By using a superconducting processor, Google Quantum AI group demonstrated that a logic qubit realized by surface code of quantum error correction performs better when the number of physical qubits increases. Two surface codes to encode logic qubits for scaling are realized experimentally, with multiple cycles of quantum error correcting assisted by ancillary qubits. This result can be considered as an important step toward fault-tolerant quantum computers. In this review paper, we introduce briefly the mechanism of quantum error correction. As an example, Shor’s nine-qubit error correction code is explained. Then, the new experiments of Google quantum AI group are introduced to show their significance in scaling. The advances in other quantum error correction experiments are also reviewed. Finally, the development of quantum computers is discussed.
Quantum computing is a big change in how to do calculations, especially good for solving complex problems that are hard for traditional computers. These problems often get worse quickly as they grow bigger, making them very difficult to solve efficiently. Although quantum computers have some big advantages, they still have some issues like limited number of qubits, noise, and problems with staying stable for long periods. To deal with these problems and make the best use of both quantum and classical methods, this research proposes a new system that mixes classical methods such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with quantum methods like Quantum Approximate Optimization Algorithm (QAOA) and different versions of Grover's search. [14] [15] [1] [5]The classical methods are good at finding good solutions and working reliably, while the quantum parts can help in certain tasks, like searching or improving calculations. The new system is tested on real problems in areas like logistics (like planning delivery routes), network routing (like finding the fastest paths), and job scheduling (like assigning tasks with limited resources). A way is developed to turn these problems into a form that quantum computers can understand, called QUBO. [10]The tests use real data and simulated quantum setups (like Qiskit and D-Wave Ocean SDK). The results show that this new system works better than using only classical or only quantum methods in terms of the quality of the solutions, how fast they find the answers, and how long they take. This study shows the potential of using a mix of quantum and classical methods to solve problems more effectively and sets up a foundation for future research in using quantum methods to help make better decisions. [12] [18]
From Pharmacology to Cryptography and from Geology to Astronomy are some of the scientific fields in which Quantum Computing potentially will take off and fly high. Big Quantum Computing vendors invest a large amount of money in improving the hardware and they claim that soon enough a quantum program will be hundreds of thousands of times faster than a typical one we know nowadays. But still the reliability of such systems is the main obstacle. In this work, the reliability of real quantum devices is tested and techniques of noise and error correction are presented while measurement error mitigation is explored. In addition, a well-known string matching algorithm (Bernstein-Vazirani) was applied to the real quantum computing device in order to measure its accuracy and reliability. Simulated environments were also used in order to evaluate the results. The results obtained, even if these were not 100% accurate, are very promising which proves that even these days a quantum computer working side by side with a typical one is reliable and especially when error mitigation techniques are applied.