diff --git a/algorithms/quantum_phase_estimation/qpe_with_qubitization/qpe_for_molecule_with_qubitization.ipynb b/algorithms/quantum_phase_estimation/qpe_with_qubitization/qpe_for_molecule_with_qubitization.ipynb index 80b80c5b9..aacdff360 100644 --- a/algorithms/quantum_phase_estimation/qpe_with_qubitization/qpe_for_molecule_with_qubitization.ipynb +++ b/algorithms/quantum_phase_estimation/qpe_with_qubitization/qpe_for_molecule_with_qubitization.ipynb @@ -77,7 +77,8 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", - "from classiq import *" + "from classiq import *\n", + "from classiq.applications.chemistry.op_utils import qubit_op_to_qmod" ] }, { @@ -89,6 +90,8 @@ "source": [ "from sympy import fwht\n", "\n", + "from classiq.open_library.functions.state_preparation import apply_phase_table\n", + "\n", "\n", "def get_graycode(size, i) -> int:\n", " if i == 2**size:\n", @@ -129,46 +132,6 @@ " skip_control(lambda: CX(qba[controllers[k]], ind))\n", "\n", "\n", - "# TODO change to classiq when available\n", - "\n", - "\n", - "def _control_qubit(i: int) -> int:\n", - " if _msb(_graycode(i)) < _msb(_graycode(i + 1)):\n", - " return (_graycode(i) & _graycode(i + 1)).bit_length() - 1\n", - " return (_graycode(i) ^ _graycode(i + 1)).bit_length() - 1\n", - "\n", - "\n", - "def _graycode(i: int) -> int:\n", - " return i ^ (i >> 1)\n", - "\n", - "\n", - "def _msb(x: int) -> int:\n", - " \"\"\"\n", - " largest non zero bit\n", - " \"\"\"\n", - " if x == 0:\n", - " return 0\n", - " return x.bit_length() - 1\n", - "\n", - "\n", - "@qperm\n", - "def my_apply_phase_table(\n", - " phases: list[float],\n", - " target: QArray,\n", - ") -> None:\n", - " alphas = -2 * (1 / len(phases)) * np.array(fwht(np.array(phases)))\n", - "\n", - " for i in range(1, len(alphas) - 1):\n", - " gray = _graycode(i)\n", - " next_gray = _graycode(i + 1)\n", - " RZ(alphas[gray], target[_msb(gray)])\n", - " CX(target[_control_qubit(i)], target[_msb(next_gray)])\n", - "\n", - " RZ(alphas[_graycode(len(phases) - 1)], target[target.len - 1])\n", - " # fix the global phase:\n", - " phase(-0.5 * alphas[0])\n", - "\n", - "\n", "@qfunc\n", "def lcu_paulis_graycode(terms: list[SparsePauliTerm], data: QArray, block: QArray):\n", " n_qubits = data.len\n", @@ -198,43 +161,13 @@ " for i in range(n_qubits):\n", " multiplex_ra(0, 1, table_z[i, :], block, data[i])\n", " multiplex_ra(1, 0, table_y[i, :], block, data[i])\n", - " my_apply_phase_table(\n", + " apply_phase_table(\n", " [p1 - p2 for p1, p2 in zip(hamiltonian_coeffs, accumulated_phase)], block\n", " )\n", "\n", " within_apply(\n", " lambda: inplace_prepare_state(probs, 0.0, block),\n", " lambda: select_graycode(block, data),\n", - " )\n", - "\n", - "\n", - "from typing import cast\n", - "\n", - "import numpy as np\n", - "from openfermion import QubitOperator\n", - "from openfermion.utils.operator_utils import count_qubits\n", - "\n", - "from classiq import *\n", - "\n", - "\n", - "# TODO: remove after bug fix\n", - "def of_op_to_cl_op(qubit_op: QubitOperator) -> SparsePauliOp:\n", - " n_qubits = cast(int, count_qubits(qubit_op))\n", - " return SparsePauliOp(\n", - " terms=[\n", - " SparsePauliTerm(\n", - " paulis=[\n", - " IndexedPauli(\n", - " pauli=getattr(Pauli, pauli),\n", - " index=qubit,\n", - " )\n", - " for qubit, pauli in term\n", - " ],\n", - " coefficient=coeff,\n", - " )\n", - " for term, coeff in qubit_op.terms.items()\n", - " ],\n", - " num_qubits=n_qubits,\n", " )" ] }, @@ -352,7 +285,7 @@ "metadata": {}, "outputs": [], "source": [ - "mol_hamiltonian = of_op_to_cl_op(qubit_hamiltonian)\n", + "mol_hamiltonian = qubit_op_to_qmod(qubit_hamiltonian)\n", "num_qubits = mol_hamiltonian.num_qubits\n", "be_scaling = sum(np.abs(term.coefficient) for term in mol_hamiltonian.terms)\n", "normalized_mol_hamiltonian = mol_hamiltonian * (1 / be_scaling)" @@ -538,7 +471,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Quantum program link: https://platform.classiq.io/circuit/36pM565NYQMrVe0YJ4moiM38FR1\n" + "Quantum program link: https://platform.classiq.io/circuit/38VyDBs3xcSfRT772EkonhMeqwA\n" ] } ], @@ -583,14 +516,14 @@ "output_type": "stream", "text": [ "Classical solution:, -1.1373060357533995 Ha\n", - "probability to measure the block variable at state zero: 0.50439453125\n", + "probability to measure the block variable at state zero: 0.50732421875\n", "\n", "Energy with maximal probability: -1.102846988772674 Ha\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -686,7 +619,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Quantum program link: https://platform.classiq.io/circuit/36pMA3Kf7ScKHkss074fHlUDkl1\n" + "Quantum program link: https://platform.classiq.io/circuit/38VyLEYiNJ1PXIRn5XDbpa2V6MN\n" ] } ], @@ -731,14 +664,14 @@ "output_type": "stream", "text": [ "Classical solution:, -1.1373060357533995 Ha\n", - "probability to measure the block variable at state zero: 0.5078125\n", + "probability to measure the block variable at state zero: 0.47119140625\n", "\n", "Energy with maximal probability: -1.102846988772674 Ha\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -766,10 +699,10 @@ "output_type": "stream", "text": [ "For the naive QPE on the walk operator:\n", - "depth: 18772\n", + "depth: 19686\n", "========================================\n", "For the optimized QPE on the walk operator:\n", - "depth: 4824\n", + "depth: 4724\n", "========================================\n" ] }