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"tiny_ansatz_1", "##characteristics#run#vqe_input#ansatz_method#repeats": 10, "##characteristics#run#vqe_input#ansatz_src#all": [ "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n" ], "##characteristics#run#vqe_input#ansatz_src#all_unique": [ "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n" ], "##characteristics#run#vqe_input#ansatz_src#min": "def tiny_ansatz_1(current_params):\n q = qiskit.QuantumRegister(2, \"q\")\n qc = qiskit.QuantumCircuit(q, qiskit.ClassicalRegister(2, \"c\"))\n\n qc.x(q[0])\n qc.x(q[1])\n qc.rx(-np.pi/2, q[0])\n qc.ry( np.pi/2, q[1])\n qc.cx(q[0], q[1])\n qc.rz(current_params[0], q[1])\n qc.cx(q[0], q[1])\n qc.rx( np.pi/2, q[0])\n qc.ry(-np.pi/2, q[1])\n\n return qc\n", "##characteristics#run#vqe_input#ansatz_src#repeats": 10, "##characteristics#run#vqe_input#classical_energy#all": [ -1.8572746704950887, -1.8572746704950887, -1.8572746704950887, -1.8572746704950887, -1.8572746704950887, -1.8572746704950887, -1.8572746704950887, -1.8572746704950887, -1.8572746704950887, -1.8572746704950887 ], "##characteristics#run#vqe_input#classical_energy#all_unique": [ -1.8572746704950887 ], "##characteristics#run#vqe_input#classical_energy#center": -1.8572746704950887, "##characteristics#run#vqe_input#classical_energy#halfrange": 0.0, "##characteristics#run#vqe_input#classical_energy#max": -1.8572746704950887, "##characteristics#run#vqe_input#classical_energy#mean": -1.8572746704950887, "##characteristics#run#vqe_input#classical_energy#min": -1.8572746704950887, "##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": 10, "##characteristics#run#vqe_input#minimizer_method#all": [ "my_cobyla", "my_cobyla", "my_cobyla", "my_cobyla", "my_cobyla", "my_cobyla", "my_cobyla", "my_cobyla", "my_cobyla", "my_cobyla" ], "##characteristics#run#vqe_input#minimizer_method#all_unique": [ "my_cobyla" ], "##characteristics#run#vqe_input#minimizer_method#min": "my_cobyla", "##characteristics#run#vqe_input#minimizer_method#repeats": 10, "##characteristics#run#vqe_input#minimizer_options#fatol#all": [ 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001 ], "##characteristics#run#vqe_input#minimizer_options#fatol#all_unique": [ 0.001 ], "##characteristics#run#vqe_input#minimizer_options#fatol#center": 0.001, "##characteristics#run#vqe_input#minimizer_options#fatol#halfrange": 0.0, "##characteristics#run#vqe_input#minimizer_options#fatol#max": 0.001, "##characteristics#run#vqe_input#minimizer_options#fatol#mean": 0.0010000000000000002, "##characteristics#run#vqe_input#minimizer_options#fatol#min": 0.001, "##characteristics#run#vqe_input#minimizer_options#fatol#range": 0.0, "##characteristics#run#vqe_input#minimizer_options#fatol#range_percent": 0.0, "##characteristics#run#vqe_input#minimizer_options#fatol#repeats": 10, "##characteristics#run#vqe_input#minimizer_options#maxfev#all": [ 200, 200, 200, 200, 200, 200, 200, 200, 200, 200 ], "##characteristics#run#vqe_input#minimizer_options#maxfev#all_unique": [ 200 ], "##characteristics#run#vqe_input#minimizer_options#maxfev#center": 200.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#halfrange": 0.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#max": 200, "##characteristics#run#vqe_input#minimizer_options#maxfev#mean": 200.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#min": 200, "##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": 10, "##characteristics#run#vqe_input#minimizer_options#maxiter#all": [ 80, 80, 80, 80, 80, 80, 80, 80, 80, 80 ], "##characteristics#run#vqe_input#minimizer_options#maxiter#all_unique": [ 80 ], "##characteristics#run#vqe_input#minimizer_options#maxiter#center": 80.0, "##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": 10, "##characteristics#run#vqe_input#minimizer_options#xatol#all": [ 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001 ], "##characteristics#run#vqe_input#minimizer_options#xatol#all_unique": [ 0.001 ], "##characteristics#run#vqe_input#minimizer_options#xatol#center": 0.001, "##characteristics#run#vqe_input#minimizer_options#xatol#halfrange": 0.0, "##characteristics#run#vqe_input#minimizer_options#xatol#max": 0.001, "##characteristics#run#vqe_input#minimizer_options#xatol#mean": 0.0010000000000000002, "##characteristics#run#vqe_input#minimizer_options#xatol#min": 0.001, "##characteristics#run#vqe_input#minimizer_options#xatol#range": 0.0, "##characteristics#run#vqe_input#minimizer_options#xatol#range_percent": 0.0, "##characteristics#run#vqe_input#minimizer_options#xatol#repeats": 10, "##characteristics#run#vqe_input#minimizer_src#all": [ "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "def my_cobyla( func, x0, my_args=(), my_options=None ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.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 ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.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 ):\n \"Stock COBYLA optimizer from SciPy library\"\n\n my_options = {\n 'rhobeg':0.2\n }\n return scipy.optimize.minimize( func, x0, method='COBYLA', options=my_options, args=my_args )\n", "##characteristics#run#vqe_input#minimizer_src#repeats": 10, "##characteristics#run#vqe_input#q_device_name#all": [ "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator", "local_qasm_simulator" ], "##characteristics#run#vqe_input#q_device_name#all_unique": [ "local_qasm_simulator" ], "##characteristics#run#vqe_input#q_device_name#min": "local_qasm_simulator", "##characteristics#run#vqe_input#q_device_name#repeats": 10, "##characteristics#run#vqe_input#sample_number#all": [ 100, 100, 100, 100, 100, 100, 100, 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#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": 10, "##characteristics#run#vqe_output#fun#all": [ -1.8572363546480268, -1.8275772039471399, -1.8152557542695011, -1.86229790249913, -1.8796809000278718, -1.1242565757418765, -1.8630306871576074, -1.6749300389973334, -1.86229790249913, -1.8710007253898742 ], "##characteristics#run#vqe_output#fun#all_unique": [ -1.8572363546480268, -1.8275772039471399, -1.8152557542695011, -1.86229790249913, -1.8796809000278718, -1.1242565757418765, 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