How Business and Tech Leaders can Prepare for Quantum Future

Quantum computing has long been heralded as the next frontier in computing, with the potential to revolutionise fields from cryptography to drug discovery. While practical quantum computers remain elusive, the field has seen steady progress in recent years, bringing this transformative technology one step closer to reality.

Classical computers process information using bits – the fundamental units of data represented as either a 0 or a 1. Quantum computers, on the other hand, use quantum bits or qubits to encode information. Qubits can exist in quantum superposition, meaning they can be a 0 and a 1 simultaneously, rather than being confined to a definite state like classical bits.

This quantum property allows quantum computers to perform certain computations exponentially faster than classical computers. Algorithms running on quantum machines can solve problems like prime factorisation and database searching much more efficiently than classical approaches. Quantum computers also have the potential to simulate complex quantum mechanical systems, which is a significant challenge for classical computers.

However, maintaining the delicate quantum states required for computation is extremely difficult. Qubits are highly susceptible to environmental interference, which can cause them to "decohere" and lose their quantum properties, and significant engineering challenges remain in building large-scale, fault-tolerant quantum computers.

Tackling these challenges requires collaboration and extensive efforts. This month, Technology Magazine speaks to Deloitte’s Global Quantum Computing Leader Scott Buchholz, whose team recently collaborated with the World Economic Forum to release a report on how organisations and governments can prepare a sound quantum strategy as the world inches closer to wider adoption.   

How business and tech leaders can prepare for a quantum future

Despite these hurdles, organisations are already investigating how quantum computing and related technologies will impact their businesses. In the near term, quantum computers may be able to solve certain optimisation, machine learning, and simulation problems faster or more accurately than classical approaches.

“Organisations are still investigating how and when quantum computing and other quantum technologies will affect their businesses,” Buccholz tells us. While almost all industries will eventually be impacted, some – like financial services and transportation – will feel the impact sooner rather than later.

“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.
• Quantum computers running QML algorithms are currently able to train to higher accuracy with less data than their comparable classical counterparts. While the scale of problems that can be solved today with QML algorithms, the scale is increasing and the range of applicable problems is growing. In the near term, QML-based systems may be suitable for problems where high predictive accuracy is critical and time to solution is not as important.
• Quantum computers appear to be incredibly well-suited to running Generative Adversarial Networks, a type of generative AI used for a variety of topics including synthetic data generation. 

“While quantum computers won’t likely be running large language models any time soon, there is significant potential in improving many areas of traditional AI and machine learning. Pairing quantum with these other emerging technologies creates even greater opportunities for workflow efficiencies and security safeguards. 

“In certain industries, like manufacturing, automotive, aerospace, and cybersecurity, AI and quantum can work together to develop seamless supply chain operations, optimised transportation/fleet routes, and more impenetrable infrastructure.”

Quantum technology set to impact cybersecurity  

In addition to its advantages, future quantum computers are likely to pose threats to our existing digital signatures and encryption techniques. “This means that we will soon need to start the process of upgrading how we secure valuable information across enterprise IT systems. Leaders in all industries need to prepare for a quantum future – one where not only scientific boundaries are pushed, but cyber threats increase too.”

On the other hand, the sensitivity of QML algorithms may also help secure enterprises against cyber threats. “By helping monitor the incoming streams of events and activities, combined with other advanced monitoring techniques, quantum computers could improve our ability to detect and react to cyber threats as they occur.”

Upskilling will be key in determining quantum’s success

Given how different it can be to work with quantum computers, it is likely that future organisations will be seeking quantum information scientists the same way today’s organisations look for data scientists, Buchholz says. “Similarly to data scientists, upskilling existing resources is a significant investment that can require years. Happily, most organisations have passionate individuals who are exploring quantum computing on their own time and whose enthusiasm can be channelled. 

“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. While quantum solutions for enterprises are still gaining traction, the need to start developing talent, teams, and people who understand the basics of the technology is going to be extremely important for those looking to be the first to adopt quantum technologies into their workflows.

“20 years ago, graphical processing units (GPUs) were special chips that accelerated the graphics in computer games,” Buchholz concludes. “Today, those same chips are the foundation for Generative AI – something that was outside of most of our wildest dreams. What could the impact of quantum computers look like in a few short decades?”

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