QUANTUM COMPUTING
“ In the near term , we ’ re looking to quantum computers to solve today ’ s problems faster or more accurately . That means organisations can start by identifying their high-value problems that may be improved by quantum computers ( typically in areas of optimisation , machine learning , and simulation .) Activities from supply chain optimisation and vehicle routing to predictive modelling to complex derivative valuation could all benefit from quantum technology .”
When it comes to quantum adoption , training resources are also critical . “ There are many individuals in organisations today who are passionate about the idea of using quantum computers . Turning them into quantum information scientists , the quantum equivalent of data scientists , can take a year or two of concerted effort . Preparing in advance to avoid the future rush will help organisations be more successful .”
How quantum will converge with AI The convergence of quantum computing and artificial intelligence is also an area of active research and development . Quantum machine learning ( QML ), one type of AI , is already a hot topic of investigation . Buchholz explains that there are several classes of interesting algorithms including :
Quantum annealers , special purpose optimization machines , can be used to optimise the training of existing systems that use ‘ decision forests ’, a type of explainable machine learning . Organisations using those algorithms can see measurable , material improvements today .
“ Given the potential for quantum in the enterprise , experts and organisations will need to invest in learning how to apply quantum computing techniques to their workplace ”
SCOTT BUCHHOLZ CTO , GLOBAL QUANTUM COMPUTING LEADER DELOITTE
96 June 2024