When developing new materials or drug substances, it is a big advantage to be able to calculate their properties. However, simulating such large quantum mechanical systems is a difficult computational task. The solution can be to use another, more controllable quantum system.
Simulation of large quantum systems is a hard task even for today’s supercomputers. One difficulty is that the amount of computer memory needed to store a quantum state grows exponentially with the size of the quantum mechanical system.
In 1982, the famous physicist Richard Feynman got an idea about how to overcome the difficulties: A controllable quantum system could be used to study another, less controllable or accessible quantum system. This is called quantum simulation.
Quantum simulation could be implemented using quantum computers (so-called digital simulation), but also with simpler devices, so-called analog simulators, which would be easier to construct. Analog simulators are specifically designed to simulate a certain system or process and therefore have a limited scope of use, while quantum computers can be programmed to take on many types of problems.
In recent years, the field of quantum simulation has been developing rapidly, and there are now a number of different platforms in which quantum systems – such as neutral atoms, ions, superconducting circuits, nuclear spins, and photons – can be experimentally probed for quantum simulation.
In addition to helping to identify new materials and drug substances, quantum simulation promises for example to solve routing and scheduling problems and to be very useful in advancing research in many fields of physics, quantum chemistry, and cosmology.
Among the most advanced examples of quantum simulation this far are simulation of many-body dynamics on a 51-atom quantum simulator and simulation of the critical dynamics associated with quantum phase transitions on a Rydberg atom simulator.