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 utilised, 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.
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