our_attempt.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"cell_id":"194028facc014202b9b41d1983159333","deepnote_cell_type":"markdown"},"source":"# Classiq Challenge - Quantum Arithmetics","block_group":"80b3744e3b75412481f30596f8b74d8f"},{"cell_type":"markdown","metadata":{"cell_id":"4ee545a9b1584932a262593b5b534230","deepnote_cell_type":"markdown"},"source":"Welcome to the Classiq challenge of the 2024 MIT IQuHack hackathon!\nThis Jupyter notebook is the framework for the challenge and you should follow it in order to solve it using Classiq (alongside the guidelines of the challenge).\n\nThe notebook has 3 parts:\n1. A warm up session, just to get up to speed with a small example of Classiq\n2. The challenge itself, including what you need to create, what's the format, and how you can check yourself\n3. A cheat sheet with relevant Classiq functions declarations\n\nAdditional resources you should use are\n- The IDE of the classiq platform at [platform.classiq.io](platform.classiq.io)\n- The [community Slack of Classiq](https://short.classiq.io/join-slack) - Classiq's team will answer any question you have over there, including implementation questions\n- [Classiq's documentation](https://docs.classiq.io/latest/user-guide/platform/) with the dedicated [Python SDK explanations](https://docs.classiq.io/latest/user-guide/platform/qmod/python/functions/)\n\nGood luck!","block_group":"3b80623fed8a4b6b9fb6ef59c0fadd40"},{"cell_type":"markdown","metadata":{"cell_id":"bf200f55e202485aae0560181375c889","deepnote_cell_type":"markdown"},"source":"![MITClassiq](data:image/png;base64,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)","block_group":"b76d3f171d7c4e6891aa6c51a06f8500"},{"cell_type":"markdown","metadata":{"cell_id":"7aeb0bfd37e8445bbb5b013f5e08aa55","deepnote_cell_type":"markdown"},"source":"## Warm Up","block_group":"c605d237b8e948dda108b769a70d4509"},{"cell_type":"markdown","metadata":{"cell_id":"8ffc1e8d50ea40399f70d8ef8efae2ac","deepnote_cell_type":"markdown"},"source":"Firstly, if you haven't done so far, you need to install the Classiq SDK package: ","block_group":"0d464f4e55704c1da73a19adfc4d18f6"},{"cell_type":"markdown","metadata":{"source_hash":"e5e19a35","execution_start":1707008565378,"execution_millis":33102,"deepnote_to_be_reexecuted":false,"deepnote_app_block_visible":false,"cell_id":"1f40c5efa11e4dc8a0ffe7382af59975","deepnote_cell_type":"markdown"},"source":"!pip install -U classiq\n!pip freeze > requirements.txt","block_group":"664fc42dfaf540789a2f1b6821e34eae"},{"cell_type":"markdown","metadata":{"cell_id":"984304cfef944103953f7693bafdb7f6","deepnote_cell_type":"markdown"},"source":"and to authenticate your device against the Classiq backend:","block_group":"eae1c4fa60dd4cd1ad06d2c9e9ef8406"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707074293939,"execution_millis":21,"deepnote_to_be_reexecuted":false,"cell_id":"c311db95313845589b48c984f91ec77e","deepnote_cell_type":"code"},"source":"import classiq\nclassiq.authenticate()","block_group":"d89968e3bb294dd69be13b8eb3b929f9","execution_count":null,"outputs":[{"name":"stderr","text":"/root/venv/lib/python3.10/site-packages/classiq/_internals/authentication/token_manager.py:82: UserWarning: Device is already registered.\nGenerating a new refresh token should only be done if the current refresh token is compromised.\nTo do so, set the overwrite parameter to true\n  warnings.warn(\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"cell_id":"e028c58b63184c9ab42bdd39e24dcee2","deepnote_cell_type":"markdown"},"source":"Let's create a uniform superposition of numbers in the domain $[0,1)$ with equal spacing of $\\frac{1}{2^{K-1}}$ for $K=5$:\n\nInitializes $K$ qubits such that each qubit $\\left|i\\right>$ represents $x\\in\\{0,\\frac{1}{2^K}, \\frac{2}{2^K}, \\ldots \\frac{k}{2^K} \\}$","block_group":"21d45dde7696458d969a98d1476f835b"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707028828166,"execution_millis":2301,"deepnote_to_be_reexecuted":false,"cell_id":"d4d96ce2071b49acbce8811c2019d16d","deepnote_cell_type":"code"},"source":"from classiq import *\nK =5\n\n@QFunc\ndef main(x:Output[QNum]):\n    \n    allocate_num(num_qubits=K, is_signed=False, fraction_digits=K,out=x)\n    # puts all qubits into a superposition of states\n    hadamard_transform(x)\n\nquantum_model = create_model(main)\nquantum_program = synthesize(quantum_model)\n\nshow(quantum_program) # view the circuit in the IDE\n\njob = execute(quantum_program)\njob.open_in_ide() # view the resulted histogram in the IDE","block_group":"8f206fc36e854c50a96d4873f93a5ee2","execution_count":null,"outputs":[{"name":"stdout","text":"Opening: https://platform.classiq.io/circuit/25e15e35-1698-4d69-812a-ce5e9003dbe6?version=0.36.1\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"cell_id":"6b63337b2c6f481585c2ad8949a95b4a","deepnote_cell_type":"markdown"},"source":"That's cool right? :)\n\nIf you are more a Python person, this is how you can post-process your results:","block_group":"8381f009ae364571b468b3896161f0f1"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707028834177,"execution_millis":719,"deepnote_to_be_reexecuted":false,"cell_id":"6f656df80f134f4daadc7bcc666cca7b","deepnote_cell_type":"code"},"source":"results = job\nparsed_counts = results.result()[0].value.parsed_counts\nsorted_counts = sorted(parsed_counts, key=lambda x: x.state[\"x\"])\nfor state_counts in sorted_counts: print('State: ', state_counts.state, '| Shots: ', state_counts.shots)","block_group":"015d70fefb014c3fa2321e274740670f","execution_count":null,"outputs":[{"name":"stdout","text":"State:  {'x': 0.0} | Shots:  67\nState:  {'x': 0.03125} | Shots:  64\nState:  {'x': 0.0625} | Shots:  67\nState:  {'x': 0.09375} | Shots:  64\nState:  {'x': 0.125} | Shots:  63\nState:  {'x': 0.15625} | Shots:  64\nState:  {'x': 0.1875} | Shots:  47\nState:  {'x': 0.21875} | Shots:  77\nState:  {'x': 0.25} | Shots:  63\nState:  {'x': 0.28125} | Shots:  59\nState:  {'x': 0.3125} | Shots:  71\nState:  {'x': 0.34375} | Shots:  78\nState:  {'x': 0.375} | Shots:  67\nState:  {'x': 0.40625} | Shots:  62\nState:  {'x': 0.4375} | Shots:  58\nState:  {'x': 0.46875} | Shots:  71\nState:  {'x': 0.5} | Shots:  65\nState:  {'x': 0.53125} | Shots:  75\nState:  {'x': 0.5625} | Shots:  68\nState:  {'x': 0.59375} | Shots:  48\nState:  {'x': 0.625} | Shots:  50\nState:  {'x': 0.65625} | Shots:  57\nState:  {'x': 0.6875} | Shots:  59\nState:  {'x': 0.71875} | Shots:  60\nState:  {'x': 0.75} | Shots:  63\nState:  {'x': 0.78125} | Shots:  67\nState:  {'x': 0.8125} | Shots:  61\nState:  {'x': 0.84375} | Shots:  66\nState:  {'x': 0.875} | Shots:  62\nState:  {'x': 0.90625} | Shots:  63\nState:  {'x': 0.9375} | Shots:  55\nState:  {'x': 0.96875} | Shots:  87\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"cell_id":"1e56a78e06c043c7a98699f21563cd31","deepnote_cell_type":"markdown"},"source":"## The Challenge Itself","block_group":"45b32d6c3dfd4df095b63b0fb0ed2432"},{"cell_type":"markdown","metadata":{"cell_id":"d7383dd183a24c86b2c55fba8579d7e2","deepnote_cell_type":"markdown"},"source":"### Imports","block_group":"8ce1fcd9265e4cd8892aebf2b0f898b9"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707074301722,"execution_millis":10,"deepnote_to_be_reexecuted":false,"cell_id":"d96ec293476d414f977dfe1431b8493d","deepnote_cell_type":"code"},"source":"# All the imports needed\nfrom classiq import *\n\nfrom classiq.interface.backend.backend_preferences import ClassiqBackendPreferences\nfrom classiq.interface.executor.execution_preferences import ExecutionPreferences\nfrom classiq.interface.model.model import Model as Model_Designer\n\nimport numpy as np\nimport json","block_group":"6170b0a0cd214c129956b57010620193","execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707074302197,"execution_millis":7,"deepnote_to_be_reexecuted":false,"cell_id":"deba4a005b9d486e99bfd86be8b8b8f0","deepnote_cell_type":"code"},"source":"def print_depth_width(quantum_program):\n    generated_circuit = GeneratedCircuit.parse_obj(json.loads(quantum_program))\n    print(f\"Synthesized circuit width: {generated_circuit.data.width}, depth: {generated_circuit.transpiled_circuit.depth}\")","block_group":"841283140e174adeabd9807aafd176de","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"cell_id":"3fe7800a1da34814abd3d93893f58195","deepnote_cell_type":"markdown"},"source":"### The Heart of the Code - Build Your Algorithm Here","block_group":"82bbee2f90df4a16bf742f8fc762f763"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707074816096,"execution_millis":66,"deepnote_to_be_reexecuted":false,"cell_id":"55b3055351624096abeae844392b0873","deepnote_cell_type":"code"},"source":"# not this one\nfrom classiq.builtin_functions import AmplitudeLoading\n@QFunc\ndef compute_tanh(x: QNum, y: Output[QNum]):    \n    a = AmplitudeLoading(\n    size=3,\n    expression=\"1/x\",\n    )\n    y|= a(x) \n\n# Your precision value\nprecision = 4\n\n@QFunc\ndef main(x: Output[QNum],y: Output[QNum]):\n    \n    # A quantum variable which represents numbers is initialized\n    allocate_num(num_qubits=precision,      \n                is_signed=False,            \n                fraction_digits=precision,\n                out=x)\n    \n    # Initalizing over all possible values of the quantum number with the hadamard transform\n    hadamard_transform(x)\n    # Computing your compute_tanh function\n    compute_tanh(x,y)\n\n# Generating the model itself\nqmod = create_model(main)\nqmod = set_constraints(qmod,Constraints(max_width=30,max_depth=30000, optimization_parameter='depth'))\nqprog = synthesize(qmod)\nprint_depth_width(qprog)\n# show(qprog)","block_group":"521e6eda2e1b4a7d9886dff55d4c5dcb","execution_count":null,"outputs":[{"output_type":"error","ename":"TypeError","evalue":"'AmplitudeLoading' object is not callable","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","Cell \u001b[0;32mIn [8], line 29\u001b[0m\n\u001b[1;32m     26\u001b[0m     compute_tanh(x,y)\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m# Generating the model itself\u001b[39;00m\n\u001b[0;32m---> 29\u001b[0m qmod \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmain\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m qmod \u001b[38;5;241m=\u001b[39m set_constraints(qmod,Constraints(max_width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m,max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30000\u001b[39m, optimization_parameter\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdepth\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m     31\u001b[0m qprog \u001b[38;5;241m=\u001b[39m synthesize(qmod)\n","File \u001b[0;32m~/venv/lib/python3.10/site-packages/classiq/qmod/quantum_function.py:33\u001b[0m, in \u001b[0;36mcreate_model\u001b[0;34m(entry_point, constraints, execution_preferences, preferences)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_model\u001b[39m(\n\u001b[1;32m     28\u001b[0m     entry_point: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQFunc\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     29\u001b[0m     constraints: Optional[Constraints] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     30\u001b[0m     execution_preferences: Optional[ExecutionPreferences] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     31\u001b[0m     preferences: Optional[Preferences] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     32\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m SerializedModel:\n\u001b[0;32m---> 33\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mentry_point\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconstraints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecution_preferences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreferences\u001b[49m\n\u001b[1;32m     35\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mget_model()\n","File \u001b[0;32m~/venv/lib/python3.10/site-packages/classiq/qmod/quantum_function.py:63\u001b[0m, in \u001b[0;36mQFunc.create_model\u001b[0;34m(self, constraints, execution_preferences, preferences)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mtype_decls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m()\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mnative_defs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m()\n\u001b[0;32m---> 63\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_add_native_func_def\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     64\u001b[0m model_extra_settings: List[Tuple[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     65\u001b[0m     (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconstraints\u001b[39m\u001b[38;5;124m\"\u001b[39m, constraints),\n\u001b[1;32m     66\u001b[0m     (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexecution_preferences\u001b[39m\u001b[38;5;124m\"\u001b[39m, execution_preferences),\n\u001b[1;32m     67\u001b[0m     (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpreferences\u001b[39m\u001b[38;5;124m\"\u001b[39m, preferences),\n\u001b[1;32m     68\u001b[0m ]\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Model(\n\u001b[1;32m     70\u001b[0m     functions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mnative_defs\u001b[38;5;241m.\u001b[39mvalues()),\n\u001b[1;32m     71\u001b[0m     types\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mtype_decls\u001b[38;5;241m.\u001b[39mvalues()),\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{key: value \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m model_extra_settings \u001b[38;5;28;01mif\u001b[39;00m value},\n\u001b[1;32m     73\u001b[0m )\n","File \u001b[0;32m~/venv/lib/python3.10/site-packages/classiq/qmod/quantum_function.py:78\u001b[0m, in \u001b[0;36mQFunc._add_native_func_def\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_decl\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mnative_defs:\n\u001b[1;32m     77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mnative_defs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_decl\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;241m=\u001b[39m NativeFunctionDefinition(\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_decl\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m, body\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbody\n\u001b[1;32m     81\u001b[0m )\n","File \u001b[0;32m~/venv/lib/python3.10/site-packages/classiq/qmod/quantum_expandable.py:69\u001b[0m, in \u001b[0;36mQExpandable.expand\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexpand\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m---> 69\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_py_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_positional_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn [8], line 26\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m     24\u001b[0m hadamard_transform(x)\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# Computing your compute_tanh function\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[43mcompute_tanh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/venv/lib/python3.10/site-packages/classiq/qmod/quantum_function.py:53\u001b[0m, in \u001b[0;36mQFunc.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 53\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_add_native_func_def\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m~/venv/lib/python3.10/site-packages/classiq/qmod/quantum_function.py:78\u001b[0m, in \u001b[0;36mQFunc._add_native_func_def\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_decl\u001b[38;5;241m.\u001b[39mname \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mnative_defs:\n\u001b[1;32m     77\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpand\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qmodule\u001b[38;5;241m.\u001b[39mnative_defs[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_decl\u001b[38;5;241m.\u001b[39mname] \u001b[38;5;241m=\u001b[39m NativeFunctionDefinition(\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_decl\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m, body\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbody\n\u001b[1;32m     81\u001b[0m )\n","File \u001b[0;32m~/venv/lib/python3.10/site-packages/classiq/qmod/quantum_expandable.py:69\u001b[0m, in \u001b[0;36mQExpandable.expand\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexpand\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m---> 69\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_py_callable\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_positional_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn [8], line 9\u001b[0m, in \u001b[0;36mcompute_tanh\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;129m@QFunc\u001b[39m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute_tanh\u001b[39m(x: QNum, y: Output[QNum]):    \n\u001b[1;32m      5\u001b[0m     a \u001b[38;5;241m=\u001b[39m AmplitudeLoading(\n\u001b[1;32m      6\u001b[0m     size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m,\n\u001b[1;32m      7\u001b[0m     expression\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1/x\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      8\u001b[0m     )\n\u001b[0;32m----> 9\u001b[0m     y\u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43ma\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n","\u001b[0;31mTypeError\u001b[0m: 'AmplitudeLoading' object is not callable"]}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707056674478,"execution_millis":5616,"deepnote_to_be_reexecuted":false,"cell_id":"812aa47b8a964e8394e1bde38c5357d5","deepnote_cell_type":"code"},"source":"# The heart of the code - the function that calculates the hyperbolic tangent\n@QFunc\ndef compute_tanh(precision: QParam[int],    # The precision of x and tanh_x - how many fraction digits will represent your solution\n                 x: QNum,                   # A quantum variable that is represents numbers\n                 tanh_x: Output[QNum]       # The output of the function, which means it needs to be initialized within the scope of the function\n                 ):\n    \n    # throws error because of no cloning theorem\n    # tanh_x |= x\n\n    # Pade approximation;\n    # does not work; division of quantum numbers not supported (maybe figure out workaround?)\n    # tanh_x |= x*( (27 + x**2) * (27 + 9*x**2) **(-1) )\n\n    # Taylor series approximation;\n    # does not work; needs way too many auxilliary qubits \n    #tanh_x |= x - ( (x**3)*(1/3) ) + ( (x**5)*(2/15) )\n    # too many \n\n    # Linear approximation using minimized RSS\n    #tanh_x |= 0.7*x+0.05\n\n    tanh_x |= 0.774492*x+0.046076\n\n# Your precision value\nprecision = 4\n\n@QFunc\ndef main(x: Output[QNum],y: Output[QNum]):\n    \n    # A quantum variable which represents numbers is initialized\n    allocate_num(num_qubits=precision,      \n                is_signed=False,            \n                fraction_digits=precision,\n                out=x)\n    \n    # Initalizing over all possible values of the quantum number with the hadamard transform\n    hadamard_transform(x)\n\n    # Computing your compute_tanh function\n    compute_tanh(precision,x,y)\n\n# Generating the model itself\nqmod = create_model(main)\nqmod = set_constraints(qmod,Constraints(max_width=30,max_depth=30000, optimization_parameter='depth'))\nqprog = synthesize(qmod)\nprint_depth_width(qprog)\n# show(qprog)","block_group":"881d843e58db46b6b840d1f036fda926","execution_count":null,"outputs":[{"name":"stdout","text":"Synthesized circuit width: 28, depth: 413\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"cell_id":"e045773f0b1440bc9d692937ecd15ae8","deepnote_cell_type":"markdown"},"source":"### Saving the Files - What You Need To Submit","block_group":"763e386fd6854f1781c06f941b5fbf59"},{"cell_type":"markdown","metadata":{"cell_id":"d68ff708312b458f8d0c329c225892f0","deepnote_cell_type":"markdown"},"source":"First, define the precisions of your solutions:","block_group":"f444ce6495db4a7284b4803548bfab44"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057148139,"execution_millis":15,"deepnote_to_be_reexecuted":false,"cell_id":"24d8d18ac4314128b189570bb636fd85","deepnote_cell_type":"code"},"source":"#Precision values stages 1&2 - You need to enter these values in Google Form submission\nprecision_value_stage_1 = 4 \nprecision_value_stage_2 = 40","block_group":"7beb17bb4c634a80b11c14ff026ca27a","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"cell_id":"e7574b7a0c1b4f5993971259cb163538","deepnote_cell_type":"markdown"},"source":"Then we can create adapted quantum models for the two stages according to the corresponding precisions and constraints:","block_group":"149125fe175248c5b12298cc9bc0ce36"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057150772,"execution_millis":52,"deepnote_to_be_reexecuted":false,"cell_id":"1b41e6f1380641bd941455814d235882","deepnote_cell_type":"code"},"source":"def get_model_with_new_precision(qmod, new_precision):\n    '''\n    The function adapts the precision value in the qmod according to the new precision value given.\n    '''\n    \n    @QFunc\n    def compute_tanh(\n        precision: QParam[int],\n        x: QNum,\n        tanh_x: Output[QNum],\n    ):\n        tanh_x |= 0.774492*x+0.046076\n\n    @QFunc\n    def main(x: Output[QNum], y: Output[QNum]):\n        allocate_num(new_precision, False, new_precision, x)\n        hadamard_transform(x)\n        compute_tanh(new_precision, x, y)\n\n    new_main = main.create_model().main_func\n    parsed_qmod = Model_Designer.parse_obj(json.loads(qmod))\n    new_model = Model_Designer(functions=[f for f in parsed_qmod.functions if f.name != \"main\"] + [new_main])\n    return new_model.get_model()\n\n\n# Adapting the qmod for the relevant constraints - stage 1\nmax_width_stage_1 = 30\nmax_depth_stage_1 = 3000\nsynthesis_timeout_sec_stage_1 = 90\nqmod_stage_1 = get_model_with_new_precision(qmod,precision_value_stage_1)\nqmod_stage_1 = set_constraints(qmod_stage_1,Constraints(max_width=max_width_stage_1,max_depth=max_depth_stage_1))\nqmod_stage_1 = set_preferences(qmod_stage_1, Preferences(timeout_seconds=synthesis_timeout_sec_stage_1))\n\n# Adapting the qmod for the relevant constraints - stage 2\nmax_width_stage_2 = 100\nmax_depth_stage_2 = 30000\nsynthesis_timeout_sec_stage_2 = 180\noptimization_param = \"depth\"\nqmod_stage_2 = get_model_with_new_precision(qmod,precision_value_stage_2)\nqmod_stage_2 = set_constraints(qmod_stage_2,Constraints(max_width=max_width_stage_2,max_depth=max_depth_stage_2,optimization_parameter=optimization_param))\nqmod_stage_2 = set_preferences(qmod_stage_2, Preferences(timeout_seconds=synthesis_timeout_sec_stage_2))\n","block_group":"2193226fa6b447beaad52b88500e5227","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"cell_id":"ce7cb2551a254b8691307c18c06a050b","deepnote_cell_type":"markdown"},"source":"Now in order to save the deliverables files, you should adapt the following file names including their locations (no need to include the file extensions, i.e. do not add `.qprog`/`.qmod` at the end):","block_group":"0a748de5585140549923eb4134dda80d"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057153874,"execution_millis":11,"deepnote_to_be_reexecuted":false,"cell_id":"0c126ede973b4802adeb0489f34792e0","deepnote_cell_type":"code"},"source":"# Files names (no extensions needed!)\nfile_name_qmod = 'qmod_default' #TODO change to your file_name\nfile_name_qprog_stage_1 = 'qprog_stage_1_default' #TODO change to your file_name\nfile_name_qprog_stage_2 = 'qprog_stage_2_default' #TODO change to your file_name","block_group":"10fece13d52a42ad838a2c5c9fcaa820","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"cell_id":"290ecc3b55e845e2af1893a4ebba5ebc","deepnote_cell_type":"markdown"},"source":"Run the following cell to save the relevant deliverables:\n1. Your model (`.qmod` file)\n2. Your quantum program for stage 1 with the corresponding precision (`.qprog` file)\n3. Your quantum program for stage 2 with the corresponding precision (`.qprog` file)","block_group":"0ecc0d73ad884a58be37bb3a4b8dc9d4"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057156500,"execution_millis":51845,"deepnote_to_be_reexecuted":false,"cell_id":"8ebbeee34473475495b2046db1116d99","deepnote_cell_type":"code"},"source":"def save_new_file(program_to_save,file_name):\n    '''\n    A simple function that saves your code as a text file\n    '''\n    file = open(file_name,\"w\")\n    file.write(program_to_save)\n    file.close()\n\n\n# Saving the qmod\nsave_new_file(qmod, file_name_qmod+'.qmod') # You need to submit this file\n\n# Save the qprof of stage 1\nqprog_stage_1 = synthesize(qmod_stage_1)\nsave_new_file(qprog_stage_1, file_name_qprog_stage_1+'.qprog') # You need to submit this file\n\n# Save the qprog of stage 2\nqprog_stage_2 = synthesize(qmod_stage_2)\nsave_new_file(qprog_stage_2, file_name_qprog_stage_2+'.qprog') # You need to submit this file","block_group":"ff67646a4bc44c1f877c6202741b05f9","execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"cell_id":"fa1238b260554e1f8d9ddb3581c8f39b","deepnote_cell_type":"markdown"},"source":"It's worth checking out your quantum programs by viewing them:","block_group":"6479e938697e461b8c63d4c19f6f7f3b"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057216846,"execution_millis":836,"deepnote_to_be_reexecuted":false,"cell_id":"24feec84f3cb48de88f1a2f0ada9206d","deepnote_cell_type":"code"},"source":"show(qprog_stage_1)","block_group":"66b1cc8eef154ae2a9f305e5c951825d","execution_count":null,"outputs":[{"name":"stdout","text":"Opening: https://platform.classiq.io/circuit/dd467f17-8e43-4895-a12e-0335b6ce7239?version=0.36.1\n","output_type":"stream"}]},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057219894,"execution_millis":752,"deepnote_to_be_reexecuted":false,"cell_id":"fc9c842ce20d4c2a90f768e810dd1628","deepnote_cell_type":"code"},"source":"show(qprog_stage_2)","block_group":"5d40d02764b6452e8178de38a7d7c852","execution_count":null,"outputs":[{"name":"stdout","text":"Opening: https://platform.classiq.io/circuit/ac7342fb-76c9-4e7e-b6d3-7e580575f570?version=0.36.1\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"cell_id":"bf4afff5acc3465c9ae257172e321127","deepnote_cell_type":"markdown"},"source":"### Evaluate Your Results","block_group":"0eae89f4339740ccbcbbb47b0deaf56a"},{"cell_type":"markdown","metadata":{"cell_id":"1a8a202df9794939b6899d8563d2ed5c","deepnote_cell_type":"markdown"},"source":"Evaluate the results of stage 1:\n\n**NOTE!** \n- With the `aer_simulator` you can simulate up to 25 qubits circuit\n- With the `nvidia_state_vector_simulator` you can simulate up to 30 qubits circuit, but you can use it up to 10 times, so make sure to use it wisely","block_group":"70b2ee0889314b61aa53b9c61f8a6275"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057225741,"execution_millis":22754,"deepnote_to_be_reexecuted":false,"cell_id":"6f15d021dc694fb995a887e51eadad2d","deepnote_cell_type":"code"},"source":"\ndef evaluate_score_stage_1(results, user_input_precision):\n\n    # The array of values the code is compared against \n    calculated_precision = 10\n    domain = np.arange(0, 1, 1/ 2**calculated_precision)\n    expected_y = np.tanh(domain)\n    \n    # Sort the results according to the values of x\n    parsed_counts = sorted(results[0].value.parsed_counts, key=lambda x: x.state[\"x\"])\n    for tuple in parsed_counts: print(tuple.state)\n\n    # Form the relevant dictionary with values of 'x' and 'y'\n    results_dict = {s.state['x']: s.state['y'] for s in parsed_counts}\n\n    # Verify all strings were sampled, also no superpositions\n    assert len(results_dict) == 2 ** user_input_precision \n\n    # Comparing to the users results\n    measured_y = []\n    for x_val in domain:\n        # The y value that is assesd at x_val is the y value of the first x the user has such that x<x_val\n        x_val_floored = int(x_val * (2**user_input_precision)) / (2**user_input_precision)\n        measured_y.append(results_dict[x_val_floored])\n\n    # The metric asseses what's the largest distance between the expected value and the measured value, the smallest the better\n    max_distance = np.max(np.abs(expected_y - np.array(measured_y)))\n    return max_distance\n\ndef print_depth_width(quantum_program):\n    generated_circuit = GeneratedCircuit.parse_obj(json.loads(quantum_program))\n    print(f\"Synthesized circuit width: {generated_circuit.data.width}, depth: {generated_circuit.transpiled_circuit.depth}\")\n\n# Execution parameters\nnum_shots = 1000\nbackend_name = \"nvidia_state_vector_simulator\" # \"aer_simulator\", \"nvidia_state_vector_simulator\"\n\nqmod_stage_1 = set_execution_preferences(qmod_stage_1, \n                                        ExecutionPreferences(\n                                            num_shots=1000,  # The number of shots is a hyperparameter of the algorithm\n                                            backend_preferences=ClassiqBackendPreferences(\n                                                                    backend_name=backend_name))) #aer_simulator / nvidia_state_vector_simulator\nprint('Synthesizing quantum program stage 1 with the execution preferences')\nqprog_stage_1 = synthesize(qmod_stage_1)\nprint_depth_width(qprog_stage_1)\nprint('Executing the quantum program')\nresults = execute(qprog_stage_1).result()\nprint(\"Results of stage 1 \\n Max distance = \", evaluate_score_stage_1(results, precision_value_stage_1))","block_group":"f7dee723f0a041c2a737fe3dd197da55","execution_count":null,"outputs":[{"name":"stdout","text":"Synthesizing quantum program stage 1 with the execution preferences\nSynthesized circuit width: 28, depth: 413\nExecuting the quantum program\n{'x': 0.0, 'y': 0.04607582092285156}\n{'x': 0.0625, 'y': 0.09448152780532837}\n{'x': 0.125, 'y': 0.14288723468780518}\n{'x': 0.1875, 'y': 0.19129294157028198}\n{'x': 0.25, 'y': 0.2396986484527588}\n{'x': 0.3125, 'y': 0.2881043553352356}\n{'x': 0.375, 'y': 0.3365100622177124}\n{'x': 0.4375, 'y': 0.3849157691001892}\n{'x': 0.5, 'y': 0.433321475982666}\n{'x': 0.5625, 'y': 0.4817271828651428}\n{'x': 0.625, 'y': 0.5301328897476196}\n{'x': 0.6875, 'y': 0.5785385966300964}\n{'x': 0.75, 'y': 0.6269443035125732}\n{'x': 0.8125, 'y': 0.67535001039505}\n{'x': 0.875, 'y': 0.7237557172775269}\n{'x': 0.9375, 'y': 0.7721614241600037}\nResults of stage 1 \n Max distance =  0.07643302628867621\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"cell_id":"78c29bed60ac4657b959c644e34f4add","deepnote_cell_type":"markdown"},"source":"Evluate the results of stage 2:","block_group":"2e9fcf3a20624cb89606fc9d0bbc3523"},{"cell_type":"code","metadata":{"source_hash":null,"execution_start":1707057596086,"execution_millis":185,"deepnote_to_be_reexecuted":false,"cell_id":"0e6d03a63b0641e0b129eef5f6b55696","deepnote_cell_type":"code"},"source":"generated_circuit = GeneratedCircuit.parse_obj(json.loads(qprog_stage_2))\nprint_depth_width(qprog_stage_2)\nprint(f'\\nResults of stage 2 \\nPrecision = {precision_value_stage_2}, Circuit depth = {generated_circuit.transpiled_circuit.depth}')","block_group":"aa488bcceb5f4fe8bd5b1c1a6015dbd5","execution_count":null,"outputs":[{"name":"stdout","text":"Synthesized circuit width: 100, depth: 864\n\nResults of stage 2 \nPrecision = 40, Circuit depth = 864\n","output_type":"stream"}]},{"cell_type":"markdown","metadata":{"cell_id":"717e2e4e7b4e4d65a2aa7b9e5d3d438e","deepnote_cell_type":"markdown"},"source":"## Cheat Sheet","block_group":"4a6053ec5e324f8bbb9b044a6ceb2ed0"},{"cell_type":"markdown","metadata":{"cell_id":"d25a93709d734598848736f8936e96d6","deepnote_cell_type":"markdown"},"source":"","block_group":"b9f43a5a29ce455c963d5efdf6c06c33"},{"cell_type":"markdown","source":"<a style='text-decoration:none;line-height:16px;display:flex;color:#5B5B62;padding:10px;justify-content:end;' href='https://deepnote.com?utm_source=created-in-deepnote-cell&projectId=39083a83-3a31-4a85-9b9a-37c227ce02c4' target=\"_blank\">\n<img alt='Created in deepnote.com' style='display:inline;max-height:16px;margin:0px;margin-right:7.5px;' src='data:image/svg+xml;base64,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' > </img>\nCreated in <span style='font-weight:600;margin-left:4px;'>Deepnote</span></a>","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"}}],"nbformat":4,"nbformat_minor":0,"metadata":{"deepnote_notebook_id":"845f6b1ab4594885a586693c6ab8d8f7","deepnote_execution_queue":[]}}