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Why invest in quantum computing? Ask The Box at Alice & Bob

Management consulting firm McKinsey has projected that quantum computing could unlock value of up to 1.3 trillion dollars across four key industries: chemicals, life sciences, automotive, and financial services [1]. Even more value can be expected when applications in other industries are considered. The dizzying effect that the size of the economic opportunity creates is matched by the velocity at which breakthroughs in the technology are happening.

What drives this trillion-dollar market that quantum computing could open up? The main point is that a quantum approach to information could speed up some computations, such as the simulation of molecular processes and fluid dynamics, optimization, forecasting, and machine learning. Additionally, it is expected to impact the all-important area of decryption, where it will transform the field of cyber security.

Every business should be asking whether this will affect their activity. Potential use cases across industries can be addressed with quantum algorithms and every algorithm type can be applied in almost all industries.

In this table an overview of few major usecases, by industry sector and type of quantum algorithm

Below follows a detailed rundown of the highest potential business applications of the quantum algorithms from the table, but there are many other possible examples.

Enhance lab-based research with quantum molecular simulation

As pointed out by the physicist Richard Feynman, there could be no better means than a quantum computer to simulate chemical processes on the molecular level, particularly those involving large numbers of electrons. There’s no mystery: these are quantum processes. The potential for simulation concerns laboratory research across the board in the life sciences, energy, and advanced manufacturing industries. With the right algorithms, considerable savings could be expected while enhancing, and in some cases even largely replacing, laboratory experiments. This will generate crucial insights earlier, thereby compressing the time to market for new and game-changing products. Here are just a few examples:

  • Identifying alternatives to scarce, expensive, or hazardous raw materials.
  • Understanding chemical reactions and the role of catalysts to engineer cheaper, more energy-efficient production processes and thereby decrease CO2 emissions.
  • Gathering insights from natural and biological processes to craft cost-effective synthetic alternatives.
  • Pioneering new capabilities and functionalities for materials and chemical compounds; this includes new drugs, more efficient solar panels or batteries, room-temperature superconductors, on-chip sensors, and many more.
  • Understanding the aging and degradation of biodegradables or recyclables to develop more robust products and extend their life cycles.

Lower operating costs by quantum optimization

Better solutions could be identified faster to handle a broad range of optimization problems that are costly and challenging to solve using today’s classical computers. This would be an opportunity to improve operations and boost profit margins. Potential examples include: last-mile delivery, packing optimization, network optimization, and real-time routing optimization for autonomous vehicles.

Upgrade forecasting and risk management with Quantum Monte Carlo simulation

Quantum computing could make forecasting more accurate by simulating a multitude of potential scenarios. Whatever the activity, this can be harnessed to stress-test operations and optimize resource use, while limiting the risks. Whether it is derivative pricing, infectious disease propagation, value-at-risk calculation, strengthening supply chains, irregular operations management, or manufacturing process design, there are gains that could be realized.

Improve machine learning performance while shortening training times

Quantum machine learning offers an expansion of parameter space for training models. This is an opportunity to boost speed, lower energy consumption, and reduce the hardware footprint needed for training. It would enable pattern identification in high-dimensional data and facilitate forecasting even when data is limited or scattered.

Generate faster simulations with the HHL algorithm

HHL is expected to deliver faster results in fluid dynamics, aerodynamics, and thermodynamics simulations, and with a higher level of granularity. This concerns every sector from aerospace, automotive, electronics, and chemistry to energy. It would be used to shorten development cycles, cut development costs, and improve products through insights that were previously unattainable without real-world testing.

Revolutionize decryption

RSA cryptography and related encryption schemes, today’s standard for data and communication security, will all be broken by quantum computers. While this kind of applications are far in the future, any firm that rely heavily on cryptography should adapt fast. Companies requiring secure communication and data storage to ensure privacy or protect sensitive information like trade secrets and IP need to assess and improve the robustness of their current cryptographic protocols. One possible avenue is adopting post-quantum cryptography methods and ensure their timely implementation.

A swift ascent

The technological shake-up represented by quantum computing will reverberate throughout industry, with the most disruptive effects expected in pharmaceuticals, chemicals, energy, and advanced industries like automotive, electronics, and aerospace [1]. Moreover, this transformation is imminent, closer than most might anticipate, with logistics, finance, and the consumer industry set to experience its effects sooner rather than later.

Our research shows that a new synthetic production method for sustainable fuels would unlock anywhere between 400 and 600 million dollars in economic value. Other use cases we investigated: fuel production optimization and energy trading optimization, would generate 40 to 80 million dollars and up to 320 million dollars, respectively.

While the field of quantum computing remains firmly grounded in intensive research and development, its path to maturity more closely resembles the swift ascent of artificial intelligence than the seemingly interminable quest for nuclear fusion. In fact, hardware and software advances in the last few years have been moving forward hand in hand so fast that substantial economic gains are beginning to look like a real possibility before 2030.

Why is it important to be well prepared?

Here are five main considerations:

  • Moore’s law is alive and well in quantum hardware. Qubit counts have more than doubled each year, e.g., IBM has increased its qubit count from 27 in 2019 to 1121 in 2024 [2]. Going forward as quantum companies grow their hardware, chip foundries are expected to rise to the challenge.
  • Software is improving fast too. This concerns both algorithms and the design of more efficient error-correction codes. The aim is to use fewer resources to get the same results, or to get an earlier match between resource capabilities and hardware requirements. To give just one example: the first algorithm for quantum decryption in 2009 would have needed 6500 million standard superconducting qubits, which was cut by two orders of magnitude to roughly 20 million qubits by 2021 [3].
  • There is a vibrant ecosystem of vying technologies. New qubit technologies are ever more efficient and use fewer computational resources to run the same algorithm. Alice & Bob’s research using cat qubits reduced the resources needed to run Shor’s algorithm by a factor of 60 [4], and our latest publication on LDPC error correction codes showed how cat qubits further reduce the above requirement by a factor of almost 200, taking it to just under 100 000 [5]. This exploits the cat qubit’s inherent robustness to errors, drastically decreasing the qubit overhead needed for error correction, as illustrated in this image.
  • Software and hardware are moving forward together. Every hardware system has its own native ‘universal’ set of operations (called gates) that can be implemented directly. Universality here means that any gate can be implemented as a combination of other gates in the native set, but it also means that some gates require an extensive overhead, because they involve long sequences of native gates. When such gates appear frequently in an algorithm, the algorithm must be optimized by identifying more efficient ways to implement common gates directly. Another approach is to improve the qubit layout to allow efficient long-range gates, and it should be noted that an optimal qubit layout for such gates can give a factor of two improvement over straightforward solutions.
  • Many new companies have taken up the challenge to Big Tech. Upwards of thirty competing technologies have emerged and new scientific insights are being built into qubit designs all the time as newcomers find ways to incorporate the latest academic research, while still benefiting from the findings of those who came before them, e.g., in cooling and control systems.

Only early movers will benefit from the present opportunity

Quantum computing is likely to be with us much sooner than anticipated, and the fact is that not everyone will benefit. There are several reasons why companies need to move now:

  1. At the beginning quantum will be a tailor-made solution. The first value-generating quantum solutions will use algorithms and hardware specific to the given problem, so it is essential to access the development process early on if solutions are to be optimized for particular needs.
  2. Quantum solutions involve long development times. At least two to three years will be needed to develop and implement a solution-specific algorithm, and then more to upskill and develop the necessary infrastructure. The small talent pool requires companies to establish their name if they want to be the chosen employer, as top talent will go to winning teams with a clear strategic focus on high-value solutions.
  3. Only the first organization to run an algorithm will be able to secure IP protection. Let us assume that a quantum simulation identifies the right catalyst among many for a key reaction. IP rights will be essential to protect against competition in such a heavily R&D driven field.

Speed-up to hedge the risk

There’s another reason to act quickly: it’s still easy and cheap, so the risk is low at the moment.

A small team of 2–5 people is enough to get started, supported by partnerships with leading quantum hardware and software players, at a cost of 1–4 million dollars per year to focus on 1–5 use cases. Quantum hardware, talent, and other essential ingredients are still cheap; however, the inflection point is imminent, and is expected well before the first applications and any actual quantum advantage.

According to McKinsey [6], quantum talent is already scarce, and the gap between supply and demand is expected to widen further in the coming years, driving up costs. The demand for partnerships with hardware players will grow significantly when hardware can run value-generating use cases, so prices can be expected to rise there as well. What this means is that corporations that could benefit from quantum computing will get a significantly higher return on investment by developing their quantum applications now, while the cost of development is still relatively low. Additionally, the R&D investment is relatively small when compared to other, comparably disruptive technologies such as AI, where projects can cost hundreds of millions of dollars.

Note also that there are plenty of near-term applications for companies that start investing now. This means that value can be extracted even before the QC revolution is underway, which is another reason to get involved as soon as possible. Naturally, there is a risk involved in betting on QC, but the risk in not doing so is much greater. It could mean obsolescence.

The best way to mitigate this risk is to start early but small and apply a methodical phased approach. New insights will emerge from looking at the problem with fresh eyes, and quantum-inspired solutions will ensure value creation along the way.

A use case can be developed for anything from a few hundred thousand to a few million dollars a year, depending on the development model, but the potential returns may easily reach hundreds of millions [1]. Due to this large difference, and the challenge involved in catching up if you’re a late starter, the risk of being too late is many times greater than the risk of being too early. This is why over a hundred enterprises across every sector of industry have already invested in quantum computing, including Goldman Sachs, BP, and Volkswagen, to name but three [7].

The take-home message is that the risk–benefit analysis of quantum investment is much more favorable than many would have estimated until recently.

How can Alice & Bob help companies to get quantum ready?

The Box is our consulting unit, designed to provide first-hand insights into the status and potential of quantum computing. The aim is to help you: 

  • Build winning quantum strategies focused on high-value applications.
  • Develop and implement quantum solutions.

We envisage five steps:

  • Assess the potential use cases in your industry and prioritize them in terms of business impact and potential quantum advantage.
  • Choose an appropriate quantum strategy, work out when and how much you should invest in prioritized solutions, and how you should develop them:
    • First, assess the timeline to implementation given the hardware requirements, the hardware roadmaps, and the time needed to develop the solution, then use this to determine an appropriate timing and speed of development.
    • Second, estimate the total cost and potential return on the investment so that you can determine an appropriate budget.
    • Finally, decide whether you want to develop, outsource, or buy the solution, considering costs, scalability of the solution, and the advantage of developing internal knowledge and obtaining IP rights.
  • Launch a quantum computing development team or engage in a partnership to develop the requisite quantum computing algorithm.
  • Re-evaluate hardware progress for different qubit technologies, decide on the right target hardware, and optimize the algorithm for the specifics of this hardware.
  • Develop a plan for implementation, which may include automation and development of an appropriate user interface.

These five steps require a combination of deep technology and a great deal of quantum computing expertise, and this we have. Alice & Bob brings together an impressive multi-disciplinary team, built upon our partnerships with the most prestigious French research institutes: Ecole Polytechnique, ENS Paris, ENS Lyon, Inria, CEA, CNRS, and Ecole des Mines Paris. As pointed out by McKinsey [8], companies like Alice & Bob have a clear advantage with respect to larger groups in attracting talent, thanks to their connections with public research institutes, and this will inevitably benefit the clients we advise.


  1. Quantum technology sees record investments, progress on talent gap“. McKinsey & Co., April 24, 2023.
  2. The hardware and software for the era of quantum utility is here“. IBM Research, 4 December, 2023.
  3. How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits” Craig Gidney et Al., April 15, 2021.
  4. Performance Analysis of a Repetition Cat Code Architecture: Computing 256-bit Elliptic Curve Logarithm in 9 Hours with 126 133 Cat Qubits” Elie Gouzien et Al., July 24, 2023.
  5. LDPC-cat codes for low-overhead quantum computing in 2D” Diego Ruiz et Al., January 17, 2024.
  6. Five lessons from AI on closing quantum’s talent gap—before it’s too late“. McKinsey & Co., December 1, 2022.
  7. Quantum Computing Is Becoming Business Ready” The Boston Consulting Group, May 4, 2023 
  8. Is winter coming? Quantum computing’s trajectory in the years ahead“. McKinsey & Co., May 19, 2023.

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