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"##characteristics#run#vqe_input#minimizer_options#maxiter#exp_allx": [ 80.0 ], "##characteristics#run#vqe_input#minimizer_options#maxiter#exp_ally": [ 100.0 ], "##characteristics#run#vqe_input#minimizer_options#maxiter#exp_warning": "no", "##characteristics#run#vqe_input#minimizer_options#maxiter#halfrange": 0.0, "##characteristics#run#vqe_input#minimizer_options#maxiter#max": 80, "##characteristics#run#vqe_input#minimizer_options#maxiter#mean": 80.0, "##characteristics#run#vqe_input#minimizer_options#maxiter#min": 80, "##characteristics#run#vqe_input#minimizer_options#maxiter#range": 0, "##characteristics#run#vqe_input#minimizer_options#maxiter#range_percent": 0.0, "##characteristics#run#vqe_input#minimizer_options#maxiter#repeats": 3, "##characteristics#run#vqe_input#minimizer_src#all": [ "def my_cobyla( func, x0, my_args=(), my_options=None ): # another non-stochastic minimizer for comparison\n \"COBYLA optimizer from SciPy library\"\n\n return minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ): # another non-stochastic minimizer for comparison\n \"COBYLA optimizer from SciPy library\"\n\n return minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ): # another non-stochastic minimizer for comparison\n \"COBYLA optimizer from SciPy library\"\n\n return minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n" ], "##characteristics#run#vqe_input#minimizer_src#all_unique": [ "def my_cobyla( func, x0, my_args=(), my_options=None ): # another non-stochastic minimizer for comparison\n \"COBYLA optimizer from SciPy library\"\n\n return minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n" ], "##characteristics#run#vqe_input#minimizer_src#min": "def my_cobyla( func, x0, my_args=(), my_options=None ): # another non-stochastic minimizer for comparison\n \"COBYLA optimizer from SciPy library\"\n\n return minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "##characteristics#run#vqe_input#minimizer_src#repeats": 3, "##characteristics#run#vqe_input#q_device_name#all": [ "QVM", "QVM", "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": 3, "##characteristics#run#vqe_input#sample_number#all": [ 100, 100, 100 ], "##characteristics#run#vqe_input#sample_number#all_unique": [ 100 ], "##characteristics#run#vqe_input#sample_number#center": 100.0, "##characteristics#run#vqe_input#sample_number#exp": 100.0, "##characteristics#run#vqe_input#sample_number#exp_allx": [ 100.0 ], "##characteristics#run#vqe_input#sample_number#exp_ally": [ 100.0 ], "##characteristics#run#vqe_input#sample_number#exp_warning": "no", "##characteristics#run#vqe_input#sample_number#halfrange": 0.0, 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"##characteristics#run#vqe_output#fun_exact#max": -2.7996166235142947, "##characteristics#run#vqe_output#fun_exact#mean": -2.805056554866585, "##characteristics#run#vqe_output#fun_exact#min": -2.8077765698779578, "##characteristics#run#vqe_output#fun_exact#range": 0.008159946363663018, "##characteristics#run#vqe_output#fun_exact#range_percent": -0.0029061950481400647, "##characteristics#run#vqe_output#fun_exact#repeats": 3, "##characteristics#run#vqe_output#fun_validated#all": [ -2.8077839575399746, -2.8077839575399746, -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 ], "##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": -2.8077839575399746, "##characteristics#run#vqe_output#fun_validated#mean": -2.8077839575399746, "##characteristics#run#vqe_output#fun_validated#min": -2.8077839575399746, "##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": 3, "##characteristics#run#vqe_output#maxcv#all": [ 0.0, 0.0, 0.0 ], "##characteristics#run#vqe_output#maxcv#all_unique": [ 0.0 ], "##characteristics#run#vqe_output#maxcv#center": 0.0, "##characteristics#run#vqe_output#maxcv#exp": 0.0, "##characteristics#run#vqe_output#maxcv#exp_allx": [ 0.0 ], "##characteristics#run#vqe_output#maxcv#exp_ally": [ 100.0 ], "##characteristics#run#vqe_output#maxcv#exp_warning": "no", "##characteristics#run#vqe_output#maxcv#halfrange": 0.0, "##characteristics#run#vqe_output#maxcv#max": 0.0, "##characteristics#run#vqe_output#maxcv#mean": 0.0, "##characteristics#run#vqe_output#maxcv#min": 0.0, "##characteristics#run#vqe_output#maxcv#range": 0.0, "##characteristics#run#vqe_output#maxcv#repeats": 3, "##characteristics#run#vqe_output#message#all": [ "Optimization terminated successfully.", "Optimization terminated successfully.", "Optimization terminated successfully." ], "##characteristics#run#vqe_output#message#all_unique": [ "Optimization terminated successfully." ], "##characteristics#run#vqe_output#message#min": "Optimization terminated successfully.", "##characteristics#run#vqe_output#message#repeats": 3, "##characteristics#run#vqe_output#nfev#all": [ 19, 22, 19 ], "##characteristics#run#vqe_output#nfev#all_unique": [ 19, 22 ], "##characteristics#run#vqe_output#nfev#center": 20.5, "##characteristics#run#vqe_output#nfev#exp": 19.939393939393938, 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