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October 14, 2024

Quantum Computing: What’s New and What’s Next?

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Quantum computers use probabilistic decision-making rather than binary decision-making (heads or tails), making them more efficient at simulating molecular behavior, accelerating research on life-saving drugs and chemical reactions, optimizing factory floors or global supply chains and performing machine learning in novel ways.

2021 revealed to us the many obstacles quantum computing must overcome, including issues of stability and error rates.

What?s New?

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Quantum computers use quantum mechanics to perform complex calculations that would be beyond the capabilities of today’s most advanced classical computers. At its core lies artificial intelligence’s capacity to accelerate complex tasks, from simulating molecular behavior and biochemical reactions, or performing machine learning with new approaches, to speeding up drug discovery as well as supply-chain optimization. Companies are still grappling with how best to use this once-obscure technology and workers across their company must be informed of its potential and how it operates for maximum effect. Key to business leaders and workers across all levels of supply chains – business leaders, workers in marketing, IT infrastructure, finance, etc – is developing technology literacy. This means understanding which problems quantum computing will solve and which it won’t.

What?s Next?

At present, quantum computing is dominated by companies like IBM, Google, IonQ and Rigetti developing prototype machines. Each of them are still fiddling around with circuits to find ways to reduce errors inherent to delicate quantum hardware.

The Holy Grail of machines is an all-consuming machine capable of correcting its own errors. A variety of research factions are working on various approaches to this problem, including using loops of electric current inside superconducting circuits, trapping particles in cold environments and storing information for very short periods.

Simulation of quantum systems–from molecules to global infrastructures–can help accelerate research and advance AI models. A quantum computer, for instance, may help companies reduce the range of possible solutions to complex problems like optimising finance or logistics networks more quickly than with traditional computers, leading to one accurate answer quickly and accelerating drug and material discovery by cutting trial and error costs.

The Race to the Top

As with the flip of a coin, quantum computing yields no definitive answers; but unlike with coins you have multiple attempts before finding what works – the promise of quantum computing lies within this opportunity.

Technology could also bring immense societal advantages. It could help doctors detect cancer earlier, assist in combatting climate change, accelerate drug development and enhance supply-chain logistics efficiency – according to McKinsey’s estimates, it could add $1.3 trillion of value.

However, before realizing their full potential there must be several hurdles overcome. A major one is dealing with errors caused by noise. To address this problem researchers are developing hardware and software strategies to suppress, mitigate or clean up quantum errors; such measures could make quantum algorithms 100x more likely to produce correct answers than traditional algorithms.

The Future

Quantum computers promise to transform industries and drive global innovation, yet for this to occur businesses must understand how this technology works; to do this, ensure their leaders and employees possess basic fluency with quantum computing.

Quantum computers could assist aerospace firms by helping airlines determine where to preposition airplane parts and supplies so that when disruption occurs (for instance during a weather event), operations continue with minimal impact to passengers. Or they might help pharmaceutical firms predict how molecules interact faster so that new drugs reach market more rapidly.

Quantum computing will ultimately be measured by how many teams it enables to tackle real-world problems with it. Organizations should outline their talent requirements clearly so they can quickly recruit and develop the required talent.

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