There are plenty of examples that already run on simulators. One example would be QAOA that can be used to solve combinational problems like MQO. Then there is

There are plenty of examples that already run on simulators. One example would be QAOA that can be used to solve combinational problems like MQO. Then there is also research that compares the performance of classical ML algorithms to Quantum Based variants, not only simulated but on real, IBM quantum hardware. Not only does the quantum equivalent outperform it (in pure terms of accuracy, as time to train cannot yet be compared), but also in complexity - iirc ~67k parameters for a NN and 16 for a QNN. This is not to say that it is all set in stone - its still highly unclear which approach and design is the best for what when it comes to quantum circuits. There is also a problem that noise suppression and error correction need even bigger quantum computers, so we‘re still a way off. In the words of a Roche ML/Research presentation, who also already tried QC: They tried it and it didnt offer the expected results due to hardware limitations. But they are still actively pursuing it, because in terms of QC you have to be prepared, as otherwise you might be playing an endless catching up with those who were ready.