What is Quantum Chemistry?¶
Quantum chemistry is an area of research focused on calculating properties of molecules, and the materials built out of them, using quantum mechanics. As an application of quantum computing, it is paramount to the commercial adoption of quantum computers, because the task of simulating various properties of matter is a ubiquitous task in many industries.
Research stemming from quantum chemistry could be used to calculate the time evolution of a complex system, estimate the ground-state energy of a molecule, or determine the electronic band structure of an exotic material, all of which go beyond classical physics. Since quantum computers are also quantum-mechanical, they offer a potential quantum computational advantage in simulating the quantum properties of matter. It is also possible that we can exploit quantum computers to learn new classical methods, using techniques such as quantum machine learning.
Nature is quantum-mechanical¶
Richard Feynman famously said that “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical.” To simulate a general quantum system on a classical computer, the available resources need to scale exponentially with the size of the system. Therefore, a classical computer cannot efficiently simulate a general quantum system.
Following Feynman’s suggestion, we note that nature is quantum. This suggests that if our simulation platform is also quantum, we should be able to simulate nature better than classical computers can, with resources scaling as the size of the target system, rather than exponentially. For quantum chemistry, this offers a dramatic improvement on traditional, exponentially costly methods.
Quantum chemistry is the leading quantum computing application¶
Understanding the quantum properties of materials is of growing importance in a wide range of industries. As a result, quantum chemistry is viewed as the leading candidate for a practical application of quantum computing. Current quantum devices are small, and are therefore limited to efficiently simulating small systems.
Hence, there is a push to develop scalable algorithms that will work on larger devices, which we expect to become available as current hardware limitations pass. Progress in this direction is being made in earnest with the ability to simulate chemical reactions, optimize molecular geometries, and retrieve low-energy states of small molecules to a high precision.
Leveraging quantum machine learning¶
Many quantum chemistry algorithms require optimization as a critical step; an algorithm may contain parameters that must be fine-tuned for a specific application. What we know about optimizing quantum algorithms falls back on another area of research within quantum computing: quantum machine learning (QML).
The advent of machine learning in the physical sciences has spurred countless generalizable techniques for the simulation of matter. Inversely, a staple quantum chemistry algorithm called the variational quantum eigensolver (VQE) has helped motivate invaluable discoveries in QML, like barren plateaus, quantum-inspired optimization routines, and hardware-compatible differentiation methods, all of which are also relevant in quantum chemistry algorithms.
PennyLane for quantum chemistry¶
PennyLane is an open-source software framework built around the concept of quantum differentiable programming. Within PennyLane, the quantum chemistry module gives users the power to implement and develop state-of-the-art quantum chemistry algorithms.
To find out more, visit the PennyLane Documentation, or check out the gallery of hands-on quantum chemistry demonstrations.
