!pip install qiskit !pip install ipywidgets

token='**********************' from qiskit import QuantumCircuit, execute, Aer, IBMQ IBMQ.save_account(token) from qiskit.compiler import transpile, assemble from qiskit.tools.jupyter import * from qiskit.visualization import * provider = IBMQ.load_account()

# Copy and paste the circut code from the IBM composer below

# Load IBM Q account and connect to quantum computer "armonk" # you should check which computer is the least busy and change the backend backend = provider.backends.ibmq_armonk shots = 1024 results = execute(circuit, backend=backend, shots=shots).result() answer = results.get_counts() plot_histogram(answer)

Angles=[,] Spin_Up=[,] Spin_Down=[,]

# This code block will produce a scatter plot of your experimental data import matplotlib.pyplot as plt %matplotlib inline %config InlineBackend.figure_format='retina' plt.scatter(Angles,Spin_Up,marker='o',label='Measured Spin Up') plt.scatter(Angles,Spin_Down, marker='^',label='Measured Spin Down') plt.xlabel('Rotation Angle') plt.ylabel('Probability') plt.xlim(0,pi) plt.ylim(0,1) plt.title('Stern-Gerlach Experimental Outcomes') plt.legend();

# This code block will produce a plot that compares your experimental data to the theortical values plt.scatter(Angles,Spin_Up,marker='o',label='Measured Spin Up') plt.scatter(Angles,Spin_Down, marker='^',label='Measured Spin Down') import numpy as np t=np.linspace(0,pi,100) tup=np.cos(t/2)**2 tdown=np.sin(t/2)**2 plt.plot(t,tup,color='b',label='Theoretical Spin Up') plt.plot(t,tdown,color='orange',label='Theoretical Spin Down') plt.xlabel('Rotation Angle') plt.ylabel('Probability') plt.xlim(0,pi) plt.ylim(0,1) plt.title('Stern-Gerlach Experimental Outcomes') plt.legend();