There is a general trend in academia: the growth of open-source software for open science. Quantum tech is part of this transformation.
Open-source code is hosted on freely reachable online repositories, such as Github, and distributed for everyone to tweak it and build upon it. Often this happens in a collaborative way, and it can be seen as just one aspect of the shift to larger teams in academic research. Link
Open science is a growing movement across various research communities, which aims at removing any boundary limiting the dissemination of knowledge. Some of its tools are characteristic of computational research, such as accessible data sets and interactive Python notebooks, ensuring that scientific results can be easily reproduced. Link
But what is the difference, one might say, from the usual implementation of numerical studies? After all, physicists have been among the first researchers to adopt computational tools, back in the days when punched cards meant counterculture innovation. The main difference indeed lies in the way code is developed and distributed.
Unlike the long programs of the past, sometimes heavy on syntax or hard to debug, interactive notebooks equip scientists with the modern notepads. Not only they are used to jot down ideas, test them, and visualize results immediately, but they are affecting the very way scientific knowledge itself develops and spreads. Link
Moreover, the features determining the success of a given programming language have shifted. Take Python: it is known to be inherently slower than other languages, but its simple syntax and flexibility has made it the fastest-growing major programming language in recent years. Link
If Python has rapidly become the language of choice for most of open-source collaborative projects, it is also because it now offers a vibrant ecosystem of open-source libraries. From plotting to machine learning to quantum algorithm design, the strength of these libraries lies in their modular and stackable configuration, which can adapt to the requirements of the user. Link
And then there are features characteristic of the quantum tech ecosystem.
It is now possible for anyone to launch a program to run it on a quantum machine from the cloud. While the usefulness of this option might now be limited — beyond some research-related aspects — the very idea of this possibility is driving thousands of non-experts to tinker with quantum circuits online, for example on the IBM Q. Link
The learning curve is still steeper for quantum mechanics than for other areas, such as the implementation of machine learning, but at the same time there is a growing community of young coders eager to learn quantum information through coding. Link
The quantum tech ecosystem is being affected by these trends, both in academia and industry.
On the one hand, quantum computing software faces a series of unique challenges. Since it is designed to be run on a quantum compiler, it needs to be converted from high-level user instructions into low-level machine code, eventually implemented on a quantum computer. As quantum computing technology is still in its infancy, also these passages are under experimental development, and the open-source approach might play a role there too.
On the other hand, quantum computation experiments are not the whole story. Indeed there is an increasingly large pool of tools designed, developed and maintained to study quantum physics or just particular aspects of quantum theory. For example, thousands of researchers, every day, simulate quantum mechanics phenomena from their laptops, using open-source software libraries as QuTiP, the quantum toolbox in Python. Link
All of these factors are contributing to create a high demand of a new professional figure: the quantum-software engineer. Startups are looking for new hires familiar with quantum mechanics. Research groups are eager to find someone able to optimize their code and consistently build upon existing projects through continuous integration.
And while only experts might master specific sets of skills, a larger number of researchers is benefiting from learning how to use the standard tools of open-source coding.
- Gravitational Quantum Sensors
- Quantum Advantage
- Analog Computing
- Quantum Internet
- Quantum Games
- Open-Source Quantum Tech
- Quantum Machine Learning
- Space Quantum Communication
© Nathan Shammah — 2017 and beyond.
Nathan Shammah 