According to TrendForce, the global quantum computing market was valued at US$470 million in 2021, an increase of 16.7% compared to 2020. This market is mainly led by China and the United States, driving global quantum computing and its technological progress, especially in upper-layer software. In terms of algorithmic speed, small-scale problems have been put to the test through experimentation. The market is expected to reach US$580 million in 2022, with an annual growth rate of approximately 18.8%, and current growth rate expanding every year until 2027.
According to TrendForce, as stated in the Chinese government’s plan for software and information technology services, its quantum technology policy will be further implemented from a national level to departments including national defense, industry, and technology and more targeted policies will be released through tiered departmental levels such as for AI, quantum information technology, biotechnology, semiconductors, and autonomous systems. To this end, the Chinese government is establishing relevant laboratories in Beijing, Shanghai, and Hefei to promote the rapid development of quantum technology and quantum computing cloud platforms.
When China launched its “Five-Year Plan” in 2006 to promote economic and industrial development, it also focused on the development of quantum science and technological breakthroughs, as well as the deeply integrated development and application of quantum computing in emerging technologies such as AI, edge computing, big data, IoT, and cloud such as advanced space quantum communication technology and quantum computing combined with AI/ML, IoT, and cloud, providing assistance to the Chinese Academy of Sciences’ quantum satellites, the University of Science and Technology of China’s quantum computer, and other quantum processors to achieve breakthroughs in technology and functional characteristics. Therefore, the cumulative investment in China’s quantum field is estimated to reach US$15 billion in 2022.
Main applications of China’s quantum computing market
Considering the immense size, extremely harsh operating environment, and high price of quantum computers, quantum computing applications are rapidly developing towards cloud platforms. Therefore, research on quantum computers primarily focus on four types of applications: simulation, optimization, cryptography, and machine learning. “Simulation” is most used in processes that occur in nature such as weather forecasting, mid- and long-term climate deductions, and polar climate change. It is also widely used in fluid mechanics, drug discovery, battery design, and high-frequency trading, derivatives, and options pricing in the financial industry.
“Optimization” is the use of quantum algorithms to determine the best solution among a set of feasible options and is mostly used for risk management in traffic arteries, logistics, self-driving navigation systems, and financial investment portfolios. “Machine learning” is used to identify patterns in data and statistics, enhance the training of machine learning algorithms, accelerate AI development, and introduced to self-driving cars and financial systems to prevent fraud and money laundering.
As enumerated above, the scope of quantum computing applications is gradually expanding, covering fields including supply chain, finance, transportation, logistics, pharmaceuticals, chemicals, automobiles, aviation, energy, and meteorology. Sectors such as pharmaceuticals, chemicals, and new materials use quantum operations to analogize molecular properties, directly analyze and obtain large molecular properties through a computerized digital format, shorten the time for theoretical verification, and thereby accelerating drug research and development and the development of new materials.
In the automotive field, in order to accelerate the promotion of electrification strategies, major carmakers have applied quantum computing to chemical analogies and are committed to developing batteries with better performance. In the aerospace field, quantum computing is used to solve some of the most difficult challenges facing the aviation industry, from basic materials, product research and development, machine learning optimization, to complex system optimization, and are even changing the way aircraft are made and fly.
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