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The professional’s guide to Quantum Technology

Part 2 – The applications: What problems will we solve with quantum computers?

At a glance:

  • Key applications are expected in four areas: simulation of chemistry and materials, cracking cryptography, using quantum networks to distribute cryptographic keys, and solving large-scale optimisation and AI problems. 
  • Not every quantum speedup is useful: a much faster classical computer is often a better choice. In optimisation and AI, we have not found a truly valuable ‘killer application’ yet.

In the previous part, we saw that quantum computers are extremely slow computers, but they happen to solve some problems more efficiently, that is, in fewer steps. The most important question in this field is: what advantage do quantum computers have on which problems? To answer this question, we break it down into two parts:

What are projected applications and use cases?

We foresee four major use cases where quantum computing can make a real impact on society. We briefly discuss each of them here and link to a later chapter that discusses each application in more depth. 

1. Simulation of other quantum systems: molecules, materials, and chemical processes

Most materials can be accurately simulated on classical computers. However, in some specific situations, the locations of atoms and electrons become notoriously hard to describe, sometimes requiring quantum mechanics to make useful predictions. Such problems are the prototypical examples of where a quantum computer can offer a great advantage. Realistic applications could be in designing new chemical processes (leading to cheaper and energy-efficient factories), estimating the effects of new medicine, or working towards materials with desirable properties (like superconductors or semiconductors). Of course, scientists will also be excited to simulate the physics that occur in exotic circumstances, like at the Large Hadron Collider or in black holes.


Simulation is, however, not a silver bullet, and quantum computers will not be spitting out recipes for new pharmaceuticals by themselves. Breakthroughs in chemistry and material science will still require a mix of theory, lab testing, computation, and most of all, the hard work of smart scientists and engineers. From this perspective, quantum computers should become a useful new tool for R&D departments.

Read more: How can quantum computers help us produce agricultural fertiliser more efficiently?

See also:

2. Cracking a certain type of cryptography

The security of today’s internet communication relies heavily on a cryptographic protocol invented by Rivest, Shamit and Adleman (RSA) in the late 70s. The protocol helps distribute secret encryption keys (so that nobody else can read messages in transit) and guarantees the origin of files and webpages (so that you know that the latest Windows update actually came from Microsoft, and not from some cybercriminal). RSA works thanks to an ingenious mathematical trick: honest users can set up their encryption using relatively few computational steps, whereas ‘spying’ on others would require one to solve an extremely hard problem. For the RSA cryptosystem, that problem is prime factorisation, where the goal is to decompose a very large number (for illustration purposes, let’s think of 15) into its prime factors (here: 3 and 5). As far as we know, for sufficiently large numbers, this task can take a classical computer such a long time that nobody would ever succeed in breaking a relevant code – think of thousands of years. RSA was deemed adequately secure, at least, until computer scientist Peter Shor discovered that quantum computers are quite good at factoring.

The quantum algorithm by Shor can crack RSA (and also its cousin called elliptic curve cryptography) in a relatively efficient way using a quantum computer. To be more concrete, according to a recent paper, a plausible quantum computer could factor the required 2048-bit number in roughly 8 hours (and using approximately 20 million imperfect qubits). The authors had to make several assumptions about what a future quantum computer would look like, and did so in a very prudent way: picking realistic properties of prospective hardware, and using the most up-to-date knowledge of error correction and compiling. Note that future breakthroughs will likely further reduce the stated time and qubit requirements.

Luckily, not all cryptography is broken as easily by a quantum computer. RSA falls in the category of public key cryptography, which delivers a certain range of functionalities. A different class of protocols is symmetric key cryptography, which is reasonably safe against quantum computers but doesn’t provide the same rich functionality as public key crypto. The most sensible approach is replacing RSA with so-called post-quantum cryptography (PQC): public-key cryptosystems resilient to attackers with a large-scale quantum computer. Interestingly, PQC does not require honest users (that’s you) to have a quantum computer: it will work perfectly fine on today’s PCs, laptops and servers.

Read more: How will quantum computers impact cybersecurity? 

See also:

 

During the following decade, every large organisation will have to worry about updating to post-quantum cryptography – a complex migration that comes in addition to the many existing cybersecurity threats. The American National Institute of Standards and Technology (NIST) runs a competition to select a new standard that is adequate for most applications. Nevertheless, many organisations run a vast amount of legacy software that is hard to update, so completing this update in the upcoming ~8 years poses a complex operational challenge. For this reason, organisations are encouraged to start this process as early as possible. 

A new type of cryptography poses additional risks: it has not yet been tested as thoroughly as the nearly 50-year-old RSA standard. Ideally, new implementations will be hybrid, meaning that they combine the security of a conventional and a post-quantum algorithm. On top of that, organisations are encouraged to adopt cryptographic agility, meaning that cryptosystems can be easily changed or updated if the need arises. 

Read more: What steps should your organisation take?

Other great sources are:

3. Quantum Key Distribution to strengthen cryptography

Out of all the applications for quantum networks, Quantum Key Distribution (QKD) is the one to watch. It allows two parties to generate secure cryptographic keys together, which can then be used for everyday needs like encryption and authentication. It requires a quantum network connection that transports photons in fragile quantum states. Such connections can currently reach a few hundred kilometres, and there is a clear roadmap to expand to a much wider internet. The most likely usage will be as an “add-on” for high-security purposes (such as military communication or data exchange between data centres), in addition to standard post-quantum cryptography. 

Unfortunately, we often see media articles suggesting that QKD is a solution to the threat of Shor’s algorithm and that it would form an ‘unbreakable internet’. Both claims are highly inaccurate. Firstly, QKD does not offer the wide range of functionality that public-key cryptography offers, so it is not a complete replacement for the cryptosystems broken by Shor. Secondly, there will almost certainly be ways to hack a QKD system (just like with any other security system). Then why bother with QKD? The advantage of QKD is based on one main selling point: contrary to most other forms of cryptography, it does not rely on mathematical assumptions. This can be an essential factor when someone is highly paranoid about their cryptography, or when data has to remain confidential for an extremely long period of time. 

At this time, pretty much every national security agency discourages the use of QKD simply because the available products are far from mature (and because PQC should be prioritised). It is unclear how successful QKD could be in the future—we will discuss this in depth in a next chapter.

Read more: What are the use cases of quantum networks?

See also: 

Major security agencies do not support the use of QKD to secure communications and agree that post-quantum cryptography should be regarded as the best way to mitigate the quantum threat.

We firmly warn that other security products with the word “quantum” in the name are no guarantee for protection against Shor’s algorithm. In particular, “quantum random number generators” (QRNGs) are sometimes promoted as a saviour against the quantum threat, which is nonsense. These devices serve a completely different purpose: they compete with existing hardware to generate unpredictable secret keys, which find a use (for example) in hardware security modules in data centres. 

See also: 

4. Optimisation and machine-learning

This is the part where most enterprises get excited: Can we combine the success of AI and machine learning with the radically new capabilities of quantum computers? Classical optimisation and AI techniques have had an incredible impact in many areas, from optimising train schedules to detecting fraud, from training chatbots to accurately predicting the weather. 

Under the hood, all such applications are based on concrete mathematical problems such as (discrete) optimisation, differential equations, classification, and optimal planning. For conciseness, we collectively refer to these problems (including machine learning tasks) as ‘optimisation’. The classical field of optimisation is of great importance and takes up a significant fraction of the world’s computational resources!

Contrary to the many classical successes, the impact of quantum optimisation or machine learning is not yet clear. To better understand the available algorithms, we will consider three different categories. 


Rigorous but slow algorithms

Many quantum optimisation algorithms have a well-proven quantum speedup: there is no dispute that these require fewer computational steps than any classical algorithm. For instance, a famous quantum algorithm invented by Lov Grover (with extensions by Durr and Hoyer) finds the maximum of a function in fewer steps than conventional brute-force search. Similarly, quantum speedups were found for popular computational methods such as backtracking, gradient descent, semidefinite programming, lasso, and interior point methods for solving differential equations

The main question is whether this also means that the quantum computer requires less time! All of the above optimisation algorithms offer a so-called polynomial speedup (in the case of Grover, this is sometimes further specified to be a quadratic speedup). As we will soon see, it is not entirely clear if these speedups are enough to compensate for the slowness of a realistic quantum computer – at least in the foreseeable future. 


Heuristic algorithms

On the other hand, some algorithms claim much larger speedups, but there is no undisputed evidence to back this up. Often, these algorithms are tested on small datasets using the limited quantum computers available today – which are still so tiny that not much can be concluded about larger-scale problems. Nonetheless, these ‘high risk, high reward’ approaches typically make the bold claims that receive media attention. The most noteworthy variants are:

  • Variational quantum circuits (VQC) are relatively short quantum programs that a classical computer can incrementally change. In jargon, these are quantum circuits that rely on a set of free parameters. The classical computer will run these programs many times, trying different parameters until the quantum program behaves as desired (for example, it might output very good train schedules or accurately describe a complex molecule). The philosophy is that we squeeze as much as possible out of small quantum computers with short-lived qubits: the (fast) classical computer takes care of most of the computation, whereas the quantum computer runs just long enough to sprinkle some quantum magic into the solution.
    Although its usefulness is disputed, this algorithm is highly flexible, leading to quantum variants of classifiers, neural networks, and support vector machines. Variants of this algorithm may be found under different names, such as Quantum Approximate optimisation Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and quantum neural networks. 
  • Quantum annealing solves a particular class of optimisation problems. The problem is encoded into a physical system (in jargon: a Hamiltonian) so that at very low temperatures, the physical system somehow describes a solution to the problem. For example, when finding the optimal locations to place mobile phone masts, a qubit in the state “1” might indicate a good site. A quantum annealing algorithm is a smart way to ‘make’ such a low-temperature system (often by starting in a setting where it’s trivial to ‘cool’ and then slowly introducing the complex forces corresponding to the target system).
    Annealing itself is a mature classical algorithm. The advantage of a ‘quantum’ approach is not immediately apparent, although there are claims that hard-to-find solutions are more easily reached thanks to ‘quantum fluctuations’ or ‘tunnelling’.
    Quantum annealing was popularised by the Canadian company D-Wave, which builds dedicated hardware with up to 5000 qubits and offers a cloud service that handles relatively large optimisation problems. 
 

Fast solutions in search of a problem

Lastly, there exist algorithms with large speedups, for which we are still looking for use-cases with any scientific or economic relevance. The most notable example is the quantum algorithm for topological data analysis (a method to assess certain global features of a dataset), which promises an exponential advantage under certain assumptions. However, to our best knowledge, no interesting datasets have been found that fit the algorithm’s requirements.

Some impressive speedups that were recently found have been ‘dequantized’: these algorithms were found to work on classical computers too! There’s a beautiful story behind this process, where Ewin Tang, a Master’s student at the time, made one of the largest algorithmic breakthroughs of the decade. A great report can be found here: https://medium.com/qiskit/how-ewin-tangs-dequantized-algorithms-are-helping-quantum-algorithm-researchers-3821d3e29c65

Unfortunately, there does not yet exist an optimisation algorithm with obvious economic value: all of them come with serious caveats. This perspective is perhaps a bit disappointing, especially in a context where quantum computing is often presented as a disruptive innovation. Our main takeaway is that quantum optimisation (especially quantum machine learning!) is rather over-hyped. 

Of course, there are still good hopes that we will find new algorithms and applications. To truly understand this field, we should examine the prospects of finding a new ‘killer algorithm’. The next section becomes slightly more technical and helps us quantify the amount of ‘quantum advantage’ that different algorithms have. 

 

Further reading: 

How large is the advantage of known speedups? 

When looking at the applications of quantum computers, one should always keep in mind: Are these actual improvements over our current state-of-the-art? Anyone can claim that their algorithm can solve a problem, but what we really care about is whether it solves it faster. Classical computers are already extremely fast, so quantum algorithms should offer a substantial speedup before they become competitive. 

We can assess algorithms by their so-called “asymptotic complexity”: As a problem becomes ‘bigger’, how much longer does a computation take? The main figure of merit is how this scales towards very large sizes. 

For every instance of a problem, we can define a ‘size’ that influences the difficulty. For example, computing 54 x 12 is much easier than 231.423 x 971.321, even though they’re technically the same problem, and we’d use the very same multiplication algorithm. Similarly, creating a work schedule for a team of 5 is simpler than dealing with 10.000 employees. We typically use the letter ‘n’ to denote the problem size. You can see n as the number of digits in a multiplication (like 2 or 6 above) or the number of employees involved in a schedule. 

For some very hard problems, the time to solution takes the form of an exponential: T ~ en, where T is the time taken. Exponential scaling is typically a bad thing, as the function en becomes incredibly large even for moderate values of n. The problem of factoring (on a classical computer) scales somewhat similar to T ~ en

There are also problems for which the scaling looks like a polynomial, like T ~ n3 or T ~ n2. Polynomials grow much slower than exponentials, making it easier to solve large problems in a reasonable amount of time. The problem of factoring on a quantum computer takes scales roughly as T ~ n3 (thanks to Shor’s algorithm*). Because a quantum computer brought the exponential down to a polynomial, we call this an ‘exponential speedup’. Such speedups are a computer scientist’s dream because they have an incredible impact on practical runtimes. 

Often, we deal with ‘merely’ a polynomial speedup, which happens when we obtain a smaller polynomial (for example, going from T ~ n2 towards T ~ n), or perhaps even a ‘smaller’ exponential function (like T ~ en towards T ~ en/2. Reducing the exponent by a factor of two (like n2 -> n) is also sometimes called a quadratic speedup (which is precisely what Grover’s algorithm gives us).

Further reading:

 
 * You may find even sources stating smaller polynomials, like n2 log(n). These are theoretically possible but rely on asymptotic optimizations that are unlikely to be used in practice.

Here is a rough overview of quantum speedups as we understand them today, categorised by their type of speedup:

 

Quantum Speedups Exponential Heuristic inforgraphic 2024

🟢   The “exponential” box is the most interesting one, featuring applications where quantum computers seem to have a groundbreaking advantage over classical computers. It contains Shor’s algorithm for factoring, explaining the incredible advantage that quantum computers have in codebreaking. We also believe it contains some applications in chemistry and material science, especially those relating to dynamics (studying how molecules and materials change over time). 

🟡   The “polynomial” box is still interesting, but its applicability is unclear. Recall that a quantum computer would need much more time per step – and on top of that, it will have considerable overhead due to error correction. Does a polynomial reduction in the number of steps overcome this slowness? According to a recent paper, small polynomial speedups (as achieved by Grover’s algorithm) will not cut it, at least not in the foreseeable future. 

🔴   For some computations, a quantum computer offers no speedup. Examples include sorting a list or loading large amounts of data. 

If this were the complete story, then most people would agree that quantum computing is a bit disappointing. It would be a niche product for hackers and a tiny community of physicists and chemists who study quantum mechanics itself. 

⚪   Luckily, there is yet another category: many of the most exciting claims come from the heuristic algorithms. This term is used when an algorithm might give a suboptimal solution (which could still be useful), or when we cannot rigorously quantify the runtime. Such algorithms are quite common on classical computers: neural networks fall in this category, and these caused a significant revolution in AI. Unfortunately, it is unclear what the impact of currently known heuristic quantum algorithms will be. 

See also:

Where is the killer application?

For a quantum algorithm to be truly impactful, we require two properties: 

  1. [Useful] The algorithm solves a problem with real-world significance (for example, because organisations can work more efficiently or because it helps answer scientific questions).
  2. [Better/faster] Out of all the possible approaches, this algorithm is the most attractive solution. This is achieved when a realistic quantum computer solves the problem faster than any classical machine could, but other aspects like energy consumption or total cost of hardware and software development can also play a role. This most likely requires an exponential speedup, or a large polynomial speedup. 


Several algorithms, most notably Grover’s algorithm and VQE’s, have a very wide range of industrial applicability. However, it seems that in practice, other (classical) approaches solve such problems faster and more cheaply. 

Then there exist exponential speedups, like the algorithm for topological data analysis, for which no practical uses have been found (despite many scientific and industrial efforts). 

To our best knowledge, only codebreaking (Shor’s algorithm) is both exponentially faster and extremely impactful. Unfortunately, this is primarily a negative application that helps criminals – we are not aware of any positive uses of Shor, hence this is not quite the killer application that we’re looking for. 

Even in chemistry, it is hard to pinpoint a convincing application. Classical computers are already incredibly fast, and very good classical algorithmic techniques have been developed. Scientist Garnet Chan gives talks which are suggestively titled “Is There Evidence of Exponential Quantum Advantage in Quantum Chemistry?”. 

Could the nature of quantum mechanics be such that it helps us in codebreaking, but in literally nothing else? We think that such a scenario is unlikely. It seems that a killer algorithm has not yet been found, but there are good reasons to hope that we will find one in the future. Perhaps we need novel mathematical tools, or perhaps we simply need to play around on increasingly mature quantum hardware. We hope that we’ll have to incrementally update this page over the coming years, as we slowly uncover the complete set of capabilities that quantum computers have!

Well, we simply don’t know! However, some useful technical hints may be:

  • As described above, we’d most likely require an exponential or some heuristic speedup. This is much more likely achieved on problems where we don’t already know very efficient classical algorithms. 
  • When reading data is a limiting factor (as in “big data” applications), quantum computers appear to be very slow. Getting the data into a quantum computer seems to take at least as long as processing the data on a much cheaper supercomputer. This holds, for example, when searching through a database, but also for data-intensive simulations like weather forecasting. 
  • Similarly, if the desired output is a large amount of data (such as a very large list or table), then a quantum computer is likely not efficient. Most quantum algorithms look at a global property of a function or dataset that can be encoded in a very small output (like Deutsch-Jozsa or Shor’s algorithm when interpreted as finding the period of a function). 
  • Some people would say that if quantum computers are not “faster”, perhaps they might solve a problem “more accurately” (for example, they might produce a more reliable forecast). However, when we look at speedups, then accuracy is already taken into account: we compare the number of steps asserting the output has a given accuracy. 
  • Classical computers are already incredibly fast, and the bottleneck for many real-world computational problems is not in a computer’s speed. If an application does require a supercomputer today, then it’s unlikely that anyone will invest in a quantum computer soon.

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