We live in the Information Age, and society is becoming increasingly reliant on big data and complex networks. A few examples of complex networks are large social networks, the interworkings of epidemic disease spreading, and telecommunications networks that we all use to access the internet or make phone calls. An interesting feature of complex networks is that they often contain a community structure. How can quantum computing be used to study community structure within these networks? Before answering this, what is meant by the term “community”, and what significance does it have in a network?
Your Guide to the World of Quantum
Artificial intelligence is set to become a $126.0B industry by 2025.¹ Machine learning is a major part of artificial intelligence involving algorithms that improve in performance over time given more data. Applications of machine learning include image and speech recognition, assisted medical diagnoses, traffic prediction, self-driving cars, and stock market trading. New applications are springing up all the time as society becomes more complex. However, the rapid growth in the amount of available real-world data requires increasingly faster classical computers (“classical” meaning not quantum). These computers are limited because the computational resources quickly become unreasonable for machine learning for large, complex datasets.
Many problems in finance take the form of mathematical optimization problems. As an example from portfolio optimization, an asset manager may be tasked with allocating funds across a pool of assets while maximizing the return at a given level of risk. However, conventional approaches often fail when computational resources cannot handle the amount of “noisy” real-world data (i.e., useful data that is corrupted or distorted by other, meaningless data), or extrapolating for missing data, or account for the correlations between all assets.
Quantum computing research and experimentation continues to make progress towards addressing complex computational problems. These include modelling systems in nature as they relate to chemistry and physics. Beyond this, quantum computing research has also shown promise in tackling problems in logistics and finance.
The consensus among many quantum computing practitioners is that chemistry, specifically quantum chemistry, has great potential to be the first field to benefit from quantum computing, as the simulation of molecules can be more efficiently handled using quantum computing methods. This could accelerate materials innovation for large-scale industrial applications, such as catalyst design to facilitate chemical synthesis for more-efficient…
The need to create new medicines faces major challenges due to the increasing costs of drug development and the high failure rate of drug candidates. These challenges are faced due to the laborious and expensive processes involved. For example, in order to develop an effective drug, it is necessary to test whether a drug candidate has sufficiently acceptable properties of ADME (absorption, distribution, metabolism, and excretion) and toxicity. Thus, there is a high demand for innovation and great opportunity for improvements in the drug discovery process.
Every year there are hurricanes, extreme heat waves, tornadoes, and other extreme weather events, resulting in thousands of deaths and billions of dollars in damages. Prediction of extreme weather further in advance and with increased accuracy could allow for targeted regions to be better prepared in order to reduce loss of life and property damage.
In the 1QBit Guide to Advanced and Quantum Computing, developments from 2019 were presented that indicate we are on the path to fully realized quantum computing. Advancements in quantum computing are strongly dependent on the development of better qubits. The key to creating more-powerful machines is building more-reliable, better-connected, and longer-lasting…
Get insights on trends and predictions in advanced and quantum computing, and find out how to get quantum ready.