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{"cells":[{"cell_type":"markdown","id":"fc72d98f-8993-4bd4-a545-23d122f3df71","metadata":{"id":"fc72d98f-8993-4bd4-a545-23d122f3df71"},"source":["# H₂ Molecule Homework Assignment\n","### Quantum Software Development Journey: From Theory to Application with Classiq - Part 3\n","\n","- Similarly to what we have done in class, in this exercise we will implement the VQE on H2 molecule.\n","- This time instead of using the built-in methods and functions (such as `Molecule` and `MoleculeProblem`) to difne and solve the problem, you will be provided with a two qubits Hamiltonian."]},{"cell_type":"markdown","id":"56eda6d8-76c4-4862-b914-0c4598d67274","metadata":{"id":"56eda6d8-76c4-4862-b914-0c4598d67274"},"source":["## Submission\n","- Submit the completed Jupyter notebook and report via GitHub. Ensure all files are correctly named and organized.\n","- Use the Typeform link provided in the submission folder to confirm your submission.\n","\n","## Additional Resources\n","- [Classiq Documentation](https://docs.classiq.io/latest/)\n","- The notebook from live session #3\n","\n","## Important Dates\n","- **Assignment Release:** 22.5.2024\n","- **Submission Deadline:** 3.6.2024 (7 A.M GMT+3)\n","\n","---\n","\n","Happy coding and good luck!"]},{"cell_type":"markdown","id":"d41e969d-f6a7-4ff7-9660-19ce6c97ba6b","metadata":{"id":"d41e969d-f6a7-4ff7-9660-19ce6c97ba6b"},"source":["### Part 1"]},{"cell_type":"markdown","id":"f710d6f4-d40b-42d5-b524-c6acb8059fe2","metadata":{"id":"f710d6f4-d40b-42d5-b524-c6acb8059fe2"},"source":["Given the following Hamiltonian:"]},{"cell_type":"markdown","id":"6ba8a6f1-3259-4492-a1ca-3abde7530cd4","metadata":{"id":"6ba8a6f1-3259-4492-a1ca-3abde7530cd4"},"source":["$$\n","\\hat{H} = -1.0523 \\cdot (I \\otimes I) + 0.3979 \\cdot (I \\otimes Z) - 0.3979 \\cdot (Z \\otimes I) - 0.0112 \\cdot (Z \\otimes Z) + 0.1809 \\cdot (X \\otimes X)\n","$$"]},{"cell_type":"markdown","id":"736d275c-9a5a-4c08-b891-3078430dc6c1","metadata":{"id":"736d275c-9a5a-4c08-b891-3078430dc6c1"},"source":["Complete the following code"]},{"cell_type":"code","source":["# !pip install -U -q classiq\n","import classiq\n","classiq.authenticate()\n","from classiq import *"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4Fnc5WqGypxB","executionInfo":{"status":"ok","timestamp":1717329207855,"user_tz":-120,"elapsed":91946,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}},"outputId":"aa98eadb-631f-4ad6-b8b2-8aee5d0edb9c"},"id":"4Fnc5WqGypxB","execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m398.1/398.1 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.7/10.7 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m72.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m75.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m93.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.4/6.4 MB\u001b[0m \u001b[31m72.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","torch 2.3.0+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n","torch 2.3.0+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\u001b[0m\u001b[31m\n","\u001b[0mYour user code: GZHQ-NPBB\n","If a browser doesn't automatically open, please visit this URL from any trusted device: https://auth.classiq.io/activate?user_code=GZHQ-NPBB\n"]}]},{"cell_type":"code","execution_count":5,"id":"19266c11-6acc-4edb-910f-2d0dfe80a6c8","metadata":{"id":"19266c11-6acc-4edb-910f-2d0dfe80a6c8","executionInfo":{"status":"ok","timestamp":1717329272619,"user_tz":-120,"elapsed":3,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}}},"outputs":[],"source":["from typing import List\n","HAMILTONIAN = QConstant(\"HAMILTONIAN\",\n"," List[PauliTerm],\n"," [\n"," PauliTerm([Pauli.I, Pauli.I], -1.0523),\n"," PauliTerm([Pauli.I, Pauli.Z], 0.3979),\n"," PauliTerm([Pauli.Z, Pauli.I], -0.3979),\n"," PauliTerm([Pauli.Z, Pauli.Z], -0.0112),\n"," PauliTerm([Pauli.X, Pauli.X], 0.1809),\n"," ])"]},{"cell_type":"code","execution_count":6,"id":"0bb68899-2076-45c0-8868-131f38aa3b78","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0bb68899-2076-45c0-8868-131f38aa3b78","executionInfo":{"status":"ok","timestamp":1717329277215,"user_tz":-120,"elapsed":2498,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}},"outputId":"ad0c9969-6048-43e6-b2e6-78489e9c9597"},"outputs":[{"output_type":"stream","name":"stdout","text":["Opening: https://platform.classiq.io/circuit/1ff23650-9aac-412f-94ec-c9dfdbaa7a6e?version=0.41.2\n"]}],"source":["@qfunc\n","def main(q: Output[QArray[QBit]], angles: CArray[CReal, 3]) -> None:\n"," allocate(2, q)\n"," U(angles[0], angles[1], angles[2], 0, q[0])\n"," U(angles[0], angles[1], angles[2], 0, q[1])\n","\n","\n","\n","@cfunc\n","def cmain() -> None:\n"," res = vqe(\n"," HAMILTONIAN,\n"," False,\n"," [],\n"," optimizer=Optimizer.COBYLA,\n"," max_iteration=1000,\n"," tolerance=0.001,\n"," step_size=0,\n"," skip_compute_variance=False,\n"," alpha_cvar=1.0,\n"," )\n"," save({\"result\": res})\n","\n","qmod = create_model(main, classical_execution_function=cmain)\n","qprog = synthesize(qmod)\n","show(qprog)"]},{"cell_type":"code","execution_count":7,"id":"0563c1a8-7aec-4da9-9105-6b16c5c24382","metadata":{"id":"0563c1a8-7aec-4da9-9105-6b16c5c24382","executionInfo":{"status":"ok","timestamp":1717329283986,"user_tz":-120,"elapsed":2574,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}}},"outputs":[],"source":["execution = execute(qprog)\n","res = execution.result()\n","# execution.open_in_ide()\n","vqe_result = res[0].value"]},{"cell_type":"code","execution_count":8,"id":"de17cfc0-8e64-4493-b4c2-4a97fc9797a0","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"de17cfc0-8e64-4493-b4c2-4a97fc9797a0","executionInfo":{"status":"ok","timestamp":1717329286246,"user_tz":-120,"elapsed":202,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}},"outputId":"28870970-3f8e-4ff0-d94d-64d6cd306987"},"outputs":[{"output_type":"stream","name":"stdout","text":["Optimal energy: -1.0768361328125\n","Optimal parameters: {'angles_0': 1.9429837733198028, 'angles_1': 4.856944464295574, 'angles_2': 4.0415675677515015}\n","Eigenstate: {'01': (0.45821494683172437+0j), '00': (0.3262595784034547+0j), '10': (0.46770717334674267+0j), '11': (0.6817945071647321+0j)}\n"]}],"source":["print(f\"Optimal energy: {vqe_result.energy}\")\n","print(f\"Optimal parameters: {vqe_result.optimal_parameters}\")\n","print(f\"Eigenstate: {vqe_result.eigenstate}\")"]},{"cell_type":"markdown","id":"5df11dfc-3e3a-4191-bd47-d522ca3dcbfa","metadata":{"id":"5df11dfc-3e3a-4191-bd47-d522ca3dcbfa"},"source":["Does it similar to the `optimal energy` we calculated in class? NO \\\n","Does it similar to the `total energy` we calculated in class?"]},{"cell_type":"markdown","id":"4f0e0bea-b12f-43ad-94e8-100fedf2b57f","metadata":{"id":"4f0e0bea-b12f-43ad-94e8-100fedf2b57f"},"source":["### Part 2"]},{"cell_type":"markdown","id":"66882248-de08-4a6e-b33c-647f015f7d79","metadata":{"id":"66882248-de08-4a6e-b33c-647f015f7d79"},"source":["**Now, we want to have a more interesting ansatz in our `main`.** \n","Add **one** line of code to the `main` function you created in Part 1 that creates **entanglement** between the two qubits. \n","Which gate should you use?"]},{"cell_type":"code","execution_count":9,"id":"bb39be9e-4994-44e5-84d8-c99bc8b77145","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bb39be9e-4994-44e5-84d8-c99bc8b77145","executionInfo":{"status":"ok","timestamp":1717329417332,"user_tz":-120,"elapsed":2484,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}},"outputId":"b79d41d2-b8d7-4076-ec09-c56972612395"},"outputs":[{"output_type":"stream","name":"stdout","text":["Opening: https://platform.classiq.io/circuit/d08ae872-3de0-40d6-a66e-db1fcc1a096a?version=0.41.2\n"]}],"source":["@qfunc\n","def main(q: Output[QArray[QBit]], angles: CArray[CReal, 3]) -> None:\n"," allocate(2, q)\n"," U(angles[0], angles[1], angles[2], 0, q[0])\n"," CX(q[0], q[1])\n"," U(angles[0], angles[1], angles[2], 0, q[1])\n","\n","\n","\n","@cfunc\n","def cmain() -> None:\n"," res = vqe(\n"," HAMILTONIAN,\n"," False,\n"," [],\n"," optimizer=Optimizer.COBYLA,\n"," max_iteration=1000,\n"," tolerance=0.001,\n"," step_size=0,\n"," skip_compute_variance=False,\n"," alpha_cvar=1.0,\n"," )\n"," save({\"result\": res})\n","\n","qmod = create_model(main, classical_execution_function=cmain)\n","qprog = synthesize(qmod)\n","show(qprog)"]},{"cell_type":"code","execution_count":10,"id":"112a1590-283c-4f79-8035-72936561102d","metadata":{"id":"112a1590-283c-4f79-8035-72936561102d","executionInfo":{"status":"ok","timestamp":1717329433362,"user_tz":-120,"elapsed":3487,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}}},"outputs":[],"source":["execution = execute(qprog)\n","res = execution.result()\n","# execution.open_in_ide()\n","vqe_result = res[0].value"]},{"cell_type":"code","execution_count":11,"id":"06500e4c-a04b-4cfa-a84d-41f96a0c68eb","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"colab":{"base_uri":"https://localhost:8080/"},"id":"06500e4c-a04b-4cfa-a84d-41f96a0c68eb","executionInfo":{"status":"ok","timestamp":1717329433363,"user_tz":-120,"elapsed":3,"user":{"displayName":"Christos Koromilas","userId":"17599903479737751299"}},"outputId":"11e4d470-38be-4918-e309-90209c8679b8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Optimal energy: -1.84172490234375\n","Optimal parameters: {'angles_0': -3.2337677964973515, 'angles_1': 5.719762730583857, 'angles_2': 0.8847642868459891}\n","Eigenstate: {'11': (0.04419417382415922+0j), '10': (0.05846339666834283+0j), '01': (0.9973108373270593+0j)}\n"]}],"source":["print(f\"Optimal energy: {vqe_result.energy}\")\n","print(f\"Optimal parameters: {vqe_result.optimal_parameters}\")\n","print(f\"Eigenstate: {vqe_result.eigenstate}\")"]},{"cell_type":"markdown","id":"30a635d7-2f15-4c94-a94b-f4270f17aed8","metadata":{"id":"30a635d7-2f15-4c94-a94b-f4270f17aed8"},"source":["Does it similar to the `optimal energy` we calculated in class? YES \\\n","Does it similar to the `total energy` we calculated in class? \\\n","What can we learn about the provided form this result Hamiltonian? The hamiltonian is the right hamiltonian to use!"]},{"cell_type":"code","source":["res[2]"],"metadata":{"id":"_PmeBI7Z0BUH"},"id":"_PmeBI7Z0BUH","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.16"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}

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