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"##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 current_func_value = func(x0, my_args)\n x=x0\n T = 1.0\n T_min = 0.1\n alpha = 0.5\n while T > T_min:\n i = 1\n while i <= 2:\n x_trial = neighbourhood(x)\n new_func_value = func(x, my_args)\n ap = boltzmann(new_func_value, current_func_value, T)\n if ap < random.random():\n x = x_trial\n current_func_value = new_func_value\n i += 1\n T = T*alpha\n minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x }\n\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 current_func_value = func(x0, my_args)\n x=x0\n T = 1.0\n T_min = 0.1\n alpha = 0.5\n while T > T_min:\n i = 1\n while i <= 2:\n x_trial = neighbourhood(x)\n new_func_value = func(x, my_args)\n ap = boltzmann(new_func_value, current_func_value, T)\n if ap < random.random():\n x = x_trial\n current_func_value = new_func_value\n i += 1\n T = T*alpha\n minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x }\n\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 current_func_value = func(x0, my_args)\n x=x0\n T = 1.0\n T_min = 0.1\n alpha = 0.5\n while T > T_min:\n i = 1\n while i <= 2:\n x_trial = neighbourhood(x)\n new_func_value = func(x, my_args)\n ap = boltzmann(new_func_value, current_func_value, T)\n if ap < random.random():\n x = x_trial\n current_func_value = new_func_value\n i += 1\n T = T*alpha\n minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x }\n\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": [ 100 ], "##characteristics#run#vqe_input#sample_number#all_unique": [ 100 ], "##characteristics#run#vqe_input#sample_number#center": 100.0, 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"##characteristics#run#vqe_output#fun_validated#exp_ally": [ 100.0 ], "##characteristics#run#vqe_output#fun_validated#exp_warning": "no", "##characteristics#run#vqe_output#fun_validated#halfrange": 0.0, "##characteristics#run#vqe_output#fun_validated#max": -0.7440627487480931, "##characteristics#run#vqe_output#fun_validated#mean": -0.7440627487480931, "##characteristics#run#vqe_output#fun_validated#min": -0.7440627487480931, "##characteristics#run#vqe_output#fun_validated#range": 0.0, "##characteristics#run#vqe_output#fun_validated#range_percent": -0.0, "##characteristics#run#vqe_output#fun_validated#repeats": 1, "##characteristics#run#vqe_output#nfev#all": [ 1 ], "##characteristics#run#vqe_output#nfev#all_unique": [ 1 ], "##characteristics#run#vqe_output#nfev#center": 1.0, "##characteristics#run#vqe_output#nfev#exp": 1.0, "##characteristics#run#vqe_output#nfev#exp_allx": [ 1.0 ], "##characteristics#run#vqe_output#nfev#exp_ally": [ 100.0 ], 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