The Future with Quantum Computing
This article is authored by Wyatt Warren, intern at Capgemini Applied Innovation Exchange. Warren is a senior at Hobart College in New York.
Introduction
With advances in technology like smartphones, AI, and space travel, it seems impossible that we may still be living in the dark ages. But we are. When looking at the world and how it can be affected by the advancements in quantum computing, you can see that quantum computing can disrupt every industry.
How do Quantum Computers work?
Let’s start with the basics; classical computers work by manipulating binary digits, meaning that they use 1’s and 0’s or “bits.” Quantum computers break free of classical computing by substituting “bits'' with “qubits”. Qubits, or quantum bits, operate according to the laws of quantum mechanics. For those who don’t have a background in engineering, this is the theory that physics works differently at the atomic and subatomic scale. In the simplest terms for lay people, quantum computing is exponentially more powerful, making it faster, more accurate and efficient than your traditional computer at home.
Why Quantum Computing?
When people ask me this question, I always have trouble answering. Trying to encompass all the incredible abilities and applications of what we can do with Quantum Computers is like asking someone to tell you the capacity of the internet in a few short sentences. While I can’t list everything here, some notable abilities of quantum computers include:
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Changing the World of Health
This pandemic showed us the importance of having a high-quality and accurate health care system. Now more than ever, it is essential for our hospitals and health care professionals to be equipped and utilize the most advanced tools possible to help patients.
Diagnoses
Currently, diagnosing patients is invasive, complex, and costly. Additionally, you must factor in human error, with the diagnostic inaccuracy rate falling in the 5-20% range[1]. Current hospital technology like MRI, CT, and X-Ray machines all produce images that are analyzed and processed to identify issues. These machines come with many challenges such as disruptions due to movement, making the image difficult to process. Quantum Computers would ease this process by allowing for much more accurate medical image analysis by performing image matching on massive data sets. While a doctor may only be able to reference a few thousand images, a quantum computer can reference every single accessible medical image to help identify medical issues.
Medicine
When treating patients, doctors know that one of the most critical factors is early diagnosis for a successful treatment protocol. It can often be difficult to accurately prescribe people medicine as they are not aware of all the factors that fall into diagnoses. Every year, nearly 10,000 people die due to medication errors which causes a financial strain of almost 40 billion dollars treating roughly 7 million patients who have suffered from these errors[2]. One way of reducing this is beginning to understand the workings of our medicine at a cellular level. For example, by considering the genomic features of cancer cells and the chemical properties of drugs, we can begin to explore models that can predict the effectiveness of cancer drugs at a granular level[3]. Quantum Computing would allow for further acceleration in this space, changing the way we approach medicine.
Efficient. Powerful. Accurate. These are the attributes that make quantum computing such disruptive technology in the coming future.
[1]Singh, Hardeep, Ashley N. D. Meyer and Eric J. Thomas. “The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations.” BMJ Quality and Safety. April 17, 2014. https:// qualitysafety.bmj.com/content/qhc/23/9/727.full.pdf; Graber, Mark, L. “The incidence of diagnostic error in medicine.” BMJ Quality and Safety. June 15, 2013. https://qualitysafety.bmj. com/content/qhc/22/Suppl_2/ii21.full.pdf
[2] Tariq, Rayhan A, and Yevgeniya Scherbak. 2019. “Medication Errors.” Nih.gov. StatPearls Publishing. 2019. https://www.ncbi.nlm.nih.gov/books/NBK519065/.
[3] Menden, Michael P., Francesco Iorio, Mathew Garnett, Ultan McDermott, Cyril H. Benes, Pedro J. Ballester, and Julio Saez- Rodriguez. “Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties.” PLOS One. April 2013. https://journals.plos.org/ plosone/article/file?id=10.1371/journal.pone.0061318 &type=printable
Incredible! Thanks for sharing Andreas Sjöström. Talk about highlighting and developing emerging leaders 😊
Thanks, Andreas!