“IBM Research is pairing its Jupyter-based Data Science Experience notebook environment with its cloud-based quantum computer, IBM Q, in hopes of encouraging a new class of entrepreneurial user to solve intractable problems that even exceed the capabilities of the best AI systems.
Big Blue has been providing scientists, researchers, and developers with free access to IBM Q processors for over a year. The favorite way to access these quantum systems is through the Quantum Information Software developer Kit (QISKit), which is software development environment designed to allow users to develop and deploy quantum algorithms via a Python interface.
In a blog post today, IBM announced that it has issued more than 20 new QISKit notebooks this week, and the bulk of them are targeted at quantum researchers to perform various types of experiments. But among the new notebooks is one designed to help developers conduct quantum experiments through the Data Science Experience (DSX), which is IBM’s cloud-based data science notebook offering that’s targeted at commercial data scientists.
The hope is that the new DSX offering could open the door to a new class of developer, specifically “entrepreneurial-minded programmers and developers” who are eager to experiment with quantum computing’s potential, but who aren’t necessarily interested in quantum computing for quantum computing’s sake.
Jay M. Gambetta, who’s a manager for Theory of Quantum Computing and Innovation at IBM, say the new DSX option is an excellent choice for a developer who’s just getting started with QISkit.
“You can skip all the installation and environment creation steps on your computer, and instead use this Web-hosted Jupyter notebook environment for running the Quantum programs,” Gambetta tells Datanami via email. “It also provides a platform where you can invite fellow researchers to collaborate on the notebooks you have developed or simply share your work within the community.”
While DSX helps data scientists script and solve big data problems using the latest machine learning (it includes Apache Spark MLlib and IBM’s own SystemML libraries), the capability to add quantum computing to the mix gives the environment something not readily available elsewhere.
It also gives data scientists the potential to solve intractable problems that even exceed the capabilities of today’s mammoth distributed clusters. For example, an IBM spokesperson says a quantum computer could yield the solution to the traveling salesman’s problem. “A new Jupyter notebook gives developers the chance to explore this age-old problem,” the spokesperson says.”