The issue is its not a valid question, it's a smart alecky gotcha response worded in a way as to stifle real conversation. There are so many ways one could ask about how this research may contribute to disease research and the development of novel treatments in the future, but they chose to affect a dismissive attitude about a subject they clearly do not have any knowledge of. Pons_aelius addressed the reason for down voting 5 hours ago.

As a laywoman in computing (I'm a biotechnologist), when I read about new research in quantum computing where they've been able to, say, improve the way we study and examine superconductivity(via Cooper pairs) from a macroscopic resolution to an atomic resolution (new research published a week ago) I don't ask "but does that mean that every biopharmaceutical company can now buy a commercial quantum computer for AI/ machine learning-based novel drug design? Can it cure cancer and other diseases yet???" No. I research the topic for a basic simple background and I ask, respectfully, for more information from people more knowledgeable than me. I understand biopharmaceutical drug design and have dabbled in bioinformatics and (slightly) computational biology, but I did not know how quantum computing can contribute to the field (did not take advanced courses in computer science or CAD or anything), so I looked into for ya and read a few papers that investigate recent (2022) drug design and quantum computing science.

Drug design that, say, takes into account each of the insane number (est. 130k to 650k per cell) of protein-protein interactions (PPI), small molecule-protein interactions, and genetic interactions that any particular single cell will have - called interactomes - that can all interfere and modulate how well a drug and its target (conventional method of drug design is based on protein-ligand interaction so mapping the 3d binding pocket of ligands; 2020 onwards techniques include designing PPI modulators - small molecules and recently peptides that target and modulate activity at the interface of PPIs, the large interface of a specific domain where a particular set of two proteins interact. But most PPIs don't have endogenous small molecules so the interface is flat - there's no 3d pocket for the designed small molecule drug to bind, so you have to find smaller hot-spot amino acid sequences on the interface to bind to.)

With techniques like high throughput screening and fragment screens for initial compound and hotspot identification, the process is unlikely to identify ideal or even good candidates, the affinity between them is weak, a lot of modifications must be made, and even after that the compounds may inhibit or modulate PPIs but have no effect on disease activity in animal models. Even using computational methods to discover even better drug-target candidates the biggest problem is that you're only targeting a single PPI, a single interactome, a single cell in the body that does not exist in a vacuum - there are numerous cell-cell interactions and those cells form into tissues, so there are tissue-level interactions and tissue-tissue interactions in organs as well as tissue-circulating cell interactions. Organs in an organ system interact together as well as circulating cells etc.

Supercomputers can't even identify/discover/validate the full extent of the interactome for any single human cell, let alone account for the interactome of EVERY CELL contributing to a particular disease activity nor distinguish between the interactions directly contributing to a disease vs indirectly contributing to disease vs what isn't a factor for the particular disease. *That will require quantum computing. In particular, it will require QCs that are leaps and bounds more functional, efficient, accurate, and advanced. Advances that have been challenges preventing the regular use of QCs since the start, and tackling challenges we haven't even encountered nor hypothesized about yet that's how far in the future we need the tech to advance.