Internal:Quantum Information Science (QIS General)

QIS General

I will use this page to keep a general progression and categorization about QIS ranging from theory to useful code.

Notebooks

QIS General

Custom built QIS notebooks (some modeled after Nielsen's lectures) can be found here[1].

Code

Qiskit

GPU

Calling a GPU to execute quantum circuits is relatively straight forward. First, choose your backend. This can be ideal, noisy, or a snapshot from a real qiskit quantum device. Once you have your simulator selected, use the *set_options* method to set the device:

try:
    simulator_gpu = Aer.get_backend('aer_simulator')
    simulator_gpu.set_options(device='GPU')
except AerError as e:
    print('Err:\n',e)

When running the script on Wahab, a GPU must be allocated for this. The .sh file should look something like this:

#!/bin/bash

#SBATCH -p gpu
#SBATCH --gres gpu:1

enable_lmod
module load container_env python3

crun -p ~/envs/python3.10 python3 -u ./main.py

Matplotlib

Inset Plot

creating an inset plot requires defining inset axes within a pre defined larger plot. For example, if we create a regular lineplot:

fig, ax = plt.subplots()
plt.plot(
    qubits,
    qc_cx_3,
    "-o",
    label="n+1 optimized",
    linewidth=2,
    color="grey",
    markeredgecolor="k"
)
plt.plot(
    qubits,
    qc_cx_op_3,
    "-o",
    label="2n optimized",
    linewidth=2,
    color="mediumpurple",
    markeredgecolor="k"
)
plt.ylabel("CX Count")
plt.xlabel("n Qubits")
plt.xticks(qubits)
plt.legend(framealpha=1)
plt.grid()
plt.title("QHED Number of CX Operations")
plt.savefig("test.png")
plt.show()

Import the following the modules for inset plots and define them underneath the original plt.plot() methods:

# Original plots must be defined first
fig, ax = plt.subplots()
plt.plot(
    qubits,
    qc_cx_3,
    "-o",
    label="n+1 optimized",
    linewidth=2,
    color="grey",
    markeredgecolor="k"
)
plt.plot(
    qubits,
    qc_cx_op_3,
    "-o",
    label="2n optimized",
    linewidth=2,
    color="mediumpurple",
    markeredgecolor="k"
)


# Define length and height of inset box
# [x_start, y_start, x_len, y_len]
axins = ax.inset_axes([2.8,700, 2, 1000], transform=ax.transData)
axins.plot(
    qubits,
    qc_cx_3,
    "-o",
    label="n+1 optimized",
    linewidth=2,
    color="grey",
    markeredgecolor="k"
)
axins.plot(
    qubits,
    qc_cx_op_3,
    "-o",
    label="2n optimized",
    linewidth=2,
    color="mediumpurple",
    markeredgecolor="k"
)
axins.set_xlim(1.7, 4.5) # Limit the region for zoom
axins.set_ylim(1, 200)
axins.set_xticks([2,3,4])
axins.grid()


#
## draw a bbox of the region of the inset axes in the parent axes and
## connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=3, loc2=4, fc="none", ec="0.5")



plt.ylabel("CX Count")
plt.xlabel("n Qubits")
plt.xticks(qubits)
plt.legend(framealpha=1)
plt.grid()
plt.title("QHED Number of CX Operations")
plt.savefig("test2.png")
plt.show()

Papers

Pulse Control

A Categorical Order Theory of Pulse Scheduling in Gate-Based Quantum Computing[2]