One of the most promising areas for the use of quantum computing is in the area of quantum computational chemistry. As Professor Richard Feynmann once said, “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.” The potential applications for quantum computational chemistry include many important things including drug discovery, material design, catalyst discovery, chemical process optimization, carbon sequestration, and many others.

To that end the software team at Quantinuum (formerly Cambridge Quantum) has been working on a software package for several years aimed at helping computational chemists leverage the power of quantum computers to solve some of their difficult problems. The package was originally called EUMEN during the beta-testing process, but now it has been publicly released under the name InQuanto. The software contains higher level routines that provide a number of key algorithms for complex molecular and materials simulations. InQuanto runs on top of Quantinuum’s TKET software which leverages the hardware agnostic and powerful optimization capabilities already present in TKET.

Some of the algorithms available within InQuanto include the Variational Quantum Eigensolver (VQE), a hybrid classical/quantum algorithm that can find the ground state energy of a molecule or material. It also includes other algorithms for ground state and excited state calculations including ADAPT-VQE, Quantum Subspace Expansion and penalty-driven VQE approaches. It also includes embedding and fragmentation capabilities where both quantum and classical solvers can be applied to different pieces of a fragmented material. One interesting feature in the software is that it includes chemistry-specific noise-mitigation techniques to get the most benefit out of currently available NISQ level machines.

The Cambridge Quantum team at Quantinuum has been working for a few years with customers such as Nippon Steel, JSR, BMW, Roche, BMW, and Honeywell Performance Materials and Technologies (PMT) with earlier versions of this software and has published several papers describing their results. (For example, see here, here, and here.)

Additional information about InQuanto is available in a press release here, a brochure for the product here, and a Medium article that provides a technical introduction to the platform here.

May 24, 2022