Quantum computing has been extensively discussed as long as the first quantum computers have become a reality. As already pointed out, we are still at very early stages of such development. What we need to keep in mind is that a quantum computer needs a quantum computing algorithm. The latter can have few to no correlation with classical algorithms, such as the ones used in density functional theory for materials modeling.
The major advantage of using qubits (quantum bits) and quantum algorithms, against the classical ones, is that they are by essence fully parallelized since we are dealing with entangled states. Also, the sampling space grows with $2^n$, where $n$ is the number of qubits. That is the key: use the massive power of parallelism to perform tasks a classical (super)computer would suffer to do.
Since materials modeling relies on dealing with complex systems with lots of interactions, quantum computing can be thought of as a tool we should take care of. The main obstacles nowadays are the lack of specific algorithms for material modeling.
There are some efforts to adapt the classical algorithms, where the time-consuming parts such as the minimization problems are done by the quantum computer, and efforts to create new algorithms from scratch.
Qiskit from IBM is
an open-source quantum computing software development framework for leveraging today's quantum processors in research, education, and business.
One of its components is the Qiskit AQUA. It is a
package contains the core cross-domain algorithms and supporting logic to run these on a quantum backend, whether a real device or simulator.
Aqua includes the Chemistry package, a specific library for quantum chemistry in (IBM's) quantum computers.
The qiskit.chemistry package supports problems including ground state energy computations, excited states and dipole moments of molecule, both open and closed-shell.
The code comprises chemistry drivers, which when provided with a molecular configuration will return one and two-body integrals as well as other data that is efficiently computed classically. This output data from a driver can then be used as input to the chemistry module that contains logic which is able to translate this into a form that is suitable for quantum algorithms. The conversion first creates a FermionicOperator which must then be mapped, e.g. by a Jordan Wigner mapping, to a qubit operator in readiness for the quantum computation.
Therefore, this is still an open question.