Quantum Computing has the potential to be revolutionary in many computation-heavy area’s: ranging from drug discovery to financial applications. The reason? Higher accuracy and faster computation times. However, one question is often neglected: at which cost? We’ve seen that supercomputers and data centres can consume an enormous amount of energy [1,2]. Will quantum computers be the next energy-thirsty technology, or are they instead the gateway to a green computing era?

Quantum computing uses the most intriguing properties of quantum physics: entanglement, superposition, and interference. Quantum computers use these phenomena to do calculations in a completely different way than normal computers do. The result is an enormous speedup of the calculations, the ability to achieve higher accuracy levels, and solve problems that are intractable for the classical computer.

These quantum phenomena take place at a very small scale: the scale of an electron. As such, one computer calculation would barely cost any energy. However, to observe these potent quantum phenomena, the system must be completely isolated. Temperatures must be cooled to near absolute zero (-273 degrees Celsius). This comes with a large energy bill.

The energy consumption of a quantum computer scales fundamentally different from a classical computer. Classically, there is a linear scaling with problem size and complexity. For quantum computers, this may be very different. Insight into this new energy consumption of a quantum computer is essential for a green future of quantum computing.

The Scaling of the Energy Consumption

Currently, the power consumption of a quantum computer is about 15-25kW, due to the cryogenic refrigerator [3, 4, 5]. This is comparable to the energy consumption of about 25 households. Note that this power is not only consumed when a calculation is performed but is continuously consumed by the quantum computer. This leads to a large energy bill.

There is hope for the future. When a classical computer becomes twice as large, it requires twice as much energy. In the near future, a quantum computer, by contrast, may barely increase its energy consumption when scaling up. This is because the cooling volume barely increases, and heat created by extra electronics is also not expected to be significant. The largest quantum computer today is 127 qubits and scaling to 1.000 or even 10.000 qubit is possible with similar energy consumption.

In the far future, we envision quantum computers with millions of qubits, situated in large data centres. It would be naive to assume that this does not add any energy costs. Recent research shows that the energy costs will scale with the number of qubits and operations at a point in the future. This is mostly due to increased cooling costs.

There is another very important factor that positions quantum computers as potential candidates for green computing. The idea is as follows: if you must run a supercomputer for a month to solve a specific problem and a quantum computer can do it within minutes – this drastically reduces the energy cost. An example of how energy costs would scale differently for Monte Carlo simulations is shown in figure 1.

Figure 1: The Energy Consumption of a Quantum Computer scales very differently than that of classical computers. When high accuracy or complexity is required, the quantum computer may become the more “sustainable” candidate.

Recent research shows a difference in energy consumption between quantum computers and classical computers of a factor of 10.000 (!) [4]. A clear quantum energy advantage, but for a toy problem, favouring the quantum computer. The question remains whether this is applicable to more generic problems.

Recently, an energy estimate for a more generic problem was made, namely breaking the RSA encryption [6]. RSA is a very common encryption method for secure data transmission. The quantum computer is expected to have an energy consumption of 1000 times as little as a classical computer. It must be noted that this energy estimate was based on futuristic full-stack quantum computers, and still require major advances in quantum hardware.

Interestingly, this estimation also showed the timeframe where a quantum computer might be slower but requires less energy [6]. This gives a great perspective for the future. Before implementing quantum computers due to their speedup, can we implement them for green computing?

Green Computing for Financial Institutions

At Capgemini, the Olive project researched the opportunity of using quantum computers for green computing in the financial industry. This is specifically applied to using quantum computers for pricing derivatives, based on a new algorithm that allows one to do this on a quantum computer [7,8]. (See more here)

Green Computing is becoming increasingly important for financial institutions. Mischa Vos, Quantum Lead at Rabobank (one of the largest banks in The Netherlands), emphasises its importance for Rabobank:

“At Rabobank, sustainability is an integral part of our corporate mission: “Growing a better world together. The focus is now on green coding and sustainable data centres. On top of that, Rabobank is investing in green computing technologies. Quantum Computers would be an interesting new candidate.”

Financial institutions use an enormous amount of computational power to ensure security, price financial products and perform risk management. Based on the insight about the “quantum energy advantage”, quantum computing can reduce the carbon impact of these computations. Would this be interesting for Rabobank?

“This has great potential for Rabobank. Running these calculations, especially when Artificial Intelligence is involved, has a negative impact on the carbon footprint of Rabobank. Rabobank is dedicated to reducing this. At the same time, as a financial institution, we still need to perform accurate risk analysis and provide security. If quantum computing would allow us to combine the two, this would be very interesting.”  

There may be a timeframe when the quantum computer is slower, but more energy efficient than classical computers. Would Rabobank already be interested in quantum computers at this stage?

“There are certain batch-oriented calculations that Rabobank performs, and these would be ideal for this. For example, evaluating the risk portfolio of investments at a large scale, or certain fraud detection methods. There will definitely be opportunities where Rabobank can already use the slower, but more efficient quantum computers during this time frame.”

A future scenario

The current hardware limitations are the main bottleneck for practical quantum computing. However, it is important for financial institutions to be ready for implementing quantum computers when the time is right, especially when this can be important from a sustainability perspective.

Phase 1. Research & Development

The current hardware limitations are the main bottleneck. As such, firstly, the hardware challenges need to be overcome before it becomes feasible to run relevant calculations on quantum computers. The Quantum Energy Initiative points out it is important to already make conscious design choices during this phase to ensure an energy-efficient quantum computer [9,10]. This should not slow down technological progress but instead, prepare for long-term energy advances.

Phase 2. Green Energy Advantage

Due to slow quantum clock speeds, and intensive quantum error correction codes, the quantum computational advantage can take longer than the quantum energy advantage. As such, the first applications of quantum computers may be due to their energy efficiency. This will be dependent on the specific advances in quantum hardware.

Phase 3. Overall Quantum Advantage

Finally, both the quantum computational advantage and quantum energy advantage are achieved. Here, it is important to make conscious choices in the usage of quantum computers and avoid the Jevon paradox. See for example this blog on quantum for sustainability. On the other hand, this is also the phase where quantum computers can really make a difference in sustainability – making better simulations leading to better material design all the way to general climate crisis mitigation plans [11]. 

Technology leaves an indelible mark on the environment. Capgemini is determined to play a leadership role in ensuring technology creates a sustainable future. Capgemini can help with implementing sustainable IT as the backbone of a company for a greener future.  It is important to consider the environmental footprint of emerging technologies. Capgemini’s Quantum Lab can help clients understand the future possibilities of quantum technologies and build their organization and strategy that will make the potential become a reality. With this project, more insight into the real environmental cost of quantum computers is acquired, as well as the opportunities that Quantum Computers can give for green computing.

For more information on the results of Milou’s research, watch the webinar here

References:

[1] IEA, Data centres and data transmission networks, 2022. [Online]. Available: https://www .iea .org/reports/data-centres-and-data-transmission-networks .

[2] A. S. Andrae and T. Edler, “On global electricity usage of communication technology: Trends to 2030,” Challenges, vol. 6, no. 1, pp. 117–157, 2015. .

[3] F. Arute, K. Arya, R. Babbush, et al., “Quantum supremacy using a programmable superconducting processor,” Nature, vol. 574, no. 7779, pp. 505–510, 2019.

[4] B. Villalonga, D. Lyakh, S. Boixo, et al., “Establishing the quantum supremacy frontier with a 281 pflop/s simulation,” Quantum Science and Technology, vol. 5, no. 3, p. 034 003, 2020.

[5] Personal communication with Olaf Benningshof, Cryoengineer of QuTech, 2023.

[6] M. Fellous-Asiani, J. H. Chai, Y. Thonnart, H. K. Ng, R. S. Whitney, and A. Auffèves, “Optimizing resource efficiencies for scalable full-stack quantum computers,” arXiv preprint arXiv:2209.05469, 2022.

[7] P. Rebentrost, B. Gupt, and T. R. Bromley, “Quantum computational finance: Monte carlo pricing of financial derivatives,” Physical Review A, vol. 98, no. 2, p. 022 321, 2018.

[8] N. Stamatopoulos, D. J. Egger, Y. Sun, et al., “Option pricing using quantum computers,” Quantum, vol. 4, p. 291, 2020.

[9] A. Auffeves, “Quantum technologies need a quantum energy initiative,” PRX Quantum, 3(2), 020101., ISO 690, 2022.

[10] quantum-energy-initiative.org [11] Berger, Casey, et al., “Quantum technologies for climate change: Preliminary assessment,” arXiv preprint arXiv:2107.05362, 2021.

The term ‘dark factory’ can be thought of as metaphorical, for example, the factory might not actually be completely dark – its machines may require some light, if equipped with optical sensors.

Dark factories are a part of the global digital transformation and move to the Industrial Internet of Things (IIoT), which is being driven by increasingly capable robotics and automation, AI and 5G connectivity. In this article, we’ll discuss the benefits, challenges, and how companies can move forward with this concept.

Pros and opportunities

Dark factories offer a number of benefits.

• First among them is increased efficiency and productivity. Dark factories are favourable on classic efficiency drivers such as production output, for example, offering 24/7 capacity beyond traditional shift hours – and they are unaffected by the human need for breaks, vacations, or sick days. And a secondary benefit is that dark factories do not need to be located near a labor pool – which means they can be set up in other areas, exploiting opportunities like cheaper land prices or more attractive surroundings.
• This also makes them more sustainable. Dark factories can be designed to be more energy-efficient and environmentally friendly than traditional ones; an obvious example of this is that they can do away with lighting and central heating.
• All of that means decreased operating costs, due to a reduction in non-added value tasks and staff numbers, a benefit which is especially prominent in high labor cost areas.
• It also improves worker safety. Fewer workers present means reduced risk of accidents and injuries in the workplace, a significant challenge in hazardous environments. Moreover, repetitive and physical tasks can be monitored (and assisted) to avoid safety issues or future physical disablement.  
• Finally all this can lead to improved quality as well as performance. Highly specialised machines monitored by a new generation of integrated industrial information systems work with the kind of efficiency that a human cannot match. They can also provide relevant recommendations to the operator, to avoid mistakes or support decisions (eg. to recycle the product or anticipate corrective actions).

Cons and challenges

There are, of course, some shortcomings.

• Whether retrofitting an existing brownfield facility or building a greenfield one from scratch, the CAPEX required to create a dark factory is considerable – new infrastructure is required and existing infrastructure may require modification. As is obvious, there are a number of technological barriers to overcome also, for example – AI, ML, 5G, robotics and system integration. These questions should be addressed with a clear vision of the future industrial platform and/or footprint, in order to avoid any “techno push” (a risky approach in which new products and services are driven by new technology and not validated by existing market needs).
• Additionally, dark factories will necessitate new training and staffing requirements.It’s clear that new specialist skills will be required in order to design, install, maintain and operate the systems that will run these plants.
• Suitability, scalability and over-specialization form another issue. Humans are still better at many tasks, and not all processes can be automated (yet). It may be a long time before dark factories are suitable for certain types of manufacturing. For example, it’s more difficult to build generalized (as opposed to specialized) automated systems and processes. This may limit a manufacturer’s ability to quickly respond to changes in production requirements. Here, we require AI sophisticated enough for generalized problem-solving (without human aid). For example, the automation of quality control is a particular challenge.
• Technological dependence is another issue that must be planned for. Cyber-driven industrial espionage is already a serious problem in conventional factories. The sheer connectivity of dark factories creates security vulnerabilities that could be exploited by malicious actors. This could result in data breaches, production disruptions, or worse. In addition, any non-malicious technical failures could result in major production delays without rapid human intervention.

The new human structure of the Dark Factory

How might humans fit in this new environment?

Lean manufacturing taught us that we could cut out much of middle management and improve the efficiency of operations. A dark factory could cut the bureaucracy further. Broadly speaking, the dark factory means fewer people in total, but more added value per person.

Consider the ’enhanced operator’ – which could be an XR-equipped human who makes periodic visits to the facility. Instead of a person with specialist skills on one part of an assembly line, this enhanced operator would be a generalist, with a very broad understanding of the factory’s E2E processes and systems.

Headcount may reduce, but collaboration will still be key. First – collaboration between teams to understand systems, engineering, impacts on manufacturing, impacts on operations and how to handle complex situations. Second – collaboration between robots and humans, to perform complex tasks requiring both capabilities.  

Darkening the factory: what now?

Implementing a dark factory (either from scratch or by retrofitting an existing facility) will not be easy. And, the pace of transformation is sector dependent. For example, it is easier to completely automate simple and repetitive tasks, ones in which every step in the end-to-end process is understood, down to the movement and the millimeter. But not all kinds of manufacturing are quite so straightforward. As companies progress the concept, here are some steps to consider.

A transformation roadmap and change management plan

Identify the steps you need for your transformation roadmap. Is now the right time? Transitioning to (or constructing) a dark factory requires a significant investment of time, resources, and capital. It’s important to carefully evaluate the potential benefits and risks of this transition before making any decisions.

Conduct a thorough analysis of the existing manufacturing processes to determine which ones can be automated and which cannot. Is it still worth it, in light of this?

If so, you may need to work with a recognized specialist company to determine which technologies will be most effective for your specific manufacturing process. The transition could also be phased – for example, a partially automated factory could run a ’dark shift’ overnight, which could provide a test or proof of concept.

And of course – build cyber security into the plan, not as an ‘afterthought’. The dark factory’s level of connectivity (and potential vulnerabilities that result) requires it.

Consider the human implications

How can we keep humans safe in this new (mostly) non-human environment? What safety measures are required – for example, can you create areas that are safe for people to traverse? And how must people behave in a space built primarily for robots, not humans? 

Anticipate and prepare for workforce transformation: think about recruiting for the skills needed for tomorrow. What will be done about those who may lose their job to a robot – can they be retrained and retained?

Consider future operations: flexibility and scalability

As previously mentioned, people are more flexible than robots and machinery. As such, forward planning must consider how the infrastructure will flex and scale, in order to meet future market needs. Detailed monitoring and analytics can help here, identifying what systems can be optimized or replaced.

Dark factories, bright future?

The fragility of global supply chains has become increasingly apparent in recent years – Russia’s 2022 invasion of Ukraine, and the COVID-19 pandemic, in particular, have demonstrated the need to ‘onshore’ (bring back) manufacturing, so as not to be dependent on foreign sources of vital goods.

But manufacturing was, of course, originally ‘offshored’ because it was cheaper to do the work abroad. Dark factories could be an equalising force – bringing down costs so goods can be produced back at home.

It’s also important to consider that fully automated factories have been tried previously, with varying degrees of success. There are a few cautionary tales; IBM tried its own in the 1980s, but closed it because it wasn’t able to respond to changing market needs. Apple also built such a plant in the 1980s, but closed it in the early 90s – likely because the plant was unable to deal with increasingly smaller components. More recently, Tesla walked back some of the automation at its Fremont CA facility, when machines failed to meet its ambitious manufacturing targets. This shows us the importance of flexibility and forward planning.

That said, successful dark factories do exist today. In perhaps the best example, robotics manufacturer, FANUC (Fuji Automatic NUmerical Control), operates a lights out facility in Japan. Here, complex robots assemble other complex robots, with zero human involvement in the manufacturing process.

As the previous examples demonstrate, success with a dark factory can be difficult – but is possible. Dark factories offer transformative benefits in terms of cost efficiency, sustainability, safety, and supply chain resilience. They also offer a considerable competitive advantage to those who ‘get there first’, who get it right and, returning to Philip K Dick’s Autofac, keep control in human hands.

The progress in quantum computing is accelerating

The first quantum computers were introduced 25 years ago (2 and 3 qubits), the first commercially available annealing systems are now 10 years old. During the last 5 years, we have seen bigger steps forward, for example systems with more than twenty qubits. Recent developments include the Osprey chip with 433 qubits by IBM, first results of quantum error correction by Google, as well as important results in interconnecting quantum chips announced by the MIT.

From hype to realistic expectations

Where some see steady progress and concrete steps forward, others remain skeptical and point out missing results or unkept promises—the most prominent of which is found in the field of the factorization into large prime numbers: There still is a complete lack of tangible results in breaking the RSA cryptosystem.

However, development in quantum computing has already passed various important milestones. Dismissing it as mere hype that will pass eventually now becomes increasingly difficult. In all likelihood, this discussion can soon be laid to rest, or at least refocused towards very specific quantum computing frontiers.

The domain of machine learning has a natural symbiosis with quantum computing. Especially from a theoretical perspective, research in this field is considered fairly advanced. Various research directions and study routes have been taken, and a multitude of results are available. While much research is done through the simulation of quantum computers, there are also various results of experiments run on actual, non-simulated quantum devices.

As both the interest and the potential of quantum machine learning is remarkably high, Capgemini and the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, have delved deeply into this topic. On request of the German Federal Office for Information Security (BSI), we went as far as analyzing the potential use for, as well as against, cyber security. One of the major results of this collaboration is the report “Quantum Machine Learning in the Context of IT Security“, published by the BSI. Current developments indicate that there is trust into quantum machine learning as a research direction and it’s (perceived) future potential.

Laggards increasingly lack opportunities

The ever-growing availability of better and more efficient IT technologies and products is not always reasonable to implement and often difficult to mirror in an organization. Nevertheless, innovation means that a certain “technology inflation” constantly devalues existing solutions. Therefore, an important responsibility of every IT department is to keep up with this inflation by implementing upgrades and deploying new technologies.

Let us consider a company that still delays the adoption of cloud computing. While this may have been reasonable for some in the early days, the technology has matured. Over time, companies that have shied away from adoption have missed out on various cloud computing benefits while others took the chance to gain a competitive advantage. Even more, the longer the adoption was delayed or the slower it was conducted, the further the company has allowed itself to fall behind.

Time to jump on the quantum computing bandwagon?

Certainly, quantum technology is still too new, too unstable, and too limited today to adopt it in a productive environment right away. In that sense, a pressure to design and implement plans for incorporating quantum computing into the day-to-day business does not exist today.

However, is that the whole story? Let us consider two important pre-implementation aspects: The first of these is to ensure everyone’s attention for the topic: For an eventual adoption, a widespread appreciation for what might be gained is crucial to get people on board. Without it, there is a high risk of failing­—after all, every new technology comes with various challenges and affords some dedication. But developing the motivation to adopt something new and tackle the challenges takes time. So, it’s best to start early with building awareness and basic understanding of the benefits throughout all levels and (IT) departments.

The second aspect is even more difficult to achieve: experience. This translates to know-how, participation, and practice within the organization to get prepared for the adoption of technologies once they are ready for productive deployment. In the case of quantum computing, gaining experience is harder to achieve than with other recent innovations: In contrast for example to cloud computing—which constitutes a different way of doing the same thing, and thus allows companies to get used to them slowly—quantum technologies represent a fundamentally new way of computation, as well as a completely new approach of solving problems and answering questions.

The key to the coming quantum revolution is a quantum of agility

Bearing in mind the scale of both pre-implementation aspects and of the uncertainty of when exactly quantum is going to deliver advantage in the real world, organizations need to start getting ready now. On a technical level, and in the realm of security, the solution for the threat of quantum cryptanalysis is deployment of post-quantum cryptography. However, on an organizational level, the solution is crypto agility : having done the necessary homework to be able to adopt quickly to the changes, whenever they come. Applying the same concept, quantum agility represents having the means to adapt quickly to the fundamental transformations that will come with quantum computing.

Thus, building awareness and changing minds now will have a considerable pay-off in the future. But how can organizations best initiate this shift in mindset towards quantum? Building awareness is a gradual process that can be promoted by a working group even with small investments. This core group might for example look out for possible use cases specific to the respective sector. Through various paths of internal communication, they can spread the information in the proper form and depth to all functions across the organization.

To build up knowledge and experience, the focus should not be on viable products, aiming to replace existing solutions within the company. Instead, it is a way of playing around with new possibilities, of venturing down paths that might not ever yield any tangible results but aiming to discover guard rails subjective to each corporation and examine fields where quantum computing might eventually be the way to substantial competitive advantages.

Frontrunners are gaining experience in every sector

For example, some financial institutions are already exploring the use of quantum computing for portfolio optimization and risk analysis, which will enable them to make better financial predictions in the future. Within the pharma sector, similar efforts are made, gauging the potential of new ways of drug discovery.

In the space of quantum cyber security, together with the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Capgemini has built a quantum demonstration: performing spam filtering on a quantum computer . While this might be the most overpriced—and under engineered—spam filter ever, it is a functioning proof of concept.

Justifying investment in quantum computing requires long-term thinking

The gap between companies in raising organizational awareness and gaining experience with the new technology is gradually growing. Laggards have a considerable risk of experiencing the coming quantum computing revolution as a steamroller, flattening everyone that finds themselves unprepared.

The risks and challenges associated with quantum technology certainly include the cost of adoption, the availability of expertise and knowledgeable talent, as well as the high potential of unsuccessful research approaches. However, the cost of doing nothing would be the highest. So, it’s best to start now.

We don’t know when exactly the quantum revolution will take place, but it’s obvious that IBM, Google and many more are betting on it—and in the Capgemini’s Quantum Lab, we are exploring the future as well.