QUANTUM COMPUTINGHorses for Courses: Where Quantum Computing Is, and Isn’t, the Answer

By Stephan Robin

Despite the impressive and undeniable strides quantum computing has made in recent years, it’s important to remain cautious about sweeping claims regarding its transformative potential. To avoid future disillusionment as the technology matures and ensure that public focus and investment is best utilized, more effort is needed to bridge the gap between perceptions and technical realities.

Quantum and classical computers should be compared based on the kinds of problems they can solve, not on the machines themselves: quantum computing is not a better or faster version of classical computing, but a different paradigm of computation altogether. Comparing their inherent speed is like asking whether a paintbrush is faster than a camera. The comparison has value only in relation to their relative capability in doing certain tasks. For many problems, such as climate change modelling, a classical computer will probably provide better solutions for the foreseeable future, even if a working and practically useful quantum computer were available.

Much of the public focus is on scaling quantum computers, often measured by the number of logical quantum bits (qubits) of prototype machines. But scale isn’t everything. An algorithm is needed to do useful calculations with the qubits. Researchers have identified around 74 quantum algorithms, with Shor’s and Grover’s algorithms being the most widely recognised. The discovery of new algorithms may expand the scope of problems that quantum computers can solve, but these breakthroughs shouldn’t be taken for granted.

Contrary to popular claims, quantum algorithms don’t ‘try all solutions at once’. Instead, they carefully manipulate qubits to amplify the probability of measuring a useful answer at the output, while suppressing the probability of measuring any other answer. It’s roughly similar to a magician’s card trick: rather than checking every card in the deck for the one that was chosen, the magician uses a clever sequence of steps to make the chosen card more likely to appear without actually knowing what it is.

Compared to classical algorithms, quantum algorithms can more effectively scale with problem size. This means that for a sufficiently large problem, they may be able to compute an answer more efficiently—in time, energy or cost—than classical alternatives. For smaller problems, it is likely that classical computers will retain a clear comparative advantage for the foreseeable future due to the overheads in quantum computing.