Quantum Computing in 2020: A Systematic Review of Algorithms, Hardware Development, and Practical Applications
Abstract
Quantum computing is the developing field at the intersection of computer science and physics with quantum mechanics principles to solve complex problems far outside the abilities of traditional computers. This systematic review analyzes key progress in quantum developments, hardware development, and practical applications in 2020. In terms of algorithms, significant progress was made in cross-quantum conventional methods such as the Variation-Quantum-Eigen solver (VQE) and Quantum-Approximate-Upgrading-Algorithms (QAOA) and alongside breakthroughs in quantum machine-learnings. The hardware front and improvements in qubit stability, error correction, and portability marked the pivotal year for quantum processors, with companies like IBM and Google in charge. Practical applications in cryptography, drug discovery, and optimization remain largely experimental, and industries are increasingly exploring quantum potential. The review also identifies ongoing challenges, including qubit coherence, error rates, and scalability, while outlining future research directions. These challenges in 2020 laid the strong foundation for quantum computing's transition from theoretical promise to practical reality.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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