The idea of quantum computing arose from the reflection made by scientists about the fundamental limits of computing. What they surmised is that if technology continued to comply with Moore’s Law (which states that the number of transistors in a microprocessor doubles approximately every 2 years), then the shrinking size of the circuits within a silicon chip would eventually reach a point where individual elements would not exceed the size of a few atoms.
Consequently, the physical laws that would govern the properties and behavior of circuits on a subatomic scale would no longer be the classical ones but rather quantum mechanics. This gave rise to the possibility of creating a computer based on the principles of quantum physics.
Feynman was one of the first to tackle this challenge in 1982 by creating an abstract model showing how a quantum system could be used to do calculations. The model explained how a machine could have the ability to perform quantum physics experiments inside a quantum mechanical computer. Later, in 1985, Deutsch proposed the possibility that a quantum computer could be general purpose and published a theoretical paper that went on to show how a quantum computer could have capabilities far beyond those of a traditional classical computer. After Deutsch published that article, the quest began to find practical applications for such a machine.
Applications
In recent years, there has been relentless progress in both quantum hardware and quantum algorithm development that has brought quantum computing much closer to reality. As an emerging paradigm, quantum computing offers the potential to provide a significant computational advantage over conventional classical computing by exploiting the principles of quantum mechanics.
The world’s leading technology companies such as IBM, Google, Microsoft, and Intel, as well as some ambitious start-ups like Rigetti Computing and IonQ, are racing to develop the first large-scale universal quantum computer that could solve many computationally complex and intractable problems in areas like data science, finance, drug design, etc. Let’s review some of the applications of quantum computing with the greatest potential today.
Machine Learning
Quantum computing promises to speed up machine learning algorithms to analyze classical data. Although it has not yet been fully demonstrated whether quantum machine learning can provide superior computational efficiency compared to classical methods, the results obtained in quantum principal component analysis and quantum neural networks look promising.
Efficient searching and sorting of large data sets have become high-priority tasks for many large firms. Another already widespread machine learning applications such as voice, image, and handwriting recognition have become challenging tasks for traditional computers in terms of speed and accuracy. Here is where quantum computing could be of great help, improving pattern recognition and processing these complex problems in a time span that would take traditional computers hundreds of years.
Robotics
Robots use high graphics processing power (GPU) to solve computational tasks that are very data-intensive, such as vision, movement, optimal control, etc. and where quantum computing could help perform calculations at considerable speed. Quantum computing may improve the ability of robots to sense their environment and, by using cloud-based quantum computing, solve highly complex problems.
In addition, the main kinematics issues associated with the mechanical movement of robots could be solved with quantum neural networks that recognize the moments of friction and inertia of the joints. Other typical issues of robot operation, such as identifying the reasons for the inconsistency between expected and observed behavior, could be solved using quantum algorithms. Quantum computing could also help reduce the complexity implicit in AI-based robotics by using quantum random walks to speed up response time and accuracy, rather than using information deduced from graph search.
Quantum cloud
Creating a safe and efficient environment for quantum computing in the cloud is an area that has great potential. A quantum cloud computer is a quantum computer that can be accessed over a network. Top tech companies like IBM (IBM Quantum), Google (GCP), Microsoft (Azure), and Amazon (Braket) have launched initiatives that combine quantum computers with cloud computing and that do not need to have a physical quantum computer installed. In this way, users have the opportunity to access quantum computers in the cloud to solve complex problems that require powerful computing.
The different quantum cloud computing services on offer today provide different architectures and performance levels. As competition continues to intensify among the big tech players, quantum cloud computing services will continue to offer specifications that promise ever better performance and faster runtimes.
Social networks
Social networks handle a huge volume of data every day as their use by millions of users continues to grow. Soon the amount of data in social networks will be so large that classic computing capabilities will no longer be able to process it easily and quickly in an acceptable time. Processing Social Big Data with relational databases where objects are semantically linked through multiple relationships is a major challenge.
Mining relational databases often requires enormous computing power in terms of hardware and software to deliver reasonably accurate and timely results. Therefore, to handle all this data and extract value from it, it will be necessary to have a large computing capacity that is also fast and efficient. Instead of the limitations presented by classical computing, quantum computing offers the ability to perform complex computation with social network data in an easy and efficient way, taking advantage, for example, of a graph theoretic representation of social network attributes to model sophisticated data structures and their interactions.
Quantum simulators
Although our capacity to predict complex systems has increased over time, state-of-the-art predictions still need new capabilities, especially in the field of business and management applications. However, the possibility of addressing these developments has been limited by the computational power currently available. Simulation models need to continuously calculate and recalculate customer flow, optimal mobility routes, prices, distribution, etc. Until now, simulations have used conventional computing tools and techniques, but with quantum computing it could be possible to solve these problems more easily and in less time in a controlled environment using small-scale “quantum simulators” of 50-100 qubits.
Quantum cryptography
Quantum cryptography is another application with great potential for the future of quantum computing. Quantum cryptography differs from traditional cryptographic systems in that the key element of its security model is based on the laws of quantum mechanics rather than classical mathematics. Since copying data encoded in a quantum state is not possible, the chances of being attacked by a cybercriminal are reduced.
Compared to traditional cryptography, quantum cryptography increases the probability of intrusion detection and improves performance. However, although with quantum computing firms could protect themselves better, it also adds the risk of being able to crack many of the conventional cryptographic systems used today, such as RSA, which uses the factorization of integers and is valid both for encrypting and for signing digitally. For example, by harnessing the power of quantum superposition with Shor’s algorithm, it is possible to factor very large numbers in a matter of seconds and crack data encrypted in this way.
Challenges
Although advances in quantum computing look promising and the tech industry is getting closer to “quantum supremacy” (solving a problem in a quantum computer that is intractable in a classical machine), there are numerous challenges ahead that will likely take years to resolve.
One of the most important is how to build a large-scale, fault-tolerant universal quantum computer that can unleash the full potential of quantum computing in real-world applications. Key milestones have been achieved in this regard. For example, in late 2021, IBM introduced Eagle, its most powerful quantum system with a 127-qubit processor, and the first to break the 100-qubit barrier. The system contained new chip cooling and packaging technologies, including a new cryogenic platform to keep temperatures low and make the system more stable. These technologies will serve as the building blocks for IBM’s next two quantum systems: the 433-qubit Osprey, scheduled for launch in 2022, and the 1121-qubit Condor, expected in 2023.
Developing a large-scale quantum computer has significant challenges, including the development of quantum hardware that reduces the effects of decoherence. Decoherence refers to the interactions that a qubit has with its environment and that can cause disturbances (or incoherent states) that collapse the superposition and lead to errors in the quantum information. For example, small variations in temperature or electric or magnetic fields can cause quantum information to degrade in the computer. Before any quantum computer can solve a complex problem, the industry will have to find a way to keep decoherence and other sources of error at acceptable levels; meanwhile the field of quantum error correction will remain one of the most active research areas in the field of quantum computing. Tests conducted with the NISQ (Noisy Intermediate-Scale Quantum) algorithm, specifically designed for quantum processors, seem promising to address these technical challenges.
Finally, it is quite relevant that quantum computing still does not have its own high-level programming language. What this means is that the algorithms are processed by building quantum circuits to which available quantum gates or operations are systematically applied to find the desired solution. An example of industry efforts to address this issue is a visual programming tool provided by IBM for beginners to code and learn how to use a quantum computer.
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