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"##characteristics#run#vqe_input#classical_energy#halfrange": 0.0, "##characteristics#run#vqe_input#classical_energy#max": -2.8077839575399746, "##characteristics#run#vqe_input#classical_energy#mean": -2.8077839575399746, "##characteristics#run#vqe_input#classical_energy#min": -2.8077839575399746, "##characteristics#run#vqe_input#classical_energy#range": 0.0, "##characteristics#run#vqe_input#classical_energy#range_percent": -0.0, "##characteristics#run#vqe_input#classical_energy#repeats": 1, "##characteristics#run#vqe_input#minimizer_method#all": [ "my_minimizer" ], "##characteristics#run#vqe_input#minimizer_method#all_unique": [ "my_minimizer" ], "##characteristics#run#vqe_input#minimizer_method#min": "my_minimizer", "##characteristics#run#vqe_input#minimizer_method#repeats": 1, "##characteristics#run#vqe_input#minimizer_options#maxfev#all": [ 1 ], "##characteristics#run#vqe_input#minimizer_options#maxfev#all_unique": [ 1 ], "##characteristics#run#vqe_input#minimizer_options#maxfev#center": 1.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#exp": 1.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#exp_allx": [ 1.0 ], "##characteristics#run#vqe_input#minimizer_options#maxfev#exp_ally": [ 100.0 ], "##characteristics#run#vqe_input#minimizer_options#maxfev#exp_warning": "no", "##characteristics#run#vqe_input#minimizer_options#maxfev#halfrange": 0.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#max": 1, "##characteristics#run#vqe_input#minimizer_options#maxfev#mean": 1.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#min": 1, "##characteristics#run#vqe_input#minimizer_options#maxfev#range": 0, "##characteristics#run#vqe_input#minimizer_options#maxfev#range_percent": 0.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#repeats": 1, "##characteristics#run#vqe_input#minimizer_src#all": [ "def my_minimizer( func, x0, my_args=(), my_options=None ):\n \"Your own attempt at writing a stochastic minimizer\"\n \n print(\"x0 is\", x0)\n print(\"func is\", inspect.getsource(func))\n para_func = mapf(func)\n def wp(*test):\n args = test[0][0]\n print(\"invoking with\", args)\n ret = func(args, my_args)\n print(\"evaluated as\", ret)\n return -ret\n \n myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n acquisition_type = 'EI',\n initial_design_numdata = 3,\n evaluator_type = 'local_penalization',\n num_cores=1, batch_size=1)\n myProblem.run_optimization(10)\n current_func_value = func(myProblem.x_opt, my_args)\n minimizer_output = { 'fun' : current_func_value, 'nfev' : 15, 'nit' : 1, 'x' : myProblem.x_opt }\n return minimizer_output\n" ], "##characteristics#run#vqe_input#minimizer_src#all_unique": [ "def my_minimizer( func, x0, my_args=(), my_options=None ):\n \"Your own attempt at writing a stochastic minimizer\"\n \n print(\"x0 is\", x0)\n print(\"func is\", inspect.getsource(func))\n para_func = mapf(func)\n def wp(*test):\n args = test[0][0]\n print(\"invoking with\", args)\n ret = func(args, my_args)\n print(\"evaluated as\", ret)\n return -ret\n \n myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n acquisition_type = 'EI',\n initial_design_numdata = 3,\n evaluator_type = 'local_penalization',\n num_cores=1, batch_size=1)\n myProblem.run_optimization(10)\n current_func_value = func(myProblem.x_opt, my_args)\n minimizer_output = { 'fun' : current_func_value, 'nfev' : 15, 'nit' : 1, 'x' : myProblem.x_opt }\n return minimizer_output\n" ], "##characteristics#run#vqe_input#minimizer_src#min": "def my_minimizer( func, x0, my_args=(), my_options=None ):\n \"Your own attempt at writing a stochastic minimizer\"\n \n print(\"x0 is\", x0)\n print(\"func is\", inspect.getsource(func))\n para_func = mapf(func)\n def wp(*test):\n args = test[0][0]\n print(\"invoking with\", args)\n ret = func(args, my_args)\n print(\"evaluated as\", ret)\n return -ret\n \n myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n acquisition_type = 'EI',\n initial_design_numdata = 3,\n evaluator_type = 'local_penalization',\n num_cores=1, batch_size=1)\n myProblem.run_optimization(10)\n current_func_value = func(myProblem.x_opt, my_args)\n minimizer_output = { 'fun' : current_func_value, 'nfev' : 15, 'nit' : 1, 'x' : myProblem.x_opt }\n return minimizer_output\n", "##characteristics#run#vqe_input#minimizer_src#repeats": 1, "##characteristics#run#vqe_input#q_device_name#all": [ "QVM" ], "##characteristics#run#vqe_input#q_device_name#all_unique": [ "QVM" ], "##characteristics#run#vqe_input#q_device_name#min": "QVM", "##characteristics#run#vqe_input#q_device_name#repeats": 1, "##characteristics#run#vqe_input#sample_number#all": [ 1 ], "##characteristics#run#vqe_input#sample_number#all_unique": [ 1 ], "##characteristics#run#vqe_input#sample_number#center": 1.0, "##characteristics#run#vqe_input#sample_number#exp": 1.0, 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"##characteristics#run#vqe_output#fun#exp_ally": [ 100.0 ], "##characteristics#run#vqe_output#fun#exp_warning": "no", "##characteristics#run#vqe_output#fun#halfrange": 0.0, "##characteristics#run#vqe_output#fun#max": -2.8077839575399746, "##characteristics#run#vqe_output#fun#mean": -2.8077839575399746, "##characteristics#run#vqe_output#fun#min": -2.8077839575399746, "##characteristics#run#vqe_output#fun#range": 0.0, "##characteristics#run#vqe_output#fun#range_percent": -0.0, "##characteristics#run#vqe_output#fun#repeats": 1, "##characteristics#run#vqe_output#fun_exact#all": [ -1.4206585269108933 ], "##characteristics#run#vqe_output#fun_exact#all_unique": [ -1.4206585269108933 ], "##characteristics#run#vqe_output#fun_exact#center": -1.4206585269108933, "##characteristics#run#vqe_output#fun_exact#exp": -1.4206585269108933, "##characteristics#run#vqe_output#fun_exact#exp_allx": [ -1.4206585269108933 ], "##characteristics#run#vqe_output#fun_exact#exp_ally": [ 100.0 ], "##characteristics#run#vqe_output#fun_exact#exp_warning": "no", "##characteristics#run#vqe_output#fun_exact#halfrange": 0.0, "##characteristics#run#vqe_output#fun_exact#max": -1.4206585269108933, "##characteristics#run#vqe_output#fun_exact#mean": -1.4206585269108933, "##characteristics#run#vqe_output#fun_exact#min": -1.4206585269108933, "##characteristics#run#vqe_output#fun_exact#range": 0.0, "##characteristics#run#vqe_output#fun_exact#range_percent": -0.0, "##characteristics#run#vqe_output#fun_exact#repeats": 1, "##characteristics#run#vqe_output#fun_validated#all": [ -2.8077839575399746 ], "##characteristics#run#vqe_output#fun_validated#all_unique": [ -2.8077839575399746 ], "##characteristics#run#vqe_output#fun_validated#center": -2.8077839575399746, "##characteristics#run#vqe_output#fun_validated#exp": -2.8077839575399746, "##characteristics#run#vqe_output#fun_validated#exp_allx": [ -2.8077839575399746 ], "##characteristics#run#vqe_output#fun_validated#exp_ally": [ 100.0 ], 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