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Using quantum computing to make better human decisions

Paul Martin, Quantum lead at PA Consulting explains how quantum computing can be pressed into practical service by business decision makers

Mention quantum computing and most business leaders will assume it is all about algorithms and technical systems.  Few will recognise the critical role it can play in helping them to make better business decisions.

Leaders have to make and communicate high quality business decisions and are personally measured on how effective they are. High value decisions are always in areas where there are complex trade-offs including resources, rewards, timescales, and risks (sometimes to life). These are often made using the responsible leader’s experience, expertise, and communication skills and usually under time pressure. Having the ability to quickly explore impacts of potential decisions makes a substantial difference to both the quality of the decision, and the effectiveness with which that decision is communicated. Quantum computing can help with both these elements.

The first advantage it offers is around strategic planning where decision makers need to explore different options. For example, they must understand how they increase resource to reduce risk, increase reward and improve timescales or mitigate the risk by balancing timescales and resources. This gets progressively more difficult as the complexity increases or where there are deadlines, or the leader simply gets tired.

QC can be used to provide decision support tools which deploy quantum specific algorithms to help leaders explore the options. These offer the opportunity to vary the individual parameters included in the RRTR to understand the trade-offs. They can be implemented alongside a conventional planning tool where each RRTR parameter can be varied, for example, on a slider so the amount of resource can be increased and the effect on risk, reward and timing fed back. Similarly, it is possible to experiment with the effect of increasing risk. QC can process these complex trade-offs and provide an optimised result in a reliable and repeatable time of tens of seconds.  In contrast, classical algorithms are often unpredictable in the amount of time they take to reach a solution, ranging from minutes to hours. This reliability and repeatability of returning a result in the same number of seconds each time is very important to decision making.

The other area where QC comes into its own is in tactical replanning. For example, a business will often implement a plan which is up and running but then inevitably disruption occurs as resources become unavailable, or risks appear.  The plan then needs to be changed and often this has to be done fast. For example, a rail network with a breakdown at a critical junction, needs to decide, with the current resource availability and positioning, what is the fastest way to organise resources to re-establish a good service? Or in a manufacturing plant with an outage, leaders will need to work out the best ways to get back to operating and reduce damage to equipment. These are time critical issues and decisions have to be made within minutes as delay costs money.

The QC optimisation process can be run with the new situation and the solution provided within a few seconds. The responsible leader can also vary the RRTR trade-off to blend experience with the QC optimisation to inform the decision.

A further area where QC is useful is in learning from the planning and replanning process. It can be used to ask questions such as, for the top five events that regularly occur in operations, will increased investment or positioning of resources reduce recovery time?

The final point is to emphasise the way QC coheres improvements in leadership decision making, communication and effectiveness. Using a QC, a leader can be confident in their decision and say, “I have explored the decision and traded off the achievement of objectives and risk to the team and this is what we’re going to do”.  This empowers both the leader and team members who know that the leader has explored the trade-offs in detail and can explain the rationale for the decision. The team can then ask questions and expect detailed answers. Compare this with a conventional decision where a leader just says, “Based on my experience we’re going to do this”. This is not as effective a communication as team members spend time wondering “why” as opposed to understanding the objectives, the reasoning and getting on and, importantly, being able to improvise if the situation arises.

In the right application, it is clear that QC can improve the quality and effectiveness of decision making.  Yet, too many businesses still see QC as a technology of the future and do not recognise how it can improve what they do today.  They need to understand that access to QC is straightforwardly available through cloud services such as AWS and Azure and at a very reasonable cost. With the right support, it transforms decision making to the benefit of leaders and their organisations.

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