When people discuss how pharmaceutical companies can leverage quantum computing the usage of computational chemistry for drug discovery is always the first thing that is mentioned. But pharmaceutical companies may be in an unusual position because they can also potentially use quantum computing for two additional use cases. Part of the process of bringing a drug to market includes clinical trials which can generate a ton of data which can potentially utilize quantum machine learning to analyze and to uncover trends. And a third application is in the manufacturing and distribution process to use quantum optimization to figure out the best way to utilize its manufacturing plants efficiently as well figure out the best logistics for distribution of the drug.

Many other industries don’t have the opportunity to leverage quantum computing in as many different ways. The finance industry, for example, does require computational chemistry nor do they manufacture or distribute anything. Aerospace companies may require computational chemistry for material design and quantum simulation for computational fluid dynamics. They may also use optimization for creating the most efficient flight schedules . But we aren’t aware of any aerospace companies that do clinical trials and or use quantum machine learning to perform complex data analytics.

IPQ Analytics is a company formed in 2012 to provide the healthcare industry with decision support solutions that can analyze complex networks of data to help organizations enhance their decision making, improve outcomes and achieve higher return on investments. Now, they are partnering with Quantum Computing Inc. (QCI) to utilize QCI’s QGraph and Qatalyst software to see if quantum and quantum-inspire tools offered by QCI can improve upon IPQ’s existing classical computing solutions. One example they provided to use is called sub-group analysis. Think about a situation where a large scale drug trial takes place and the results for the overall population don’t look promising. But perhaps there is a small group of people who share a common characteristic where the drug trial does look like it is returning good results. These types of insights could be overlooked because the data analytics to bring them out may be too complex to discover with classical algorithms. This is where things like community detection on a quantum computing could provide a benefit because programs like QGraph can perform a complex cluster analysis to uncover important results that would be overlooked with a classical computing algorithm. Other areas where sophisticated data analytics can be utilized include helping to improve diagnostics, reducing unnecessary tests and minimizing the use of ineffective patient treatments. With worldwide spending on healthcare estimated to reach $8.8 trillion in 2021, even small improvements in these areas can provide huge payoffs.

In other QCI news, we reported last January that QCI had applied for up listing on the NASDAQ stock exchange. The also announced that this has finally been completed and trading of their stock started on July 15th under the ticker symbol QUBT. We’re not sure what took NASDAQ so long, but perhaps they need a quantum computer so they can process these types of applications at a much quicker rate!

July 22, 2021