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"##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, "##characteristics#run#vqe_input#sample_number#max": 100, "##characteristics#run#vqe_input#sample_number#mean": 100.0, "##characteristics#run#vqe_input#sample_number#min": 100, "##characteristics#run#vqe_input#sample_number#range": 0, "##characteristics#run#vqe_input#sample_number#range_percent": 0.0, "##characteristics#run#vqe_input#sample_number#repeats": 3, "##characteristics#run#vqe_output#fun#all": [ -2.8077839575399746, -2.8077839575399746, -2.8077839575399746 ], "##characteristics#run#vqe_output#fun#all_unique": [ -2.8077839575399746 ], "##characteristics#run#vqe_output#fun#center": -2.8077839575399746, "##characteristics#run#vqe_output#fun#exp": -2.8077839575399746, "##characteristics#run#vqe_output#fun#exp_allx": [ -2.8077839575399746 ], "##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": 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"##characteristics#run#vqe_output#fun_validated#max": -2.7797061179645746, "##characteristics#run#vqe_output#fun_validated#mean": -2.798424677681508, "##characteristics#run#vqe_output#fun_validated#min": -2.8077839575399746, "##characteristics#run#vqe_output#fun_validated#range": 0.0280778395754, "##characteristics#run#vqe_output#fun_validated#range_percent": -0.01000000000000009, "##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, 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