Proponents of quantum technology believe its will change the world. Others remain skeptical, as they do of technologies like fusion energy.

Speaking at a quantum developers’ forum, IBM Distinguished Engineer Jan-Rainer Lahmann retraced the history of quantum computing, reviewing IBM’s hardware and development roadmaps and describing the ingredients of “Raspberry Pi quantum”.

The history of quantum computing goes back four decades to a conference where the Nobel laureate Richard Feynman introduced the idea of simulating quantum mechanical systems on a traditional computer. At the time, this required a significant computational resources. Even with Moore’s Law scaling, it was clear to Feynman and many others that the road to quantum computing needed to be pursued. “What if we built completely different kinds of computers that made quantum mechanics’ effects such as superposition, interference, entanglement, directly accessible and controllable?” Lahmann recalled Feynman as asking.

Lahmann continued: “With such a different kind of computer, it should be much easier to simulate quantum mechanical systems. I think this idea is very clear, and it makes perfect sense.”

Since then, many scientists and engineers have pursued various approaches to building actual quantum computers. Feynman’s basic idea was that a quantum mechanical system, with several subsystems, for each qubit, provides as many traditional bits as would be needed on a traditional computer to express that state of a quantum mechanical system. For example, 2 qubits are equivalent to 512 bits, 10 qubits are equivalent to 16 kB and so on with exponential growth. Also understood at the time was how difficult it was to build large computers that could handle qubit demands.

“If you have a quantum mechanical system, you need a huge traditional computer to simulate the same things; if you have a traditional computer, then you can express this amount of information on a quantum computer under certain conditions,” said Lahmann.

Increasing the speed of a quantum computer only makes sense for very specific problems. In an example, Lahmann described how long a quantum computer and a traditional computer would take to multiply two numbers. P and Q are integers with 2,048 bits. On a traditional computer, it takes a few milliseconds. And on a fairly small and noisy quantum computer, it would take an estimated 75 seconds.

But as Lahmann noted, a similar but much more complicated problem illustrates the potential and speed of quantum computers. “We don’t want to multiply two numbers, we want to factor a large number. So we have a number of 2,048 bits and we want to derive the prime factors of that number. This is the core of our two big asymmetric encryption schemes. This takes a long time on the traditional computer, on the order of years – this takes a couple of billion CPU cores on a traditional computer.”

Citing Peter Shor’s quantum algorithm, if “we have a large enough quantum computer, this could be reduced to a few hours. That vividly shows the enormous potential and speed that quantum computers can achieve for very specific problems and very specific tasks considered intractable for classical computers,” said Lahmann.

In addition to factorization, quantum computers could help solve problems involving materials science and quantum chemistry.

**Hardware and Software**

Quantum hardware incorporates complex architectures and cooling systems. The entire system or chip must be cooled almost to absolute zero to avoid electronic noise that might interfere with calculations. Among the architectures are IBM’s superconducting qubits. Required are qubits and the quality of the qubit system, the so-called delta Q. “That’s an area of current research and development at IBM that will improve the quality of the qubits and reduce the effects of errors and noise. Currently, the systems use 65 qubits, but Lahmann said, “We want to go to 127 (IBM Quantum Eagle system) soon, and just over 1,100 qubits in two years.”

Adding a single qubit to a quantum computer doubles the capacity of the system. That’s is a huge step forward, Lahmann noted.

Quantum computing applications like neural networks are predicted for applications ranging from drug discovery to simulations of complex chemical reactions along with mathematical problem optimization and AI.

On the software side, Qiskit is a Python-based open source framework developed by IBM for quantum computing. Extensions could help improve integration with classical computing.

Qiskit is a set of coding tools for quantum circuit-level applications, as well as backend execution and administration via remote access. IBM has made the functions relatively simple even for novices in quantum theory or quantum mechanics. The objective is to provide a larger range of circuits, allowing users to solve issues that are impossible to solve with traditional computers.

Meanwhile, a frictionless development approach implements algorithms without specific requirements for using standard programming languages. “We also need additional types of developers who work much closer to the quantum hardware, and really optimize the way algorithms are implemented on quantum systems,” Lahmann said.

**Raspberry and quantum computing**

To fully utilize the potential power of future quantum computers, whole new algorithms – and perhaps new ideas – are required. That necessitates novel techniques for teaching quantum computing to IT specialists, developers and engineers in a way that is both compelling and intelligible.

To that end, RasQberry combines Qiskit, a Raspberry Pi implementation (from the Pi 4 to the Pi Zero) and a 3D printed replica of the IBM Quantum System One all used to study different quantum technologies and build tools that can be used in demonstrations. The platform will be used to demonstrate superposition, interference and entanglement.

In no way does this turn the Raspberry Pi into a true quantum computer. Rather, Qiskit is used to simulate a rudimentary quantum computer. Practically speaking, it is similar to operating a quantum computer emulation on Raspberry Pi. The scheme would provide developers a means of investigating the mysteries and promise of quantum computing.

Written in Python, Qiskit can operate natively on Raspberry Pi. The Qrasp program is used to execute quantum computing simulations directly on Raspberry Pi without an internet connection. The visualized results are then presented on a Sense HAT RGB LED matrix display.