Scientists at Xanadu have curated a list of 20 molecules that could be key to advancing quantum computing. The list includes hydrogen, methylene, ammonia, water, dicarbon, nitrogen, ethylene, ozone, thioformaldehyde, benzene, pyrazine, manganese carbide, titanium oxides, chromium dimer, iron-sulfur clusters, pentacene, oxo-Mn(salen), iridium complexes, iron porphyrin complexes, and FeMoco. The molecules are ranked by increasing complexity, with hydrogen being the simplest and FeMoco being the most complex. The team at Xanadu hopes this list will guide those working at the intersection of quantum computing and quantum chemistry.
Introduction to Quantum Computing and Chemistry
The development of quantum computing as a commercial technology faces the challenge of finding concrete examples where quantum computers could outperform classical methods for industrially interesting use cases. Quantum chemistry applications are no exception. A team of researchers at Xanadu, including Soran Jahangiri, Diego Guala, Utkarsh Azad, and Juan Miguel Arrazola, have introduced a curated list of molecules for quantum computing. This list is part of an ongoing endeavor at Xanadu, and the team invites the community to join their efforts.
Top 20 Molecules for Quantum Computing
The list of top 20 molecules for quantum computing is ordered by increasing complexity, starting with simple molecules with few electrons and ending with some of the most difficult molecules to simulate, with properties that are not yet fully understood. The list includes Hydrogen (H2), Methylene (CH2), Ammonia (NH3), Water (H2O), Dicarbon (C2), Nitrogen (N2), Ethylene (C2H4), Ozone (O3), Thioformaldehyde (CH2S), Benzene (C6H6), Pyrazine (C4H4N2), Manganese carbide (MnC), Titanium oxides (TinOm), Chromium dimer (Cr2), Iron–sulfur clusters (FenSm), Pentacene (C22H14), oxo-Mn(salen), Iridium complexes, Iron porphyrin complexes, and FeMoco.
Quantum Properties of Molecules
The quantum properties of many of the molecules listed can be found, downloaded, and easily implemented into various calculations using PennyLane Datasets. Each molecule presents unique challenges and opportunities for quantum computing. For example, Hydrogen, the smallest neutral molecule, is the “hello world” of quantum algorithms for chemistry. Methylene, a simple molecule, is relevant to excited-state energy calculations. Ammonia is a good candidate for simulating reactions and testing the accuracy of quantum algorithms. Water, an intermediate-size molecule, is ideal for benchmarking more sophisticated quantum algorithms.
Quantum Computing and Complex Molecules
The second half of the list presents molecules that are too large to be treated with existing quantum hardware or simulators. These molecules are representative of some of the most demanding simulation problems in quantum chemistry. If quantum computing is to become a disruptive new approach to chemistry, it will have to demonstrate its ability to handle systems of this size and importance. These include Pyrazine, Manganese carbide, Titanium oxides, Chromium dimer, Iron–sulfur clusters, Pentacene, oxo-Mn(salen), Iridium complexes, Iron porphyrin complexes, and FeMoco.
Future Directions
The team at Xanadu hopes that their Top 20 list will serve as a useful guide for anyone working at the intersection of quantum computing and quantum chemistry. They continue to expand quantum datasets for chemistry and to develop more powerful quantum algorithms, targeted at the most promising applications. The future of quantum computing will likely be closely tied to quantum chemistry and quantum simulation, and the community should be ready to meet it. The team has prepared numerous quantum chemistry datasets that can be set up and downloaded, plug-and-play directly into your PennyLane workflow.
The authors of the article are Soran Jahangiri, Diego Guala, Utkarsh Azad, and Juan Miguel Arrazola, all of whom are researchers at Xanadu. Their work is focused on developing and implementing quantum algorithms for chemistry and industrial applications, making quantum computing more useful and accessible, and exploring its applications in natural sciences.
