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    "reduced_HW_efficient_ansatz",
    "reduced_HW_efficient_ansatz",
    "reduced_HW_efficient_ansatz",
    "reduced_HW_efficient_ansatz",
    "reduced_HW_efficient_ansatz",
    "reduced_HW_efficient_ansatz",
    "reduced_HW_efficient_ansatz"
  ],
  "##characteristics#run#vqe_input#ansatz_method#all_unique": [
    "reduced_HW_efficient_ansatz"
  ],
  "##characteristics#run#vqe_input#ansatz_method#min": "reduced_HW_efficient_ansatz",
  "##characteristics#run#vqe_input#ansatz_method#repeats": 10,
  "##characteristics#run#vqe_input#ansatz_src#all": [
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n",
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n"
  ],
  "##characteristics#run#vqe_input#ansatz_src#all_unique": [
    "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    return qc\n"
  ],
  "##characteristics#run#vqe_input#ansatz_src#min": "def reduced_HW_efficient_ansatz(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.rz(current_params[0], q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], q[1])\n\n    qc.rz(current_params[1], q[0])\n    qc.rz(current_params[2], q[1])\n    qc.y(q[0])\n    qc.y(q[1])\n\n    # no CNOT, works without it.\n\n    qc.rx(current_params[3], q[0])\n    # qc.rz(current_params[4], q[1])\n    qc.z(q[0])\n    qc.y(q[1])\n\n    qc.cx(q[0], 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,
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  "##characteristics#run#vqe_input#classical_energy#all_unique": [
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  "##characteristics#run#vqe_input#classical_energy#exp_allx": [
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  "##characteristics#run#vqe_input#classical_energy#exp_ally": [
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  "##characteristics#run#vqe_input#classical_energy#exp_warning": "no",
  "##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": [
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  "##characteristics#run#vqe_input#minimizer_method#min": "my_cobyla",
  "##characteristics#run#vqe_input#minimizer_method#repeats": 10,
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  "##characteristics#run#vqe_input#minimizer_options#fatol#mean": 0.0010000000000000002,
  "##characteristics#run#vqe_input#minimizer_options#fatol#min": 0.001,
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  "##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,
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  "##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    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    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    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    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    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    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    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    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    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    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    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    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": [
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    "local_qasm_simulator",
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  "##characteristics#run#vqe_input#q_device_name#min": "local_qasm_simulator",
  "##characteristics#run#vqe_input#q_device_name#repeats": 10,
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