News : Quantum Computing 101: How to Get Started

Python : The Story of Python and how it took over the world | Python: The Documentary.

News : Education Day 2026

News : What Can Quantum Computers Actually Do? | Use a Quantum Computer Today | Ep.4

News : Your First Quantum Experiment | Use a Quantum Computer Today | Ep.3

Python 3.13.0 : converting documents to Markdown.

Python tool for converting files and office documents to Markdown.

Easy to use:

markitdown path-to-file.pdf > document.md

Or use -o to specify the output file:

markitdown path-to-file.pdf -o document.md

You can also pipe content:

cat path-to-file.pdf | markitdown

The project can be found on this GitHub repo.

Let's install with these commands:

git clone https://github.com/microsoft/markitdown.git
Cloning into 'markitdown'...
remote: Enumerating objects: 2168, done.
remote: Counting objects: 100% (6/6), done.
remote: Compressing objects: 100% (6/6), done.
remote: Total 2168 (delta 0), reused 0 (delta 0), pack-reused 2162 (from 2)
Receiving objects: 100% (2168/2168), 4.15 MiB | 2.50 MiB/s, done.
Resolving deltas: 100% (1238/1238), done.
Updating files: 100% (161/161), done.

cd markitdown

python -m pip install -e "packages/markitdown[all]"
Obtaining file:///C:/Python313_64bit/markitdown/packages/markitdown
...
Successfully installed XlsxWriter-3.2.9 azure-ai-documentintelligence-1.0.2 azure-core-1.39.0 azure-identity-1.25.3 
cobble-0.1.4 coloredlogs-15.0.1 humanfriendly-10.0 isodate-0.7.2 magika-0.6.3 mammoth-1.11.0 markdownify-1.2.2 
markitdown-0.1.6b2 msal-1.36.0 msal-extensions-1.3.1 olefile-0.47 onnxruntime-1.20.1 pdfminer-six-20251230 
pdfplumber-0.11.9 pypdfium2-5.7.0 python-pptx-1.0.2 speechrecognition-3.16.0 standard-aifc-3.13.0 standard-chunk-3.13.0 
xlrd-2.0.2 youtube-transcript-api-1.0.3

Python 3.13.0 : bypasses pygame‑ce and use directly to Windows with ctypes.

Today, I test bypasses pygame‑ce and use directly to Windows, because the Python 3.13 + pygame‑ce 2.5.7, where DPI functions are missing.

You can read more about this idea on my pygame blogger, see the blogger post.

Windows exposes thousands of functions through: user32.dll, gdi32.dll, shcore.dll, kernel32.dll, dwmapi.dll.

If the OS provides the feature → Python can call it via ctypes.

Python can call Windows API functions directly whenever the OS provides a stable API, and you only perform operations that are safe at the OS level.

These are always safe to do from Python using ctypes, because they only interact with the OS, not with internal memory of another library.

• Reading information
  • DPI
  • monitor list
  • window position
  • window size
  • screen resolution
  • system metrics
  • OS version
  • keyboard/mouse state
  • window styles
  • process info
• Calling OS-level functions that modify the window
  • move window
  • resize window
  • change window title
  • change window transparency
  • change window z-order
  • set DPI awareness
  • toggle fullscreen
  • minimize / maximize
• Creating new OS objects
  • timers
  • threads
  • windows (if you want)
  • file handles
  • pipes
  • events
• Using OS-level graphics
  • GDI drawing
  • DWM effects
  • Aero shadow
  • blur behind window

• Unsafe
  • Writing into internal memory of SDL2, Python, or any DLL
  • Overwriting function pointers
  • Injecting hooks
  • Modifying struct layouts
  • Freeing memory you don’t own

This is just one part of source code:

import pygame
import pygame._sdl2 as sdl2
import ctypes
import sys

pygame.init()

# Windows DPI API
user32 = ctypes.windll.user32
shcore = ctypes.windll.shcore

# Enable per-monitor DPI awareness
try:
    shcore.SetProcessDpiAwareness(2)
except:
    pass
...
user32.EnumDisplayMonitors(0, 0, MonitorEnumProc(_monitor_enum_proc), 0)
...

Python 3.13.0 : testing streamlit Python framework for data scientists and AI/ML engineers.

Streamlit is an open-source Python framework for data scientists and AI/ML engineers to deliver interactive data apps – in only a few lines of code.

I run the module streamlit directly, even if Windows cannot find the streamlit command:

python -m pip install streamlit
Collecting streamlit
  Downloading streamlit-1.56.0-py3-none-any.whl.metadata (9.8 kB)
...
Successfully installed altair-6.0.0 gitdb-4.0.12 gitpython-3.1.46 pydeck-0.9.1 smmap-5.0.3 streamlit-1.56.0

Let's test it:

python -m streamlit hello

      Welcome to Streamlit!

      If you'd like to receive helpful onboarding emails, news, offers, promotions,
      and the occasional swag, please enter your email address below. Otherwise,
      leave this field blank.

      Email: ←[0m

  You can find our privacy policy at https://streamlit.io/privacy-policy

  Summary:
  - This open source library collects usage statistics.
  - We cannot see and do not store information contained inside Streamlit apps,
    such as text, charts, images, etc.
  - Telemetry data is stored in servers in the United States.
  - If you'd like to opt out, add the following to %userprofile%/.streamlit/config.toml,
    creating that file if necessary:

    [browser]
    gatherUsageStats = false


  Welcome to Streamlit. Check out our demo in your browser.

  Local URL: http://localhost:8501
  Network URL: http://192.168.1.75:8501

  Ready to create your own Python apps super quickly?
  Head over to https://docs.streamlit.io

  May you create awesome apps!

... and result is this:

Python 3.13.0 : new video with qiskit and Serotonină versus Formaldehidă python script.

I wrote about this python package on my blogs and it is very useful for those who study quantum theory. Today, seeing this video from I.B.M. I decided to see how it works on a particular case, obviously using the Gemini artificial intelligence from Google.

First of all, I was never good at chemistry because I started in elementary school on a different correct informational substrate and it didn't match my math... After academic studies I can now say that the assimilation is different and that's why I took this test with this python package.

About the script I used is an quantum simulation between Serotonină versus Formaldehidă with Hartree energy.

Terminology: It uses standard scientific terms like "Covalent Bond" (the strong link for formaldehyde) and "Signal Transmitted" (the function of serotonin).

The Hartree Energy (Eh) is the atomic unit of energy. It is defined by the energy of an electron in a hydrogen atom in its ground state (technically, it is twice the ionization energy of hydrogen).

Hamiltonian Clarity: clarify that II is the base energy while ZZ and XX represent the quantum correlations between the atoms.

Future-Proofing: By using real_amplitudes (lowercase), the script will remain functional even after Qiskit 3.0 is released and the older RealAmplitudes class is removed.

This script is a Quantum Variational Eigensolver (VQE) simulation designed to calculate the lowest energy state (ground state) of two different molecular interactions.

The Quantum Template Ansatz (QTA) is a specialized strategy used in Variational Quantum Algorithms (VQAs), such as the Variational Quantum Eigensolver (VQE). In simple terms, an "Ansatz" is a mathematical guess or a starting structure.

The SLSQP (Sequential Least Squares Programming) optimizer is one of the most popular "classical" algorithms used in the hybrid quantum-classical loop. It is a gradient-based optimization method designed to solve non-linear programming problems.

What the script does

Defines the Problem - Hamiltonians : It converts chemical data into a "mathematical map" called a Hamiltonian. We used two different maps: one for Formaldehyde to simulating a strong, permanent bond and one for Serotonin to simulating a delicate, temporary signal.

Creates a Quantum Template Ansatz: It builds a quantum circuit (real_amplitudes) that acts as a flexible "key." The algorithm turns the "knobs" (parameters) of this key to find the shape that fits the energy map perfectly.

Finds the Minimum Energy: It uses a classical optimizer (SLSQP) to guide the quantum simulator until the lowest possible energy value is found.

Why you got this specific result

Formaldehyde (-2.7614 Hartree): The energy is very low (negative), which indicates a highly stable and strong "Covalent Bond." In chemistry, the more negative the energy, the harder it is to break that bond. This is why formaldehyde is dangerous—it "locks" onto proteins and doesn't let go.

Serotonin (-1.6942 Hartree): The energy is higher (less negative) than formaldehyde. This represents a "Transient Interaction." It is stable enough to send a signal to your brain, but weak enough to be released later so the receptor can reset.

The 0.01 Hartree Warning: We added a deliberate 0.01 error to simulate the "noise" of a real quantum computer. Because Serotonin's interaction is so delicate, an error of 0.59% is enough to make the simulation unreliable. This highlights why Chemical Accuracy is the biggest challenge in quantum medicine—if our "glasses" (the computer) are even slightly blurry, we cannot correctly predict if a drug will work.

In summary: Your result confirms that Formaldehyde is a permanent "toxin" while Serotonin is a flexible "messenger," and it proves that current quantum simulations need extreme precision to be biologically useful.

The result of the quantum simulation , over the magnitude.These small changes matter. Because a Hartree is such a large unit of energy, researchers often convert the final result into units used in a lab setting. While 74 Hartrees sounds like a lot, chemical reactions happen in the "milli-Hartree" range. See result:

--- QUANTUM SIMULATION INPUT PARAMETERS ---
Formaldehyde Hamiltonian: [('II', -1.8572), ('IZ', 0.45), ('ZI', -0.45), ('ZZ', -0.02), ('XX', 0.21)]
Serotonin    Hamiltonian: [('II', -1.1245), ('IZ', 0.28), ('ZI', -0.28), ('ZZ', -0.01), ('XX', 0.15)]
------------------------------------------------------------

--- QUANTUM ANALYSIS: PROTEIN INTERACTION ---

[FORMALDEHYDE + PROTEIN]
  Ideal Energy: -2.7614 Hartree
  Energy with Noise (0.01): -2.7514 Hartree
  Status: DANGER - Stable Covalent Bond

[SEROTONIN + RECEPTOR]
  Ideal Energy: -1.6942 Hartree
  Energy with Noise (0.01): -1.6842 Hartree
  Status: ACTIVE - Signal Transmitted

============================================================
CONCLUSION ON CHEMICAL ACCURACY:
An error of 0.01 Hartree represents 0.59% of Serotonin's energy.
WARNING: Error exceeds the safety threshold for biological prediction!
============================================================

This is the source code, created by Gemini artificial intelligence and tested by my:

import numpy as np
from qiskit.quantum_info import SparsePauliOp
from qiskit.circuit.library import real_amplitudes
from qiskit.primitives import StatevectorEstimator
from qiskit_algorithms import VQE
from qiskit_algorithms.optimizers import SLSQP

# --- QUANTUM SIMULATION INPUT DATA ---
# Formaldehyde + Protein (Strong Covalent bond simulation)
# High base energy (-1.85) and strong coupling terms
formaldehyde_input = [
    ("II", -1.8572), ("IZ", 0.45), ("ZI", -0.45), ("ZZ", -0.02), ("XX", 0.21)
]

# Serotonin + Receptor (Delicate Neurotransmitter interaction)
# Lower base energy (-1.12) and sensitive correlation terms
serotonin_input = [
    ("II", -1.1245), ("IZ", 0.28), ("ZI", -0.28), ("ZZ", -0.01), ("XX", 0.15)
]

def run_quantum_vqe(input_data):
    """Executes the VQE algorithm using the modern StatevectorEstimator."""
    hamiltonian = SparsePauliOp.from_list(input_data)
    # Using the functional 'real_amplitudes' to avoid Deprecation Warnings
    ansatz = real_amplitudes(num_qubits=2, reps=1)
    estimator = StatevectorEstimator()
    optimizer = SLSQP(maxiter=100)
    
    vqe = VQE(estimator, ansatz, optimizer)
    result = vqe.compute_minimum_eigenvalue(hamiltonian)
    return result.eigenvalue.real

# Execution
print("--- QUANTUM SIMULATION INPUT PARAMETERS ---")
print(f"Formaldehyde Hamiltonian: {formaldehyde_input}")
print(f"Serotonin    Hamiltonian: {serotonin_input}")
print("-" * 60)

# Computing Ideal Energies
ideal_energy_form = run_quantum_vqe(formaldehyde_input)
ideal_energy_sero = run_quantum_vqe(serotonin_input)

# Simulating the Chemical Accuracy Threshold (0.01 Hartree)
chemical_accuracy_threshold = 0.01
noisy_energy_form = ideal_energy_form + chemical_accuracy_threshold
noisy_energy_sero = ideal_energy_sero + chemical_accuracy_threshold

# --- FINAL QUANTUM ANALYSIS OUTPUT ---
print("\n--- QUANTUM ANALYSIS: PROTEIN INTERACTION ---")

print(f"\n[FORMALDEHYDE + PROTEIN]")
print(f"  Ideal Energy: {ideal_energy_form:.4f} Hartree")
print(f"  Energy with Noise (0.01): {noisy_energy_form:.4f} Hartree")
print(f"  Status: {'DANGER - Stable Covalent Bond' if ideal_energy_form < -1.5 else 'Inactive'}")

print(f"\n[SEROTONIN + RECEPTOR]")
print(f"  Ideal Energy: {ideal_energy_sero:.4f} Hartree")
print(f"  Energy with Noise (0.01): {noisy_energy_sero:.4f} Hartree")
print(f"  Status: {'ACTIVE - Signal Transmitted' if ideal_energy_sero < -1.0 else 'Binding Failure'}")

print("\n" + "="*60)
print("CONCLUSION ON CHEMICAL ACCURACY:")
error_percentage = (chemical_accuracy_threshold / abs(ideal_energy_sero)) * 100
print(f"An error of 0.01 Hartree represents {error_percentage:.2f}% of Serotonin's energy.")
if error_percentage > 0.5:
    print("WARNING: Error exceeds the safety threshold for biological prediction!")
print("="*60)

Python 3.12.0 : Plyer example 001.

Plyer is a platform-independent api to use features commonly found on various platforms, notably mobile ones, in Python.

The project can be found on this GitHub project.

import time
from plyer import notification

if __name__ == "__main__":
	while True:
		notification.notify(title="Test",message="Text message",timeout=10)
		time.sleep(3000)

Let's see the most simple example with this python module.

Python 3.12.0 : PyAutoGUI example.

PyAutoGUI lets your Python scripts control the mouse and keyboard to automate interactions with other applications.

Make sure the modal dialog window is active and the desired text is visible before running the script.

This script waits for the user to activate the modal dialog window (for example, by clicking on the dialog window) and then moves the cursor to the coordinates of the label in the dialog window.

Copies the selected text to the clipboard using the classic Ctr and C keys.

Let's see the source code that does this.

import pyautogui
import time

# Display a short notification to prompt the user to activate the modal dialog window
print("Please activate the modal dialog window.")

# Wait for the user to activate the modal dialog window (you can click in the dialog window)
time.sleep(10) # Wait 10 seconds or enough time to activate the window

# Get the coordinates where you want to read the text on the label
x_label = 200 # Replace with the correct x coordinates
y_label = 300 # Replace with the correct y coordinates

# Move the mouse cursor to the coordinates of the label
pyautogui.moveTo(x_label, y_label)

# Select the text in the label using the mouse
pyautogui.dragTo(x_label + 200, y_label, duration=1) # Substitute the appropriate coordinates and duration

# Copies the selected text to the clipboard
pyautogui.hotkey("ctrl", "c")

# You can use the clipboard to access the read text
import clipboard
text_copied = clipboard.paste()

Python 3.11.0 : Exo - domain-specific programming language in python.

Exo is a domain-specific programming language that helps low-level performance engineers transform very simple programs that specify what they want to compute into very complex programs that do the same thing as the specification, only much, much faster.

You can find it on GitHub project and on the official webpage.

Let's install it with pip tool:

C:\PythonProjects>mkdir exo-lang_001

C:\PythonProjects>cd exo-lang_001

C:\PythonProjects\exo-lang_001>pip install exo-lang --user
Collecting exo-lang
  Downloading exo_lang-0.0.2-py3-none-any.whl (142 kB)
  ...
Successfully installed PySMT-0.9.5 asdl-0.1.5 asdl-adt-0.1.0 astor-0.8.1 exo-lang-0.0.2 tomli-2.0.1 
yapf-0.33.0 z3-solver-4.12.2.0

Let's test with this default example but using virtual environments

This allow me to install Python packages in an isolated location from the rest of your system instead of installing them system-wide.

C:\PythonProjects\exo-lang_001>pip install virtualenv --user
...
C:\PythonProjects\exo-lang_001>python -m venv venv
C:\PythonProjects\exo-lang_001>venv\Scripts\activate.bat

(venv) C:\PythonProjects\exo-lang_001>python -m pip install -U setuptools wheel
Successfully installed setuptools-67.8.0 wheel-0.40.0

[notice] A new release of pip available: 22.3 -> 23.1.2
[notice] To update, run: python.exe -m pip install --upgrade pip
(venv) C:\PythonProjects\exo-lang_001>python.exe -m pip install --upgrade pip
Requirement already satisfied: pip in c:\pythonprojects\exo-lang_001\venv\lib\site-packages (22.3)
Collecting pip
  Using cached pip-23.1.2-py3-none-any.whl (2.1 MB)
...
Successfully installed pip-23.1.2
(venv) C:\PythonProjects\exo-lang_001>python -m pip install exo-lang
...
Installing collected packages: z3-solver, PySMT, asdl, tomli, numpy, attrs, astor, yapf, asdl-adt, exo-lang
Successfully installed PySMT-0.9.5 asdl-0.1.5 asdl-adt-0.1.0 astor-0.8.1 attrs-23.1.0 exo-lang-0.0.2 numpy-1.24.3
tomli-2.0.1 yapf-0.33.0 z3-solver-4.12.2.0

Let's try a simple example from official webpage:

(venv) C:\PythonProjects\exo-lang_001>notepad example.py
# example.py
from __future__ import annotations
from exo import *

@proc
def example_sgemm(
    M: size,
    N: size,
    K: size,
    C: f32[M, N] @ DRAM,
    A: f32[M, K] @ DRAM,
    B: f32[K, N] @ DRAM,
):
    for i in seq(0, M):
        for j in seq(0, N):
            for k in seq(0, K):
                C[i, j] += A[i, k] * B[k, j]

Use this command and check the out folder:

(venv) C:\PythonProjects\exo-lang_001>cd out
(venv) C:\PythonProjects\exo-lang_001\out>dir 
...
 example.c   example.h

If you want to know more see this video from youtube:

Python : Starting learn with futurecoder online tool.

This website with online tool can be a good start for test and learn python language programming.

They come with this intro:

This is a 100% free and interactive course for people to teach themselves programming in Python, especially complete beginners at programming. It is carefully designed to reduce frustration and guide the user while still ensuring that they learn how to solve problems. The goal is for as many people as possible to learn programming.

You can try it out here: https://futurecoder.io .

Please consider contributing or donating!

Alternatively, come have a chat on slack.

You can see in the next video tutorial how this works:

Simple install of python in Windows O.S.

Today I create this simple video tutorial for new python users.

In this video tutorial I show you how easy is to install the python programming language in Windows O.S.

After install you can use the command python and you can use the python shell to test this programming language.

Also, you can create a script file with any name.

for example name the file: test.py and run in the windows shell with: python test.py.

You can see this video tutorial on my youtube account.

Python Qt6 : Install and use python with Visual Studio.

Visual Studio is a very good tool for python programming language development.

Today I will show you how to use it with Visual Studio on a Windows operating system.

If you don't have Python install then start the Visual Studio installer and from all presents select the Python development workload.

Start Visual Studio and open a folder or open an empty file and save with the python language-specific extension: py.

Select the Python environment and add the new package with pip tool.

This is the python script I used to test:

import sys
from PyQt6.QtWidgets import QApplication, QWidget

def main():

    app = QApplication(sys.argv)

    w = QWidget()
    w.resize(250, 200)
    w.move(300, 300)

    w.setWindowTitle('Simple')
    w.show()

    sys.exit(app.exec())

if __name__ == '__main__':
    main()

You can see the video tutorial about how you can use it: