With the onset of what has been coined the Second Quantum Revolution, there is a need to increase quantum literacy – both within the general public and in science and technology educations. The growing appreciation and proficiency when it comes to scientific computing is an asset this regard. With basic programming skills in frameworks such as, e.g., Python, learners are able to approximate solutions and visualize them for a wide range of relevant quantitative problems.

The ability to solve tasks for which purely analytical methods fall short by writing a few lines of opens up a vast pedagogical landscape. And it enables learners to solve comparatively complex problems even with a rather scarce numerical toolbox at hand.

However, it goes beyond that; by implementing solutions to problems which are simple but generic, theory becomes tangible. This, combined by adequate visualization of results and simulations, may be used to foster increased understanding.

We aim to take advantage of such an approach in introducing quantum physics and quantum technology taking full advantage of this possibility. And we aim to do so addressing a rather diverse group of students, i.e., beyond the traditional context of bachelor students in physics. While introducing quantum phenomena in this way does not require familiarity with physics from the outset, it does require a certain proficiency when it comes to calculus, linear algebra and numerics.

Our hope is that students, by writing or adapting their own implementations, develop a sense of ownership to the problems to be solved. And the ability to visualize dynamical quantum phenomena such as wave propagation, interference, scattering and tunneling in an exploratory manner may nurture curiosity, spurring questions and further investigation. Such a computational approach also enables variational calculations using optimization techniques such as the gradient descent method. Moreover, through the dynamics of spin ½-systems, quantum bits and quantum computing may be introduced – along with the technology using nuclear magnetic resonance.

The basic numerical framework developed could also serve as a platform for more advanced topics, involving photo ionization, open quantum systems, resonances and non-Hermicity. While such concepts may be rather hard to grasp, implementing them in simple systems is not necessarily that challenging. In this way, such implementations could work as an adequate entry point.

We will present specific examples, implementations and visualizations illustrating these ideas. Additionally, we will share some experiences regarding how and to what extent it has proven viable in real classroom situations.

Keywords: Quantum physics, quantum technology, python, scientific computing.