Computing as we know it may be about to change forever. Fortunately, we are not talking about home computing or our computers, but about those centres that process vast amounts of information. The quantum computer race has begun and quantum computers have the potential to revolutionise the way we deal with today's problems. With computer giants such as IBM, Google and Microsoft betting on a technology that seems to be straight out of science fiction, this quantum future is getting closer and closer.
Quantum computers are based on qubits and promise us that, by applying the principles of quantum physics, it will be possible to solve very complex problems more quickly. However, a quantum computer does not work on its own; it needs algorithms, a kind of programme that tells it how to deal with the challenges it will face. This branch that brings together computer science, experimental physics and theoretical physics is called quantum algorithms, and it is a relatively new and fascinating field because of its possibilities.
Thanks to quantum algorithms, it will be possible to reduce the computation time of certain operations from hundreds or thousands of years to a few minutes, thus providing answers to problems that are currently unsolvable due to the limitations of our machinery. But this capability also poses certain challenges. Much of today's cryptographic system, i.e. the basis for making our computer passwords secure, depends on these limitations. With this enormous capacity for future computing, the race for quantum algorithms is on.
INSIDE. The benefit of probability to optimise decisions
The word algorithm is increasingly present in our daily lives. There is talk of algorithms that control what we see and hear on social platforms, as well as algorithms that learn based on our preferences. The algorithm resembles those oracles of antiquity that offered answers to any question, but at the same time instilled fear and an unclear process. So, amidst all this algorithmic noise, it is sometimes a good idea to try to walk before we run and start by asking the most basic questions. So, what is an algorithm?
An algorithm is nothing more than a set of instructions to arrive at a result.[i]. In real life we find algorithms in everything we do. For example, flicking the switch to turn on a light is an algorithm, following a recipe is an algorithm, or pressing certain buttons on the remote control to watch the current series is another algorithm. Algorithms are therefore an intrinsic part of our lives, those steps we follow to reach an end.
When we talk about algorithms in computing[ii] the idea is the same: to follow a series of steps to find the result of a problem. However, in these cases, the algorithm requires hundreds or thousands of mathematical operations that the computer solves at an astonishing speed. These operations occur using so-called “binary code”, i.e. long sequences of digits that can only be presented as 0 or 1. The resulting long sequences of binary code are then translated by the various programming languages into messages that humans can understand.
Thus, it is computational algorithms that, for example, allow us to write this text. In simplified form, every time a key is pressed, an electric current is generated in a circuit. Depending on which key has been pressed, a microcontroller transforms this electrical information into a unique binary number, which it sends to the computer. The computer will interpret the sequence of 0's and 1's and, thanks to its operating system and text editing software, will display the indicated letter on the screen by turning certain pixels on or off. With modern computers, this process happens almost instantaneously, as they are capable of operating at colossal speed. The more power they have, the faster they can perform operations, which translates into greater data analysis capabilities.
With these same principles, supercomputers such as the MareNostrum in Barcelona[iii] can handle 314 billion operations per second, a power equivalent to 380,000 home laptops. Scientists analyse millions of operations to make discoveries on subjects as varied as the discovery of exoplanets or the fight against cancer.
But in quantum computing this fundamental property of computing changes. Quantum computers do not work with bits, but with qubits. These pieces of information can act like normal bits and have a value of 0 or 1, but thanks to quantum mechanics they can also be in a superposition state. That is, they can be worth both 0 and 1 until their state is measured.
This idea dates back to the 1980s. Pioneers of quantum mechanics such as the American physicist Richard Feynman predicted that computers based on quantum physics could be the key to simulating other quantum systems, a very complex task for classical computing. Thus, in 1985, David Deutsch formulated the concept of a universal quantum Turing machine.[iv], This provided a new basis for quantum algorithms.
To understand this, let's imagine a tape that is divided into cells and each cell has a symbol. Something similar to the negative tapes of the old photographic cameras. In a Turing machine, instead of photographs, each cell has a symbol, such as a 1 or a 0. A head reads that tape and, depending on what it reads, it is given one instruction or another. This instruction can be to change the 1 to a 0 or vice versa, to touch nothing, to move to a section, or to move on to a new instruction. With this hypothetical machine, Turing demonstrated that mathematical calculations can be performed using a simple set of rules. Deutsch's universal quantum Turing machine is governed by the same principles, but the basic rules are replaced by Schrödinger equations, the symbols by a sequence of simple quantum states and the spindle by a quantum interaction capable of reading or resetting the spin value of the quantum state.
The problem with this mechanism is that it does not exploit the full potential of quantum mechanics since, when measuring the spin value, the superposition state collapses and is fixed as 0 or 1. As these concepts are extremely complex and not very intuitive, let us think that the important thing about Deutsch's theory is that, just as the Turing machine made it possible to lay the foundations of classical algorithms (and therefore computation), the quantum Turing machine paved the way for the formulation of quantum algorithms. Thanks to this advance, the first quantum algorithms began to appear in the 1990s. In them, it was shown that a quantum computer could be more efficient than classical ones in solving certain problems. But why?
As quantum algorithms start from a fundamentally different basis, they have other ways of arriving at a solution to a problem. As we saw with the Turing machine, a classical algorithm is a sequence that will follow a series of guidelines and as long as the initial state and the commands are the same, it will produce the same result. In other words, in a normal computer, whenever we perform a mathematical operation, it will give us a consistent result. In a quantum algorithm, on the other hand, the state of the qubits interferes with the result and, therefore, it is usually probabilistic. Therefore, the algorithm has to be run several times to increase the confidence of the result.
With this in mind, mathematician Peter Shor developed Shor's Algorithm in 1994. This algorithm is able to solve the factorisation of integers, i.e. to find out whether they can be broken down into smaller numbers or whether they are prime numbers. In the case of small numbers, such as 10 (2×5), this task is simple, but for prime numbers with millions of digits, the calculations become complicated and take forever on a classic computer. Due to the RSA cryptographic system, the security of the passwords we use on our computers depends on this difficulty of calculation.
When we send an encrypted message, as with instant messaging apps, we have two keys: a public key and a private key. When we send a message to a person, the sender looks for the receiver's key, which is usually a large prime number, and encrypts the message using that key. Once the message reaches the person of interest, they can decrypt the message using their private key, another large prime number. In the event that the message reaches another person, without the private key, they would only be able to read meaningless gibberish. To decrypt the system, the hacker in question would have to know which two prime numbers have been used to encrypt the message, and to do so, he would need to perform operations that could take thousands of years. But with Shor's Algorithm, the time is substantially reduced. Therefore, if applied to a quantum computer, it would mean that this cryptographic system would become insecure.
However, quantum computers are not yet advanced enough to solve such equations in a way that makes the RSA system obsolete. However, this is a risk worth bearing in mind for the future, because quantum computing is advancing rapidly over the last decade.
Later, in 1996, Lov Grover developed an algorithm[v] which was able to search a database quadratically faster than traditional algorithms. If his method were applied, artificial intelligences based on machine learning could become more efficient. To give an example, using this quantum algorithm could greatly advance the area of artificial intelligence for healthcare. Thanks to this algorithm, automatic pathology recognition systems in scanners or X-rays could be substantially improved.
Later on, other purely quantum and hybrid algorithms have been developed, which allow certain important scientific tasks to be carried out, such as calculating the minimum energy of molecules, or combinatorial problems to find the fastest route between two points. However, all this was done theoretically, without ever materialising in a real quantum computer.
It was not until 2019 that Google announced quantum supremacy. That is, it managed to use a quantum algorithm on a 53-qubit quantum computer to solve a problem. It is estimated that an average supercomputer would have taken thousands of years to carry out the calculation, but it took this tiny computer only 200 seconds. Since then, other companies have developed or simulated quantum computers to solve problems.
Several companies are currently investing significant money and resources in the creation of quantum computers with a higher number of qubits. Technology giants such as IBM, Microsoft and Google are in a real race to achieve the quantum quantum quantum computer. computer[U1] quantum technology to date, and they are exploring the physical limits to achieve it.
The biggest problems they are encountering arise in ensuring that the results extracted by applying the algorithms are correct, as the probabilistic foundation of quantum computing has this intrinsic problem. Therefore, many of the physical qubits in a quantum computer have to be used to protect against errors and provide stability and consistency to those doing the computation. According to one of the latest error correction methods, called the Gröss code, for each stable logical qubit, dozens of physical qubits are needed. Specifically, protecting 12 logical qubits for 1 million operations requires 288 physical qubits.[vi].
This can be a major problem in the scaling of quantum computers, but it is expected that, with the development of new algorithms, more practical solutions will be found.
Quantum algorithms is an area of research that has the potential to revolutionise current computing power and possibilities. With a growing need for massive data analysis, quantum algorithms are a safeguard for near-instantaneous results. In this way, quantum algorithms are expected to revolutionise sectors such as health, climate science, physics, and computer science. Although it has certain limitations, since it has been shown that, no matter how much progress is made, it will not be able to solve unsolvable problems, its study is very interesting in terms of analysing data more efficiently.
At present, the development of this technology is only within the reach of the largest technology companies, as it requires enormous resources to bring it to fruition. But the talent capable of understanding how to realise the quantum world and use its capabilities in the real world is spread across the globe.
IN ACTION. Quantum comes first in the form of algorithms
While quantum hardware tends to get most of the headlines in the media, it is quantum software that has advanced significantly in just a few years. The volume of information that can be processed is growing at an exponential rate and the pressure to make the way we operate in all areas, public and private, sustainable from all points of view, including environmental and social, is increasing.[vii], is growing.
The recent Glasgow Climate Pact could be seen as a source of a myriad of optimisation problems for IT, for example. It drives the continuous expansion of photovoltaic and wind power generation parks and this increases the number of variables in the energy grid. As the level of complexity of energy supply increases, at the confluence of different technological revolutions, the challenge for today's computers in terms of sustainability grows. Society demands short-term solutions, but energy resources are limited and at some point may not scale. The technologies of the second quantum revolution are emerging as a third way to resolve this apparent conflict between technological development, environmental friendliness and the fight against climate change.
In the hardware domain, the current era of quantum computing is known by the acronym NISQ (noisy intermediate scale quantum computing) and is characterised by devices with a still limited number of qubits and subject to high levels of noise and errors. Quantum systems cannot handle very large, high-dimensional data sets, and have to overcome enormously complicated technical and scientific challenges as yet. It should not be forgotten that the field of quantum computing itself is still emerging, its literature is sparse and not yet systematically classified.
This translates into limited possibilities for existing quantum software. The most valuable use cases would require between 10,000 and 20,000 qubit operations and a fidelity close to 100%. However, circuits of more than 30 qubits had only achieved, at most, a fidelity rate of 99.5% until the Microsoft and Quantinuum collaboration managed to partially surpass it in April 2024, when they achieved a «three nines» rate for two-qubit gates in their H1 systems. This is still, however, an uncompetitive record. The most useful algorithms need millions of gate operations, even billions in the case of Shor's algorithm, so quantum machines still need to improve by many orders of magnitude.
These factors have prevented quantum computing from achieving a definitive advantage over classical systems, at least through the digital gate approach, and are driving new avenues for the application of quantum or quantum-inspired algorithms. The prospects, in that sense, are much more encouraging. It is estimated that quantum-enhanced hybrid computing could become the standard by 2030.[viii], especially in areas ranging from health and finance to autonomous systems.[ix].
Even before the first quantum computer was built, researchers had already started to develop classical algorithms inspired by quantum computing theory. These algorithms take advantage of certain properties of quantum computing to achieve higher performance and problem-solving capabilities. It is true that the development of new algorithms has lagged behind. In fact, most of the significant advances were formalised between the 1980s and 2010s, and little progress has been made in the last ten years. But today, more and more companies are investing in commercial applications based on quantum-inspired software.
Companies such as D-Wave, Pasqal, Kipu Quantum and Spain's Qilimanjaro have embraced analogue and hybrid (analogue combined with few gates) quantum computing. Quantum cloud services such as IBM Quantum and AWS Braket help integrate quantum algorithms into business workflows, while open source frameworks such as IBM's Qiskit, Google's Cirq, Quantinuum's TKET and Xanadu's PennyLane are emerging that lower barriers to entry, allowing developers to write quantum programmes in high-level abstractions that resemble classical programming paradigms. Multiverse Computing, which topped Spain's list of companies for European patent applications in 2022, is one of the world's most advanced in quantum-inspired algorithms.
This hybrid approach could offer practical near-term benefits for specific use cases in molecular simulation, cryptography and small-scale optimisation. In general, for industries relying on optimisation and complex simulations: early adoption of quantum and quantum-inspired algorithms is expected in the financial and pharmaceutical sector over the next three to five years, targeting portfolio optimisation, risk modelling and drug discovery. This will be followed by aerospace, defence and energy (5-10 years), which will benefit from quantum simulations for materials science, fuel efficiency and nuclear fusion. Meanwhile, robotics and artificial intelligence (AI) may require more than 10 years before the impact of the new technology is widely experienced, as real-time quantum processing and advances in AI are still in the early stages of research.
Quantum algorithms are part of a quantum computing industry that grew at an annual rate of 5.24% in 2024 and counts more than 360 startups among the more than 13,000 companies contributing to its global expansion. The sector employs more than one million people worldwide and added more than 59,000 new employees in 2024, a year in which more than 65,000 applicants exceeded 296,000 patents.[x]. The combined investment value of major investors, such as Google, Quantum Capital Group, National Growth and others, was over $6 billion.
Of the 26 different types of quantum-inspired algorithms identified by the end of 2024, six of them are receiving special attention from researchers. The Quantum Particle Swarm Optimization (QPSO) algorithm appears in about 43% of publications and is dominant in the field of electricity, gas, steam and air conditioning supply. It is followed by Quadratic Unconstrained Binary Optimisation (QUBO), which accounts for almost 17% of studies and leads in the field of transport and storage, although its growth rate could soon put it in the lead. Other promising algorithms are the Quantum Genetic Algorithm (QGA), whose performance is shown in Figure 1, the Quantum-Inspired Evolutionary Algorithm (QIEA) and the Quantum Bat Algorithm (QBA).
Sectors where the volume of research on quantum-inspired algorithms is high include public administration and defence; social security; architecture and engineering; management consultancy; water collection, treatment and supply; information services; and repair and installation of machinery and equipment.
The European Union is focused on the quantum technologies race without a specific space for actions aimed at promoting the software industry. Two years after the Quantum Manifesto[xi] of 2016, launched the Quantum Technologies Flagship[xii] whose aim was to support the work of hundreds of researchers over 10 years. In the Flagship start-up phase (2018-2022), it earmarked €152 million and benefited 24 projects, with more than 1,600 researchers involved. The next phase, which is already part of the Horizon Europe programme, initially has 400 million at its disposal and also aims to boost more than 20 new projects. The Flagship's research objectives are based on the Strategic Research Agenda on Quantum Technologies, to which more than 2,000 European experts contributed. Its long-term vision is to develop the so-called quantum internet in Europe, which will connect computers, simulators and quantum sensors via quantum communication networks.
The European High Performance Computing Joint Undertaking (EuroHPC JU) is working on just that. In October 2022, it announced the selection of six sites in the EU to host Europe's first quantum computers, to be integrated into EuroHPC supercomputers in the Czech Republic, Germany, Spain, France, Italy and Poland. This decision marked the start of the deployment of a quantum computing infrastructure, accessible to European scientific and industrial users via the cloud, and for non-commercial purposes.[xiii]. This infrastructure is designed to address complex simulation and optimisation problems, especially in materials development, drug discovery, weather forecasting and transport, among others.
Finally, in December 2023, the “European Declaration on Quantum Technologies” stated that they are a high priority for EU sovereignty.[xiv], He cited the European Economic Security Strategy, as well as the Commission Recommendation of 3 October 2023, which includes quantum technologies among the critical technology areas for economic security. It reads that, since 2018, the EU and member states have committed more than €8 billion to quantum technologies.
The reality is that few European institutions rank among the world's leading quantum research centres. In the case of universities, the centres in Copenhagen, Paris, Munich and Delft stand out, but they are still far behind US institutions such as Caltech, MIT and Harvard, and universities in non-EU countries such as the UK and Switzerland. Public and private funding in the EU also lags behind the US and China. The latter has committed to invest around $15 billion, while the US has announced $5 billion over the next decade, although it benefits from strong venture capital and investment.[xv] which will far exceed public sector contributions.
The Draghi Report rightly called for boosting investment in quantum technology and facilitating access to European private capital for startups and growing companies. Experts believe that the EU lacks a comprehensive understanding of quantum technology's critical supply chains, its potential bottlenecks and its own strengths and weaknesses. Although the European Chip Law includes measures to encourage low-cost, high-volume manufacturing of quantum chips in the EU, the sector is mainly made up of relatively small ecosystems spread across the EU states. members[U2] that have formulated comprehensive national quantum technology strategies: Denmark, France, Germany, Ireland and the Netherlands, and Spain has just joined them.
Fragmentation is replicated in the private sector: instead of large corporations, the EU has mainly start-ups and a few growing companies. Software based on quantum-inspired algorithms is crying out for more attention in this array of national and European efforts. The challenge is to find concrete applications in productive sectors that can improve the efficiency of companies and open the door to new business models, as can be seen in Figure 3. A promising field called quantum-inspired machine learning (QiML) allows, for example, quantum systems to be simulated digitally, without the need for quantum computers. The key to making a difference in this case will be to identify which effects can be efficiently simulated to achieve a computational advantage.[xvi].
Current examples of dynamism in the case of quantum-inspired algorithms confirm their growth potential and their ability to transform entire industries. The case of the financial industry and the trading is one of the most obvious. Quant-inspired portfolio recommendation systems, based on trend rate and optimisation, specifically designed for global cross-equity markets, are now appearing. In some cases[xvii], The model already identifies robust and stable upward trends, assesses complex market relationships and analyses cross-country stock market connections. It does so by prioritising explainability and transparency, so that investors understand the results generated by AI, an increasingly important capability for business users and regulators.
ICOSA and NEC have developed Vector Annealing (VA), a quantum-inspired computing method, which significantly accelerates the search for solutions in financial portfolio optimisation. And Toshiba's SQBM+ quantum-inspired technology helps to find profitable, short-term, real-time arbitrage opportunities in the foreign exchange market with a high-frequency trading (HFT) solution. The system can capture short-term opportunities in less than a millisecond with no errors, increasing the success rate to 98%. y, and ultimately solves a bottleneck in the financial industry.
Nearly 80% of the world's 50 largest banks are actively engaged in quantum technology.[xviii]. JPMorgan Chase accounts for two-thirds of all job postings and publishes more than half of all research papers in the field, and has already implemented quantum-inspired algorithms to improve portfolio optimisation and cybersecurity. Italy's Intesa Sanpaolo is exploring quant applications in credit rating, fraud detection and derivatives pricing. Since mid-2024, the number of quant professionals in banking has increased by 10%, and research by bank-affiliated quant experts has been cited more than 3,000 times. McKinsey[xix] estimates that, by 2035, quantum computing use cases in finance could generate up to $622 billion in value.
One of the lines of research for modelling the distribution of financial returns[xx] and follow the dynamics of asset prices is that of so-called quantum walks, although they should be handled with caution, because they may introduce small biases, e.g. reflecting optimism on the part of the buyer. Game theory, a fundamental concept in economics, could also be approached from this perspective, as the model could reflect subjective factors, as opposed to classical models representing the seller.
A growing number of financial asset managers and hedge funds are also exploring the possibilities of tensor networks, quantum-inspired algorithms and specialised hardware. Figure 4 compiles the categories of quantum-inspired solutions. The aim is to find speed-ups for well-known classical algorithms, rather than replacing them with new ones, in order to avoid using quantum algorithms that do not yet have the scale for real-world business cases.[xxi]. Quantum-inspired software can be used, for example, in option pricing, as previous models assume constant volatility and this becomes a limitation. Google has developed[xxii] an open source library called TensorNetwork for implementing tensor network algorithms.
The possibilities for technological convergence with the new cycle of artificial intelligence (AI) are vast. Researchers from Spain's Multiverse Computing and CounterCraft have developed a new quantum AI model, trained on real network traffic datasets and system logs, that identifies the 100% of cyberattacks. It does this by employing adversary-generated threat intelligence instead of traditional systems.[xxiii]. Researchers at South China University of Technology and Huawei Technologies, meanwhile, have developed a quantum-inspired machine learning method for molecular docking, a key tool in drug design.[xxiv]. Their method outperforms traditional coupling algorithms and deep learning-based algorithms by more than 10%. Quantum-inspired machine learning algorithms (a type of AI), embedded in peripheral devices, can optimise real-time decision-making in applications related to autonomous vehicles or internet of things (IoT) systems.
In the field of personalised medicine, machine learning based on quantum computing represents a real revolution for sophisticated real-time decision making. Toray[xxv] collaborated with Fujitsu to use a annealer digital and predict the optimal conformations of protein side chains. Moderna is exploring quantum computing and GenAI to advance and speed up its research on messenger RNA (mRNA) to develop vaccines faster. Scientists can create synthetic genetic data very similar to real-world data that could feed quantum algorithms to develop more accurate molecular models, speeding up the entire drug discovery process.
Sustainable cities also have a lot to gain from the combination of quantum computing and AI.[xxvi]. Urban planners could create synthetic traffic data that simulates real-world traffic patterns to train quantum algorithms to improve congestion management and optimise routes, leading to more sustainable cities with efficient transport systems, reduced travel times and better air quality.
Currently, generative AI (GenAI) struggles with some complex mathematical tasks, in particular when converting classical algorithms into quantum ones. However, as AI continues to develop and improve its understanding of mathematical libraries and solvers, its potential to bridge this gap will be further enhanced.[xxvii] will continue to grow. Business leaders will need to consider the strategic value of large quantitative models (LQMs), which run on classical computers, not quantum computers, but can simulate quantum mechanical behaviour and other quantitative factors that traditional methods struggle to address. They are algorithms capable of tackling multivariate problems, which will enable the introduction of efficient and more accurate models and strategies that could potentially unlock up to $700 billion in value, according to McKinsey, and reduce healthcare costs.
The performance of different functional materials (such as alloy materials, optical materials, etc.) depends on many design factors, such as geometrical characteristics, composition, processing conditions and environmental factors. Traditional methods, including experiments and simulations, are often too time-consuming and costly in workspaces that are sometimes extremely large. A QGA algorithm combines the advantages of quantum computing and genetic algorithms.[xxviii] to overcome these obstacles.
NASA's Jet Propulsion Laboratory partnered with Azure Quantum to develop an optimisation solution that helped reduce scheduling times from hours to just minutes. Ford also collaborated with Microsoft to use quantum-inspired technology in a 5,000-vehicle traffic routing simulation, which was shown to reduce traffic in Seattle by 73% and shorten travel times by 8%. In the manufacturing industry, quantum-inspired algorithms help optimise production schedules. This minimises downtime and maximises throughput and can reduce companies' operating costs by 30%.
55% of companies that adopted quantum-inspired algorithms have experienced improved market positioning, according to Deloitte. Unlike experimental quantum computing, quantum-inspired algorithms leverage the principles of quantum superposition and annealing to design classical solutions for route optimisation, demand forecasting and inventory management. In the transportation sector, IBM collaborated with a commercial vehicle manufacturer to optimise deliveries at 1,200 locations in New York City. Traditional methods were hampered by the interaction of dynamic variables, such as 30-minute delivery times and real-time traffic. By integrating quantum-inspired algorithms into a hybrid framework, the team simultaneously processed millions of potential routes, and managed to drastically reduce operational costs.
Japanese restaurant chain Toridoll Holdings partnered with Fujitsu to predict customer traffic by analysing AI-based sales data, weather patterns and promotions. This quantum-inspired approach and solution optimised inventory levels and kitchen operations, reduced energy consumption and food waste. Japan Post implemented Fujitsu's Quadratic Unconstrained Binary Optimisation (QUBO) solver to reimagine Tokyo's last-mile logistics. The system evaluated millions of delivery sequences, reduced operating times by approximately 30% and reduced the delivery fleet from 52 to 48 trucks.
Despite the potential impact of quantum and quantum-inspired computing, 65% of those surveyed by Market Connections admit that their organisations are slow to adopt this technology and, in fact, 58% considered it a medium or low priority for the next 12 to 24 months. But the scope of application of quantum-inspired algorithms goes beyond the search for purely economic efficiency. In the field of internet social networks and large population management, the concept of “influence maximisation” is used to help select the optimal nodes to achieve a goal. Practical applications of influence maximisation techniques range from epidemiology to marketing, making it a popular research topic due to its diverse real-world uses.
Turning to the complexity of the problems associated with climate change, even with modern computer technology, direct and accurate simulation of all but the simplest turbulent fluid flows remains impossible, particularly in the case of the atmosphere. This is because turbulence is characterised by swirls and eddies of various shapes and sizes interacting in a chaotic and unpredictable manner.
Instead of directly simulating the problematic fluctuations, one group of researchers decided to model them as distributed random variables, according to a probability distribution function. In this way, they were able to extract all significant flow quantities, such as lift and drag, without having to worry about the chaos of turbulent fluctuations, which is unfeasible with classical methods. To solve this, the team applied a quantum-inspired computing technology[xxix] developed at Oxford University that runs on a single CPU (central processing unit) core. In just a few hours it was able to calculate what an equivalent classical algorithm would take several days to solve using a supercomputer.
To realise its full potential in the private sector, as in other areas, the problem of talent availability will need to be addressed. 60% of technology leaders surveyed by IBM cited a lack of skilled professionals as the main obstacle. The majority (77%) of them believe that having the right talent and identifying skills gaps are major obstacles to their organisation's ability to adopt quantum computing. The talent and skills gap could jeopardise potential value creation, which McKinsey estimates at up to $1.3 trillion.
A McKinsey study has found that there is only one qualified quantum computing candidate for every three job openings. Less than 50% of quantum computing jobs were expected to be filled by 2025 unless there were significant changes in the available talent pool or in the expected rate of quantum computing job creation. Interestingly, small startups working in the quantum sector tend to emerge from university research labs and often have direct access to qualified candidates. Larger companies may have less connection to these talent pools.[xxx].
SPAIN. A strategy for hiring and startups that change the world.
The Quantum Spain strategy, integrated in the National Artificial Intelligence Strategy (ENIA) and in the Quantum Technologies Strategy 2025-2030.[xxxi], has come into being with the aim of promoting an ecosystem that seeks synergies and unifies the objectives of business and research activity. It is coordinated by the BSC-CNS, with an investment of 22 million euros to acquire quantum computing capabilities based on superconductors to support research and innovation. The Quantum Spain consortium involves 27 research institutions from 14 autonomous communities and 15 public universities. The project has succeeded in launching the first quantum computer built with European 100% technology and three quantum emulators.
In addressing the possibilities of quantum algorithms, Quantum Spain highlights that several groups of institutes of the Spanish National Research Council (CSIC) are actively involved in their development within its Interdisciplinary Thematic Platform on Quantum Technologies.[xxxii]. One of them, the QUINFOG group at the Institute of Fundamental Physics, develops quantum optimisation algorithms and quantum machine learning. Together with it, the CSIC is researching new numerical simulation methods based on tensor networks, and a group at the Institute of Theoretical Physics is exploring quantum algorithms applied to condensed matter physics and high-energy physics processes. The group at the Centre for Research in Nanomaterials and Nanotechnology is experimenting with the use of cold atoms to simulate quantum processes, and is promoting the construction of a simulator and computer based on Rydberg atoms in Asturias. The Institute for Interdisciplinary Physics and Complex Systems has focused on new models of quantum computing and artificial intelligence for application to complex systems.
The collaborative network also integrates universities, public research organisations and companies. For example, a quantum computing laboratory has been created in Oviedo (CINN), a quantum communication laboratory in Madrid (ITEFI-CSIC) and an inter-university master's degree in quantum technologies in Spain has been launched through the Menéndez Pelayo International University (UIMP), in collaboration with nine other universities. The Instituto de Ciencias Fotónicas (ICFO), the Instituto Nacional de Técnica Aeroespacial (INTA) and the Instituto de Astrofísica de Canarias (IAC), which researches on the compensation of the adverse effects of atmospheric turbulence, are also international benchmarks in quantum. Interest in quantum technologies is present in the Spanish government's Defence Technology and Innovation Strategy, with a focus on their potential for cybersecurity, advanced data processing and secure communications. The Quantum Spain project.
It is interesting to follow the emergence of this new technological field, which is still largely pending the development of the fundamental principles on which it will expand, in the real economy in Spain. The energy multinational Iberdrola, for example, has promoted a project together with Multiverse Computing, winner of Digital Europe's Future Unicorn Award in 2024, for its technology for the compression of extended language models (LLM), on which generative artificial intelligence is built, by means of quantum algorithms.[xxxiii]. The collaboration between the two companies has been developed in northern Spain with the aim of optimising the installation of grid-scale batteries, which will become increasingly important as the energy transition progresses. Multiverse's solution uses quantum and quantum-inspired algorithms to select the optimal number, type and location of batteries. This helps to reduce the costs of adding batteries to the grid and increases their performance. More and more Spanish venture capital investment is going into quantum-inspired software. Bullnet Capital has led an investment round in Inspiration-Q[xxxiv], spin-off The CSIC's research centre, founded in 2021, is working on the development of such algorithms so that they are capable of running on conventional computers. The goal is also that they can be quickly and safely implemented on commercial quantum computers as they become commercially available. Inspiration-Q software focuses on hard-to-solve optimisation problems primarily for the financial sector.
[i] Algorithm (No date) NNLM. Available at: https://www.nnlm.gov/guides/data-glossary/algorithm (Accessed on: 11/07/2025).
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[iii] Mateu, P. (2024) From Marenostrum 4 to Marenostrum 5, The Evolution of Spain's Largest Supercomputer, National Geographic Spain. Available at: https://www.nationalgeographic.com.es/ciencia/marenostrum4-5-evolucion-supercomputador-mas-grande-espana_21183 (Accessed on: 11/07/2025).
[iv] Deutsch, D. (1985) ‘Quantum theory, the church-turing principle and the Universal Quantum Computer’, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 400(1818), pp. 97-117. doi:10.1098/rspa.1985.0070.
[v] Grover, L (1996) A fast quantum mechanical algorithm for database search. doi: 10.48550/arXiv.quant-ph/9605043
[vi] Bravyi, S., Cross, A.W., Gambetta, J.M. et al. High-threshold and low-overhead fault-tolerant quantum memory. Nature 627, 778-782 (2024). doi: 10.1038/s41586-024-07107-7
[vii] Cláudio Gomes et al. A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research, ACM Computing Surveys, 11 November 2024, doi.org/10.1145/3700874
[viii] George Lawton, “The future of quantum computing: Near- and long-term outlook”, TechTarget, 27 March 2025, accessed 18/06/2025
[ix] Krishna Kanagarla, Venkata Nagendra kundavaram, Quantum Computing-Inspired Machine Learning for Real Time Decision Making, IJMECE, 8 May 2025, doi.org/10.5281/zenodo.15236671
[x] Adarsh R., “Quantum Computing Outlook 2025: Key Innovation and Insights”, StarUs, 28 February 2025, accessed on 19/06/2025.
[xi] https://ec.europa.eu/futurium/en/content/quantum-manifesto-quantum-technologies
[xii] https://digital-strategy.ec.europa.eu/en/policies/quantum-technologies-flagship
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[U1]I think that in Spain it is more common to say "ordenador" than "computador" or "computadora".
[U2]This is what the RAE recommends: https://www.rae.es/dpd/miembro#:~:text=Cuando%20miembro%20se%20usa%20en,%2C%20los%20Estados%20miembros%2C%20etc.