rish-16

import qiskit import qk

'''
When you run the script once, you do not need to add
your API key again as your account has already been
saved in memory
'''
qk.IBMQ.save_account('YOUR_API_KEY')
qk.IBMQ.load_accounts()

rish-16

# Using Qiskit Aer's Qasm Simulator
simulator = qk.BasicAer.get_backend('qasm_simulator')

# Simulating the circuit using the simulator to get the result
job = qk.execute(circuit, simulator)
result = job.result()

# Getting the aggregated binary outcomes of the circuit.
counts = result.get_counts(circuit)
print (counts)

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print (circuit)

rish-16

# Hadamard Gate on the first Qubit
circuit.h(q[0])
# CNOT Gate on the first and second Qubits
circuit.cx(q[0], q[1])
# Measuring the Qubits
circuit.measure(q, c)

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circuit = qk.QuantumCircuit(q, c)

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import qiskit as qk

# Creating Qubits
q = qk.QuantumRegister(2)
# Creating Classical Bits
c = qk.ClassicalRegister(2)

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import qiskit as qk

# defining our Qubits and Bits
q = qk.QuantumRegister(2)
c = qk.ClassicalRegister(2)

# Creating a circuit with our Qubits and Bits
circuit = qk.QuantumCircuit(q, c)

# Adding Gates

rish-16

# The main RL event loop
for i in range(num_episodes):
    S0 = env.reset() # Current state
    total_reward_for_episode = 0
    game_done = False

    for s in range(steps_per_episode):
        # Choosing an action to perform in the current state
        if np.random.rand() > epsilon:
            action = env.action_space.sample() # Explore with a random action

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rewards_lookup = {
	'empty': -1,
	'cheese': 5,
	'mousetrap': -10,
	'start': 0,
	'end': 0
}

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discount_factor_gamma = 0.9
num_episodes = 1000
learning_rate_alpha = 0.8
epsilon_threshold = 1.0