Modern Computer Architecture and Organization

Modern Computer Architecture and Organization, by Jim Ledin. Published by Packt Publishing.


Chapter 14, Exercise 3

Work through the example quantum program at https://qiskit.org/documentation/tutorials/fundamentals/1_getting_started_with_qiskit.html. This example creates a quantum circuit containing three qubits that implements a Greenberger–Horne–Zeilinger (GHZ) state. The GHZ state exhibits key properties of quantum entanglement. Execute the code in a simulation environment on your computer.

Answer

  1. Start an Anaconda prompt console. Type anaconda in the Windows search box and click on Anaconda prompt when it appears in the search list. A console window will appear.

  2. Enter the qiskitenv environment with this command: 
    conda activate qiskitenv
  3. Enter the following commands at the Anaconda prompt: 
    python
import numpy as np
from qiskit import *
  4. Create a quantum circuit containing a three-qubit GHZ state and add measurements for each qubit: ``` circ = QuantumCircuit(3)

    Add an H gate to qubit 0, creating superposition

    circ.h(0)

    Add a CX (CNOT) gate. Qubit 0 is control and qubit 1 is target

    circ.cx(0,1)

    Add a CX (CNOT) gate. Qubit 0 is control and qubit 2 is target

    circ.cx(0,2)

Add a measurement to each of the qubits

meas = QuantumCircuit(3, 3) meas.barrier(range(3)) meas.measure(range(3),range(3))

Combine the two circuits

qc = circ + meas


5. Display the circuit onscreen:

qc.draw()


 The output of this command should appear as follows:

qc.draw() ┌───┐ ░ ┌─┐ q_0: |0>┤ H ├──■────■───░─┤M├────── └───┘┌─┴─┐ │ ░ └╥┘┌─┐ q_1: |0>─────┤ X ├──┼───░──╫─┤M├─── └───┘┌─┴─┐ ░ ║ └╥┘┌─┐ q_2: |0>──────────┤ X ├─░──╫──╫─┤M├ └───┘ ░ ║ ║ └╥┘ c_0: 0 ═══════════════════╩══╬══╬═ ║ ║ c_1: 0 ══════════════════════╩══╬═ ║ c_2: 0 ═════════════════════════╩═

```

  1. Run the circuit on your computer using the qasm_simulator simulator. The shots parameter provides a count of the number of times the circuit will be executed to collect statistical results: ``` backend_sim = Aer.get_backend(‘qasm_simulator’) job_sim = execute(qc, backend_sim, shots=1024)

7. Retrieve and display the count of the number of times each bit pattern resulted from a simulation run:

result_sim = job_sim.result() counts_sim = result_sim.get_counts(qc) counts_sim


You should see results similar (but not identical) to these:

counts_sim {‘000’: 527, ‘111’: 497}

```

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