Adapt VQE- Paper Implementation Challenge

applications/chemistry/adapt_vqe_for_energy_curve/adapt_vqe_energy_curve.qmod

@@ -0,0 +1,80 @@
+qfunc main(t: real[30], output qbv: qbit[]) {
+  allocate(molecule_problem_to_hamiltonian(MoleculeProblem {
+    mapping=FermionMapping::JORDAN_WIGNER,
+    z2_symmetries=False,
+    molecule=Molecule {
+      atoms=[
+        ChemistryAtom {
+          element=Element::H,
+          position=Position {x=0.0, y=0.0, z=0.0}
+        },
+        ChemistryAtom {
+          element=Element::H,
+          position=Position {x=0.0, y=0.0, z=3.5}
+        }
+      ],
+      spin=1,
+      charge=0
+    },
+    freeze_core=False,
+    remove_orbitals=[]
+  })[0].pauli.len, qbv);
+  molecule_hartree_fock(MoleculeProblem {
+    mapping=FermionMapping::JORDAN_WIGNER,
+    z2_symmetries=False,
+    molecule=Molecule {
+      atoms=[
+        ChemistryAtom {
+          element=Element::H,
+          position=Position {x=0.0, y=0.0, z=0.0}
+        },
+        ChemistryAtom {
+          element=Element::H,
+          position=Position {x=0.0, y=0.0, z=3.5}
+        }
+      ],
+      spin=1,
+      charge=0
+    },
+    freeze_core=False,
+    remove_orbitals=[]
+  }, qbv);
+  full_hea(4, [1, 1, 1, 1, 1, 1], t, [
+    [0, 1],
+    [0, 2],
+    [0, 3],
+    [1, 2],
+    [1, 3],
+    [2, 3]
+  ], 1, [lambda(angle, q) {
+    RX(angle, q);
+  }, lambda(angle, q) {
+    RX(angle, q);
+  }, lambda(angle, q) {
+    RX(angle, q);
+  }], [lambda(angle, q1, q2) {
+    CRX(angle, q1, q2);
+  }, lambda(angle, q1, q2) {
+    CRX(angle, q1, q2);
+  }, lambda(angle, q1, q2) {
+    CRX(angle, q1, q2);
+  }], qbv);
+}
+
+cscope ```
+vqe_result = vqe(
+    hamiltonian=molecule_problem_to_hamiltonian(struct_literal(MoleculeProblem, mapping=FermionMapping.JORDAN_WIGNER, z2_symmetries=False, molecule=struct_literal(Molecule, atoms=[struct_literal(ChemistryAtom, element=Element.H, position=struct_literal(Position, x=0.0, y=0.0, z=0.0)), struct_literal(ChemistryAtom, element=Element.H, position=struct_literal(Position, x=0.0, y=0.0, z=3.5))], spin=1, charge=0), freeze_core=False, remove_orbitals=[])), maximize=False,
+initial_point=[],
+optimizer=Optimizer.COBYLA,
+max_iteration=20,
+tolerance=0,
+step_size=0,
+skip_compute_variance=False,
+alpha_cvar=1.0,
+
+)
+save({'vqe_result': vqe_result})
+
+molecule_result = molecule_ground_state_solution_post_process(struct_literal(MoleculeProblem, mapping=FermionMapping.JORDAN_WIGNER, z2_symmetries=False, molecule=struct_literal(Molecule, atoms=[struct_literal(ChemistryAtom, element=Element.H, position=struct_literal(Position, x=0.0, y=0.0, z=0.0)), struct_literal(ChemistryAtom, element=Element.H, position=struct_literal(Position, x=0.0, y=0.0, z=3.5))], spin=1, charge=0), freeze_core=False, remove_orbitals=[]),vqe_result)
+save({'molecule_result': molecule_result})
+```

applications/chemistry/adapt_vqe_for_energy_curve/adapt_vqe_energy_curve.synthesis_options.json

@@ -0,0 +1,46 @@
+{
+  "constraints": {
+    "max_gate_count": {},
+    "optimization_parameter": "no_opt"
+  },
+  "preferences": {
+    "machine_precision": 8,
+    "custom_hardware_settings": {
+      "basis_gates": [
+        "t",
+        "x",
+        "u1",
+        "cz",
+        "u2",
+        "rx",
+        "id",
+        "sxdg",
+        "cy",
+        "z",
+        "ry",
+        "rz",
+        "tdg",
+        "u",
+        "p",
+        "y",
+        "sx",
+        "r",
+        "cx",
+        "s",
+        "sdg",
+        "h"
+      ],
+      "is_symmetric_connectivity": true
+    },
+    "debug_mode": true,
+    "synthesize_all_separately": false,
+    "optimization_level": 3,
+    "output_format": [
+      "qasm"
+    ],
+    "pretty_qasm": true,
+    "transpilation_option": "auto optimize",
+    "timeout_seconds": 300,
+    "random_seed": 1066485126
+  }
+}

applications/chemistry/adapt_vqe_for_energy_curve/adapt_vqe_for_energy_curve.json~

@@ -0,0 +1,44 @@
+{
+  "constraints": {
+    "max_gate_count": {},
+    "optimization_parameter": "no_opt"
+  },
+  "preferences": {
+    "machine_precision": 8,
+    "custom_hardware_settings": {
+      "basis_gates": [
+        "u2",
+        "r",
+        "cy",
+        "t",
+        "sdg",
+        "x",
+        "z",
+        "s",
+        "h",
+        "sxdg",
+        "p",
+        "u",
+        "rx",
+        "rz",
+        "y",
+        "cz",
+        "tdg",
+        "cx",
+        "sx",
+        "u1",
+        "id",
+        "ry"
+      ],
+      "is_symmetric_connectivity": true
+    },
+    "debug_mode": true,
+    "synthesize_all_separately": false,
+    "optimization_level": 3,
+    "output_format": ["qasm"],
+    "pretty_qasm": true,
+    "transpilation_option": "auto optimize",
+    "timeout_seconds": 300,
+    "random_seed": 723171489
+  }
+}

applications/chemistry/molecular_energy_curve/molecular_energy_curve.ipynb

@@ -391,7 +391,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,

tests/notebooks/test_adapt_vqe_energy_curve.py

@@ -0,0 +1,24 @@
+from tests.utils_for_testbook import (
+    validate_quantum_program_size,
+    validate_quantum_model,
+    wrap_testbook,
+)
+from testbook.client import TestbookNotebookClient
+
+@wrap_testbook("adapt_vqe_energy_curve", timeout_seconds=600)
+def test_notebook(tb: TestbookNotebookClient) -> None:
+    """
+    A notebook for a hybrid classical quantum neural network.
+    The test verifies that the pre-trained model is indeed well trained.
+    """
+    # test models
+    for qmod in tb.ref("qmods"):
+        validate_quantum_model(qmod)
+    # test quantum programs
+    validate_quantum_program_size(
+        tb.ref("qprog"),
+        expected_width=1,  # actual width: 1
+        expected_depth=10,  # actual depth: 4
+    )
+    # test notebook content
+    pass  # TODO

tests/resources/timeouts.yaml

@@ -2,6 +2,10 @@
 3_sat_grover.qmod: 48
 3_sat_grover_large.qmod: 10
 3sat_oracles.ipynb: 1800
+
+adapt_vqe_energy_curve.ipynb: 600
+adapt_vqe_energy_curve.qmod: 600
+
 CRX.qmod: 10
 CX.qmod: 10
 HW1_QClass2024.ipynb: 200
@@ -22,6 +26,7 @@ SWAP.qmod: 10
 U.qmod: 10
 WS_iQuHack_2025_final.ipynb: 200
 X.qmod: 10
+
 add_bell_states.qmod: 10
 advection_equation_2nd_place_submission.ipynb: 500
 advection_equation_winning_submission.ipynb: 1200