                OperationType::Gate(gate) => Some(
+                    gate.num_qubits() <= 2
+                        && node
+                            .instruction
+                            .extra_attrs
+                            .as_ref()
+                            .and_then(|attrs| attrs.condition.as_ref())
+                            .is_none()
+                        && !node.is_parameterized(),
+                ),
+                _ => Some(false),
+            }
+        }
+        Err(_) => None,
+    }
+}
+
 #[pymodule]
 pub fn convert_2q_block_matrix(m: &Bound<PyModule>) -> PyResult<()> {
     m.add_wrapped(wrap_pyfunction!(blocks_to_matrix))?;
+    m.add_wrapped(wrap_pyfunction!(collect_2q_blocks_filter))?;
     Ok(())
 }

crates/accelerate/src/synthesis/linear/mod.rs

@@ -14,6 +14,7 @@ use crate::QiskitError;
 use numpy::{IntoPyArray, PyArray2, PyReadonlyArray2, PyReadwriteArray2};
 use pyo3::prelude::*;
 
+mod pmh;
 pub mod utils;
 
 #[pyfunction]
@@ -186,5 +187,6 @@ pub fn linear(m: &Bound<PyModule>) -> PyResult<()> {
     m.add_wrapped(wrap_pyfunction!(binary_matmul))?;
     m.add_wrapped(wrap_pyfunction!(random_invertible_binary_matrix))?;
     m.add_wrapped(wrap_pyfunction!(check_invertible_binary_matrix))?;
+    m.add_wrapped(wrap_pyfunction!(pmh::synth_cnot_count_full_pmh))?;
     Ok(())
 }