Classical computational tools have driven much innovation in chemistry of the past decades, but due to exponential increase in the required resources the most accurate methods struggle with more complex problems. Richard Feynman, among others, recognized that quantum systems encode quantum information and should then be simulated on a quantum computer.
Today we have commercial access to small quantum computers which can be isolated from the emvironment in such a way that they an be controlled. However, these devices are still prone to noise from the environment. Hence we are in the Noisy Intermediate Scale Quantum (NISQ) era, which is projected to last for a long time.
In order to take some of the computational burden away from the quantum device, hybrid quantum-classical algoritms have been developed. One such algorithm is the Variational Quantum Eigensolver (VQE), which uses a parameterized quantum circuit to measure the expectation value of a certain operator (usualy the hamiltonian) and optimizes the parameters using a classical optimizer. However, during this optimization procedure, the VQE parameters can absorb significant noise.
There is currently great uncertainty about the quality of parameterized wave functions on quantum computing. Are they simply mitigating noise to obtain the lowest energy, or do they still contain relevant chemical information? Our main hypothesis is that conceptual open quantum subsystems can give us insight into the current chemical applicability of quantum computing wave functions and help us develop new algorithms with reliable chemical accuracy.
