International Journal of Computers and Applications

Quantum computing (QC) is an emerging area of computer science that is mainly based on the principles of quantum theory. Quantum computers are more capable of solving certain complex computations more efficiently than classical computers in just a fraction of a second. Since classical computers are modeled based on linear computations, it is difficult to solve nonlinear problems. Even supercomputers are limited to solving nonlinear problems as it analyzes each combination in a sequential manner which may take a long time. It does not have the working memory to hold the multiple combinations of real-world problems. As QC has non-linear properties of nature, it will be the most suitable for solving non-linear problems which includes solving many complex problems in biology, chemical, and energy sectors. QC has been widely used in medical research, the aviation sector, data analytics, finance, weather forecasting, national security, etc.

In QC information is processed using qubits which can be either 0 or 1, or else can be a superposition of both states simultaneously whereas classical computers can encode the information in binary digits (bits) only that take the value either 0 or 1 which restricts their ability. Entanglement is a quantum mechanical phenomenon that allows qubits to be dependent on each other which means a change in one state will immediately reflect in another state. This factor guarantees secure encryption when compared to conventional cryptographic security as it has limited computation power. On the other hand, QC may potentially make current encryption methods unsecure; quantum cryptography would be an appropriate solution to overcome this issue. Unlike traditional encryption methods, Quantum Key Distribution (QKD) is unbreakable that ensures secure transmission of sensitive information between shared parties. However, breaking modern encryption algorithms used on the internet like RSA fairly requires large quantum computers. Furthermore, quantum annealing is the optimal solution for solving complex optimization problems in a more efficient manner which is important for the healthcare and finance sector. As more and more connections between qubits, the superposition becomes more susceptible to noise.

Many leading tech giants are racing to build their quantum computers and also they are striving to achieve quantum supremacy. Since quantum systems are prone to errors, noise, loss of coherence, organizations are focusing to build more resistant quantum computers with low-error qubits which are very crucial for their operations. Recently, scientists developed the first quantum algorithm to characterize the noise across large systems. However, there are some significant challenges in quantum computing while implementing real-world quantum computers. Most importantly, it requires large and expensive cooling systems to bring down the temperature to near absolute zero. Meanwhile, this low temperature will create stability problems in itself. Though quantum computers are incredibly fast and effective, there is still a technological race to scale up quantum computers for large-scale operations. In this blog, we intend to bring out the significant advances of quantum computing aimed at solving real-world problems. We welcome researchers and practitioners to present their novel contributions in this regard.

- Quantum computing and information: A new era of computer science.
- Impact of quantum computing on today’s cryptography: Challenges and opportunities.
- Quantum Key Distribution protocols in quantum cryptography.
- Resource consumption estimation for quantum computing systems and applications.
- Security challenges in quantum information and computing.
- Role of quantum inspired algorithms for reducing noise and errors in quantum computing.
- Key challenges in the future of quantum computing.
- Quantum networks and communications for information processing.
- Quantum inspired algorithms for optimizations of complex large scale quantum computers.
- Quantum annealing solutions for resource optimization of quantum systems.
- Designing and developing quantum algorithms for large scale quantum computers.
- Quantum computing towards information security and key management.
- Reverse quantum annealing approaches for optimization problems

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