Quantum Computing: Why you should care (in less than 30 minutes)

This is section 2 of 3 from my recent eBook around the business relevance of quantum computing.
The previous chapter (see here) gives a brief overview of what Quantum Computing is. This section talks about what it means in reality. The final section talks about quantum readiness and makes clear why managers and executives need to start understanding this today, even if their first steps are small.
Why is this actually useful?
Relevant problems for you and your colleagues
From a layperson’s point of view, there are several families of problems that quantum computers are already considered well suited to solving. Over time we will identify many more.
Combinatorial problems. This is where we interrogate a massive number of intertwined numbers all at once. The actual volume of initial data is typically not large (not in terms of ‘big data’ analysis, which is broadly unsuited to quantum computers) but what is large is the number of combinations. See two examples that you can extrapolate off in the context of your industry and company.
Simulation of physical processes. Given that quantum computers reflect the quantum nature of our universe it is unsurprising that they are suited to modelling what goes on in that universe; including simulating the interaction between molecules. Currently, popular simulation topics include the folding of proteins in the design of new medicines, the identification of the best materials for new battery design (in the context of electric vehicles), and the modelling of catalytical processes; for example, the fabrication of fertilizer. Some of these are detailed further below through the industries lens.
Quantum Machine Learning. Of increasing interest, but relatively harder for a non-technical executive to get their head around is "QML". Machine Learning is of course a technology that we all nod enthusiastically when mentioned in a meeting but would struggle to actually explain to our great aunt Geraldine! It is clearly a technology that is here to stay and what we will do with it is becoming increasingly sophisticated and valuable. A key aspect of Machine Learning is the training of the tools using appropriate data sets. Quantum Computing can help with the creation of those data sets and thus the training of the machine, in particular the increasingly important field of "non-supervised learning".
Examples of combinatorial optimization problems
As you continue your journey you will come across references to several problems. These include the Travelling Salesperson Problem and the Knapsack Problem. These are simple to understand (even if you do need a quantum computer to solve them!)
The Travelling Salesperson Problem
A salesperson needs to visit N cities, the journeys between which each have one or more different costs (time, distance, expense). What is the optimal route to take?
As well as the obvious challenges for salespeople, travel and logistics firms, etc, it becomes equally relevant for optimizing warehouse layout, the manufacturing of printed circuit boards, and many, many other activities involving movement.
In tech language this begins as "what is the shortest distance between the nodes?" but if you are considering more than just distance – e.g., time and fuel efficiency – it rapidly becomes a "hard" optimization problem. Each additional node increases the calculation by an order of magnitude, to the point where optimising for a large network is unfeasible for classical computers. However due to the different way they work – effectively considering all options in one go – quantum computers eat this kind of problem for breakfast.
The Knapsack Problem
This problem simply states given a set of items how do you optimize within a given limit – e.g., to maximise what you can get in a knapsack while satisfying constraints such as maximum weight, no-more-than-x-of an-item, etc. This family of problems then becomes meaningful more complicated if you consider the order you add the items, e.g., to optimize unloading of a multi-stop parcel delivery.
The number of variables rapidly makes this impossible to optimize with a classical computer, especially if you are needing to deal with e.g., hundreds of trucks a day. However, it is ideally suited to the way a quantum computer calculates problems
This is relevant for every industry. Yes, even yours!
It might sound trite to state Quantum Computing will impact everything. However, even the briefest consideration of use cases does suggest that looking 10–30 years out every aspect of our lives will have been optimised by QC in some form or other. Now, as covered in the introduction, these changes will not all be obvious, and many will only become apparent in later waves of Innovation. How many people would have predicted 20 years ago that we would use the signal from people’s cell phones to identify where there is congestion on roads? While that may now be a well-understood example, how many of us are aware of work to use the interference of rain on those same cell phone signals to give hyper-local weather information?
So, yes, it is clear everything will be touched, but not at the same speed and likely only in small ways initially. We are still at the point that there is more discussion around where QC should be focused than examples of real, impactful work.
It’s not difficult to realise that those industries where complex combinatorial problems are common will be impacted at some point, but the timeline-to-value of the technology will also change the visibility of efforts and investments. You should also expect that the level of publicity will vary by company and by industry (and by who is funding the work), so simply paying attention to headlines will not be an effective way of tracking progress. Below are some of the anticipated activities in the near and medium-term.
Pharmaceuticals, Life Sciences, Medicine and Healthcare
At the heart of new drug development is testing how protein molecules interact. Powerful enough quantum computers will model this in a way that has never previously been possible. This will not simply speed up the traditional approach but offer a paradigm shift in the way scientists and researchers approach the problem. This will lead to more new medicines and a shorter development cycle.
It is similarly anticipated that more detailed models of viruses’ interactions with the human body can be created. A very topical challenge at the moment, and one that should allow a faster, more sophisticated approach to future pandemics.
Richard P Feynman
The potential for quantum machines to be useful in simulating natural systems was first memorably postulated by Richard Feynman in his seminal lecture "Simulating Physics with Computers". Feynman, a leading 20th century American theoretical physicist, stated "Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy."
Chemistry and material science
Much as with life sciences, the simulation of inorganic chemical processes and the development of nanotechnology materials will benefit from the ability to use Quantum Computing driven models. Being able to take a fundamentally different approach to simulations will speed up the current techniques and allow new approaches, generating a future wave of new materials.
Automotive, Logistics and Travel
The automotive industry may not initially seem an obvious candidate for quantum computing, however through high-profile partnerships between Volkswagen and both D-Wave and Google, attention has been brought to two critical topics. One is battery technology, the other route planning. Quantum London had the good fortune of hearing from the ex-CTO of Volkswagen who spearheaded much of their early quantum work, and it was clear there was a genuine belief that this is a critical part of the company’s technology innovation strategy.
The battery technology point links to the above mention of material science. With VW committed to an electric vehicle future, it is looking for ways to increase battery capacity, decrease manufacturing cost and reduce the usage of components that are uncommon or risk environmental damage. All other vehicle manufacturers are logically looking for the same. Batteries cost up to a third of the price of the vehicle, and the short-range they currently offer is a major blocker to uptake. As such this might be the most overtly commercial driver of quantum computing supported material science research.
Also of importance is quantum-enabled routing optimization. We’ve all seen the massive improvements in ‘satnav’ routing over the past two decades. We’ve also all experienced the frustration when every car is routed around a hold-up in the same way, simply causing a second pinch-point. While we are some way from having real-time quantum-computing-driven updates for each of our journeys, the use of quantum computing to develop a range of different routes, that can then be selected real-time by a classical computer will already make a difference. This topic naturally fits also with the logistics industry on a day-to-day basis.
For airlines, the optimisation of routes and equipment is critical and complex. It is normally done on an infrequent basis given the computation effort and will rarely be truly optimized. Quantum computing will allow this to be done more frequently and with a greater degree of optimization. At one level this will be hidden from individual flyers, though there will be costs savings that might be passed on. What will be more visible will be the reduced chaos after a major incident forces planes to divert or be grounded. Currently, there is no easy way to hour-by-hour optimise plans with hundreds of aircraft, thousands of passengers and a lack of clarity around the constraints. In the future quantum computers will allow airlines to minimise the impact of such problems.
Information mgmt.
Data analytics is not expected to be a priority for quantum computing, despite some commentators conflating QC’s computation capabilities with a suitability to do many analytics tasks. Where it comes to searching through "databases" however, QC will be relevant. In most cases, the time taken by a classical computer is low enough that most companies will not see immediate use cases, but for those doing structured searches of massive datasets, or who would like to do more sophisticated searching than is currently feasible, then there will be significant opportunities.
Financial Services
Banks are typically in the front line of any new technology given their scale, sizeable IT expenditure, and tech-comfortable teams, as well as the clear value in marginal improvements in many areas.
While banking trials of quantum computing are typically being managed discretely and confidentially it is clear enough what some of the relevant topics are. At the forefront of a lot of effort is the topic of portfolio optimization. Not only does 0.05% on $10 billion pay easily for your initial QC spend, but as we’ve all experienced, the best way to get a big project budget signed off is to show real financial benefit from early pilots.
This ‘very small part of a very large number’ value point does also mean that unlike many other technologies the neo-banks are unlikely to be at the front of quantum adoption. They have simply not yet accumulated enough assets for micro-% gains to make a difference.
So far there is limited engagement from the insurance industry. This is understandable given the main areas of pricing risks and managing claims do not obviously benefit from the early opportunities from quantum computing. One area that will likely become relevant is the modelling done around hurricanes, wildfires, and floods which, much as weather forecasting models, will benefit from QC.
Combating climate change
As well as taking an industry view it is interesting to see themes. This is particularly relevant when engaging governments, NGOs and other bodies for support and funding.
With sustainability very much part of the "build back better" agenda for 2021 it is not surprising that plenty of work is being done to link QC to efforts to reduce energy consumption. The actual opportunities fall into the various categories we have defined above but taken together paint a compelling picture of why a technology-led road to sustainability must include quantum computing.
The worrying truth however is that the time horizon for powerful enough quantum computers to make a real difference is longer than we have when it comes to managing the climate crisis. As such, while quantum computing will play a major role in the medium term, anyone touting it as a rapid saviour of our currently heating world is being disingenuous.
The broadly discussed themes include:
- New catalysts to reduce the energy cost for manufacturing fertilizer (e.g., the Haber-Bosch process for producing ammonia fertilizer burns 3–5% of global natural gas), to store energy better, and to find ways of capturing carbon (e.g., in concrete)
- New materials that are more environmentally friend to produce and use (e.g., because they weigh less)
- Improved designs – e.g., computational fluid dynamics (CFD) to make ships, planes, trains, etc which require less fuel
- Optimised logistics and travel – to reduce the distance objects need to be moved
As 2021 progresses and attention moves from Covid to Climate Change it will be interesting to see how Quantum Computing sits into the conversation.
Urban planning
Smart devices are now giving urban planners near-infinite data about how humans and their vehicles use cities. There is too much of this data for traditional optimization simulations and so quantum has a role to play. Whether it is modelling traffic signal sequencing, management of roadworks, or identifying how to get bus, tram and train schedules to synchronise, there is a wealth of opportunity that will become increasingly important as cities get ever more crowded and we wish to reduce the footprint of every journey.
Everything else
The above are simply examples. Consider similar activities in your industry and how much business advantage optimisation might bring.
Thank you for reading this section. Continue via the links below or through this link. Or read the ebook (US link, UK link)
- Introduction (link here)
- Why QC is useful and potential business applications (this post)
- Why busy executives should engage with the topic (link to be added once published)