Google claims that its quantum computer has been able to perform a calculation in a matter of seconds, a task that would take a traditional computer thousands of years. Even though quantum processors currently only handle simple problems, they are expected to have a significant impact on the future of the pharmaceutical, manufacturing, and banking industries.
Google’s recent article on quantum computing published in the scientific journal, Nature, has been a source of intense debate. The Mountain View company claims to have executed a computational task in 200 seconds (three minutes and 20 seconds). The operation involved performing a random sampling calculation, which would have taken the world’s most powerful supercomputer 10,000 years. This would be the first empirical demonstration of quantum supremacy, a term that describes the point where quantum computers have an unparalleled advantage over classical computers — when they can perform a task that classical computers cannot.
Shortly after Google’s announcement, IBM countered the claim: “Big blue” believes that a supercomputer based on classical computing could run the same experiment in just two and a half days. Both companies are the leading contenders in the race to successfully develop this technology and are independently responsible for the greatest advances made to date.
Quantum computers use qubits, as opposed to the conventional bits of classical computing. Unlike classic bits, whose value is limited to a binary range — 0 or 1 — qubits can exist in an intermediate state of “quantum superposition.” Consequently, quantum computers can operate with an inordinate amount of data at the same time. Quantum supremacy consists of performing a task in a quantum computer using exponentially fewer resources than a traditional computer would use. Experts agree that many hours of work — and probably many more articles — will be required in order to put the topic of quantum supremacy to rest. “What is relevant is that quantum technology is now beginning to mature enough to be able to compete with classical computing, at least in certain specific tasks,” explains Samuel Fernández Lorenzo, who works with BBVA’s New Digital Business (NDB) leading research into quantum algorithms.
He points out that the term “quantum supremacy” lends itself to misinterpretation outside technical fields: “It was a term coined by a professor at the California Institute of Technology, John Preskill, to refer to the time when we would be able to build a quantum processor that would be capable of performing a particular task that a classic computer could not execute in a reasonable amount of time.” He stresses that, even if this event has been achieved, it does not come close to implying that a quantum computer is more appropriate or otherwise better to use for any task than a traditional computer.
“These applications are exploratory and depend on the size of the processor, the speed and the amount of time quantum states persist in the processor”
In fact, in the article where IBM researchers refute Google’s claim, the company argues that the term ‘quantum supremacy’ itself leads to misunderstandings, among other reasons because “quantum computers will never reign ‘supreme’ over classical computers, but will rather work in concert with them, since each have their unique strengths.”
The computer built by Google, with 53 operating qubits and high processing speed, can execute one specific task. In fact, quantum computers available to researchers today only serve to test algorithms for small problems. Juan José García Ripoll, a researcher at the Institute of Fundamental Physics (IFF), part of the Spanish National Research Council (CSIC), explains that the most remarkable problems they address include the simulation of quantum systems, like small molecules; optimization problems; and small machine learning tasks, such as data classification. “These applications are exploratory and depend on the size of the processor, the speed with which we can perform operations, and most of all, the amount of time quantum states persist in the processor, which is still very brief,” he says.
Optimization and machine learning
But these algorithms can be applied to increasingly large problems, to such an extent that they will be able to solve systems that exceed the reach of classic computers. Ripoll maintains that this could happen soon with the simulation of molecules and materials that are described by quantum mechanics, but might take longer with respect to problems where there are very good classic algorithms, the areas of optimization and machine learning, for example.
In addition to Google, other companies like IBM, Rigetti, and IonQ have quantum computers that are available to the public through contracts and one-off partnership agreements. Currently, it is difficult to forecast which sectors quantum computing will impact the most, since the algorithms have only been tested on what is still a very limited set of problems. Published works “point to promising results in the simulation of quantum systems” such as molecules of intermediate size and exotic materials and processes like photosynthesis or the transformation of solar energy into electricity. This could have applications in the manufacturing, pharmaceutical, and petrochemical industries.
Quantum computing could also have a strong impact on banking. “There are a lot of financial problems related to investment strategy optimization, which involves researching a huge number of possible portfolio combinations to find the one that best fits specific criteria. A quantum computer can help during this exploratory task and provide us with investment options that are better than those calculated by classic methods,” the researcher explains.
This technology can also serve to streamline routine activities such as risk calculation or the valuation of financial products that are based on numerically simulating various financial scenarios. In addition, algorithms are used in numerous finance industry decision-making processes. For example, to recognize fraudulent transactions: “It is not unreasonable to think that quantum computers could be used in the future to speed up these kinds of algorithms and produce better results.”
Fernández Lorenzo argues that the main challenge facing this technology resides at the level of engineering, “because it is very difficult to build larger and large, better quality processors.” Moreover, there is the problem of ‘impatience:’ “Nobody today seems to question that artificial intelligence has the impressive potential to transform our society and our economy. All the same, this domain has required a minimum of four decades to get where we are today. With quantum computing we envision a challenge of the same magnitude. We will need to wait patiently for some years before we can fully appreciate the full splendor of the quantum computing era.”
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