Although the idea originated as early as the mid-19th century, quantum computers (and their technology) are still relatively new. Quantum computers are believed to have great potential, but how do they differ from conventional computers – and what are their strengths?
Unlike classic, deterministic computers, whose information is processed as binary bits, quantum computers use the properties of quantum physics (thus their name), thereby creating entirely new possibilities for the processing of data.
The key to the power of a quantum computer lies in quantum bits, or “qubits.” In classical physics, a particle always exists in a clearly definable state. Qubits, however, are able to be in both of the binary ground states of a particle simultaneously. This is known as “superposition,” or the property that allows quantum computers to simultaneously store and process information in many different states. This simultaneous processing is one of the major strengths of quantum computers.
Multiple qubits can also be combined to create powerful quantum systems. In quantum mechanics, this is known as “entanglement.” When several qubits are entangled, their individual states are no longer independent of each other and they can henceforth only be considered as an entire system. This allows for the modeling of complex problems and quantum registers.
Quantum computers also differ from conventional computers in the way they read information from such quantum systems. With classic computers, all information can be read at any time without the need for modifications. This is not possible with quantum computers. Quantum mechanics dictates that the measurement of a quantum state will cause the system to collapse into one of the many possible states at the time of the measurement. The measured state is arbitrary, and can never be predicted.
From idea to implementation
The first quantum computers (based on the properties above) arose in the 1990s. In 2019, quantum supremacy was achieved for the first time by a team of researchers. Concretely speaking, this meant that a quantum computer was shown to be many times faster than the fastest supercomputer (to date) in solving a particular problem. In this case, a quantum computer from Google called “Sycamore” needed just 200 seconds to solve the “random circuit sampling” problem, which – according to the researchers – would have taken the fastest conventional computers 10,000 years to crack. The results of the study sparked many debates in the industry. In addition to vulnerabilities in Sycamore’s error correction, one of the main critiques was that the researchers intentionally chose a problem that was perfectly suitable for quantum hardware, but which had no practical use. Despite the criticism, this success is widely seen as an important milestone in the era of quantum computing.
Which practical problems can actually be solved with the help of a quantum computer, and for which tasks is the technology unsuitable?
To answer these questions, it should first be noted that – unlike conventional computers – many different technologies can be used to implement quantum computers. Each of these technologies has its advantages and disadvantages. In addition to (often completely) different hardware, there are currently two types of quantum computers that differ in their basic approach to executing algorithms.
Universal quantum computers – The (almost) all-rounders
On the one hand, we have universal quantum computers, whose approach is extremely similar to that of computer engineering. These computers implement algorithms as quantum circuits. A circuit can be implemented either directly with certain (parameterized) quantum gates, or generated from a mathematical formulation of the problem using appropriate tools and frameworks. The circuit is then evaluated by the quantum processing unit (QPU). Currently, it is common practice to use “hybrid algorithms” for certain problems, in particular due to the controversy surrounding the measurement of the results of quantum computers. A hybrid algorithm (e.g., VQE or QAOA) executes a parameterized quantum circuit several times and repeatedly adjusts the parameters until the result is found. The targeted adjustment of the parameters is performed by a classical computer – hence the name “hybrid algorithms.” Universal quantum computers are suitable for a wide variety of tasks. They can be used to perform complex simulations, creating entirely new possibilities in the field of medicine (for example). They can also be used to solve optimization problems (such as path-finding tasks) and execute cryptography procedures. In addition, great things are expected of these computers in the areas of neural networks and artificial intelligence.
Adiabatic quantum computers – The optimizers
The second main type of quantum computers are adiabatic quantum computers, or quantum annealers. Quantum annealers are based on a concept from metallurgy – namely, the melting of metals. When red-hot metals cool down, their molecules settle into an energetically favorable state. This state is called the “ground state,” and is vital to running an algorithm on an adiabatic quantum computer. Unlike with universal quantum computers, the problem to be solved with an adiabatic quantum computer must be presented using the Ising model. The Ising model is a lattice model in physics in which the nodes represent the individual atoms of a physical system. With this model, it is assumed that each atom has a positive or negative magnetic moment. The rendering comes relatively close to models produced by current quantum hardware, and makes it easier to create the corresponding algorithms. Specifically, the problem is modeled in the form of a quadratic function, or the so-called “energy function.” The ensuing Ising model is then embedded in the QPU of the annealer, and the individual qubits configured accordingly. As per the laws of quantum physics, the system moves from this energetically excited initial state to the ground state. The ground state of the system is then measured, and the solution is found. The peculiarity of quantum annealers is that they are only suitable for optimization problems – but in this area, they are already proving extremely beneficial.
Quantum computers in the insurance industry?
In summary, quantum computers are suitable for many problems that could benefit from simultaneous computations, and for which approximate solution strategies are acceptable. Practical examples of applications that could also be of interest to insurance IT include the logical optimization of database queries and searches for specific elements in unsorted databases. Grover’s search algorithm speeds up the latter quadratically compared to conventional methods. Algorithms that include such a search step could be sped up significantly in this manner.
Quantum computers are also capable of generating true “indeterminacy.” This may sound trivial at first, until we consider the limits of conventional, deterministic computers in this area. Furthermore, indeterminacy can be extremely interesting for certain simulations (for example). On the other hand, quantum computers are still unsuitable for the execution of simple arithmetic operations (among other things).
One of the major drawbacks of quantum computers involves the implementation of hardware, which can be extremely onerous and flawed. Both types of quantum computers are still relatively limited in their computing power as they do not yet possess the quantities of qubits that are necessary for more complex calculations. They are also prone to errors, due to the fact that their qubits (and the quantum systems generated therefrom) are still relatively unstable. Moreover, the more qubits involved in a calculation and the higher their degree of entanglement, the more error-prone the computer becomes. Most error correction strategies are still based on repeated calculations, which further lessens the edge quantum computers have over their conventional counterparts.
If these obstacles can be overcome, quantum computers will surely live up to their incredible potential and revolutionize many areas of research and technology in the future.
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