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Quantum Computers in Healthcare Industry

Healthcare is perhaps one of the most globally lucrative industries, as it takes more than 10% of the GDP of most developed countries


Quantum Computation, employs quantum effects to dramatically speed up certain calculations, such as number factoring. Quantum computing does not merely provide an incremental speedup. It is the only known technology that can be exponentially faster than classical computers for certain tasks, potentially reducing calculation times from years to minutes.


In healthcare, as in other industries, using quantum computers in concert with classical computers is likely to bestow substantial advantages that classical computing alone cannot deliver. From developing new drugs to allocating patient care, there are many different areas within the healthcare industry that can significantly benefit from quantum computing. In the healthcare industry, quantum computing could enable a range of disruptive use cases for providers and health plans by accelerating diagnoses, personalizing medicine, and optimizing pricing. As a result, there is now a race toward quantum applications.


Quantum computers have already begun permeating our healthcare systems.


Quantum Computing And Health Care

In the near future, for pharmaceuticals, quantum could be applied to improve patient selection and design in clinical trials, more quickly generate new molecules with a desired set of biological properties, better predict drug response and speed a drug’s time to market, even for various diseases that can’t be treated yet, some experts say.


For healthcare, a quantum computer could be used to optimize drug design and the drug testing process. Quantum computers can also perform simulations and could compute accurate simulations of a new drug on virtual human subjects, only within a few hours. This would save drug companies money and time, as well as remove the number of test subjects for a study, be it animal or human test subjects.


This process has already been tried by the company InSilico Medicine, which was able to develop a new drug candidate in 46 days using a simulated algorithm. Using quantum computers can speed up the drug design and test process, offering new medicines that could save potentially thousands of lives. For drug companies, this would save them thousands, if not millions, in years of drug testing and drug development.


Quantum computers could be particularly useful in tackling problems that involve:
– Chemistry, machine learning/artificial intelligence (AI), optimization, or simulation tasks. In fact, machine learning has shown potential to be enhanced by quantum computing and is symbiotically helping drive quantum advances
– Complex correlations and interdependencies among many highly interconnected elements, such as molecular structures in which many electrons interact
– Inherent scaling limits of relevant classical algorithms. For instance, the resource requirements of classical algorithms may increase exponentially with problem size, as is the case when simulating the time evolution of quantum systems.


1. Diagnostic assistance: Diagnose patients early, accurately, and efficiently

Early, accurate, and efficient diagnoses usually engender better outcomes and lower treatment costs. For example, survival rates increase by a factor of 9 and treatment costs decrease by a factor of 4 when colon cancer is diagnosed early. At the same time, for a wide range of conditions, current diagnostics are complex and costly. Even once a diagnosis has been established, estimates suggest that it is wrong in 5–20 percent of cases.


There is a growing trend of applying machine learning to aid with patient diagnostics. Much of machine learning is about “pattern recognition.” Algorithms crunch large datasets of patient information to find signals in the noise, and the goal is to leverage comparisons made to help identify a diagnosis.


With AI making significant strides in accelerating disease detection, it seems that quantum computing, combined with AI, will create significant shifts in the healthcare industry.  With quantum computing, we’ll be able to do this processing orders of magnitude more effectively than with classical computing. Quantum computing will allow doctors to compare much, much more data in parallel, simultaneously, and all permutations of that data, to discover the best patterns that describe it.


Quantum computing has the potential to improve the analysis of medical images, including processing steps, such as edge detection and image matching. These improvements would considerably enhance image-aided diagnostics.


Furthermore, modern diagnostic procedures may include single-cell methods. One challenge is the classification of cells based on their many physical and biochemical characteristics. These cause the feature space, that is, the abstract space in which the predictor variables live, to be large (high dimensional). Such classification is important, for example, in distinguishing cancerous from normal cells. Quantum enhanced machine learning approaches, such as quantum support vector machines appear poised to enhance classification and could boost single-cell diagnostic methods


AI entrepreneur Gary Fowler elaborated on these predictions in a 2021 Forbes article, writing “Quantum computing will be another useful tool to find answers to diseases such as Parkinson’s, cancer, and other ailments that take so many lives each day.” As experts warn about the rising threats of undiagnosed diseases like colorectal cancer in younger generations, quantum computing can create an earlier warning system, making early screening easier and more accessible to a wider audience.



Radiation therapy is the most widely-used form of treatment for cancers. Radiation beams are used to destroy cancerous cells or at least stop them from multiplying. Devising a radiation plan is to minimize damage to surrounding healthy tissue and body parts is a very complicated optimization problem with thousands of variables. To arrive at the optimal radiation plan requires many simulations until an optimal solution is determined. With a quantum computer, the horizon of possibilities that can be considered between each simulation is much broader. This allows us to run multiple simulations simultaneously and develop an optimal plan faster


Drug Research and Drug Design

Molecular comparison is an important process in early-phase drug design and discovery. Today, companies can run hundreds of millions of comparisons on classical computers; however, they are limited only to molecules up to a certain size that a classical computer can actually compute. As quantum computers become more readily available, it will be possible to compare molecules that are much larger, which opens the door for more pharmaceutical advancements and cures for a range of diseases.


Drugs and drug design are just two factors within the healthcare industry that help with patient well-being. “We are really only now beginning to evaluate this,” Keinan added. CEO and Founder of Polarisqb, Shahar Keinan. Polaris is one of the leading companies using quantum computing to optimize drug design, just one of the many subsectors featured within the healthcare industry.

“Because if you can design drugs that are going to be better, they are more efficient with fewer side effects, right? Safer drugs mean the rest of the drug development process is going to be faster and cheaper. And that means that your cost of medicine is going to go down, which means you can develop drugs for smaller patient populations.” With quantum technology being used in both chemical simulations and optimizations for drug development, this can significantly lower the production costs for these drugs. As Keinan stated, this frees up drug companies to pursue cures for more specific ailments. While current drug companies mainly target larger diseased populations, as there is a higher profit, quantum computing can reduce the constrain and allow for new drug research into other illnesses.


Protein Folding

One specific way that quantum computing can assist in drug design is by studying protein folding. Proteins are the basic building blocks of life. Malfunction of a given protein is frequently due to its being wrongly folded. While the chemical composition of proteins is quite well known, their physical structure is much less well understood. Obtaining more detailed knowledge of the way proteins are folded can help lead to the development of new therapies and medicines. A quantum computer will in theory be able to simultaneously test a huge number of possible protein fold structures and identify the most promising ones


Not only would simulating and analyzing the folding process help with drug discovery, but it also has wider implications for other sub-industries like nutrition, oncology, and others. According to a recent article by Booz-Allen: “Using a quantum algorithm called a Variational Quantum Eigen-solver, it’s possible to produce a statistical representation of possible folds in an amino acid chain, with the result being a prediction for the protein configuration we would observe in nature.” Polaris takes this process one step further by looking at the potential toxicity of the molecules as they pass through the brain’s blood-brain barrier. “We are finding molecules that bind to the protein passing into the brain and are less toxic, in a single optimization, not in three different steps,” Keinan stated. “That’s how we make the timeline shorter.”


Quantum May Help Rare Diseases Get The Attention They Deserve

Rare diseases are often ignored by drug companies because they aren’t profitable to treat. It can cost over a billion dollars to bring a new drug to market, and pharmaceutical companies aren’t typically willing to invest that kind of money in a drug that will only be marketable to a small population. Therefore, many rare diseases still lack effective treatment.


With quantum technology, the hope is that drug-trial simulations can eliminate the high development costs, reducing the barriers that prevent pharmaceutical companies from investing in developing treatments for rare diseases.

2. Precision medicine: Keep people healthy based on personalized interventions/treatments

Precision medicine aims to tailor prevention and treatment approaches to the individual. Due to the complexity of human biology, individualized medicine requires taking into account aspects that go well beyond standard medical care. In fact, medical care only has a relative contribution of 10 to 20 percent to outcomes; health-related behaviors, socioeconomic factors, and environmental aspects account for the other 80 to 90 percent.  Computationally, the interdependencies and correlations among these diverse contributors create formidable challenges with regard to optimizing treatment effectiveness.


As a result, many existing therapies fail to achieve their intended effects due to individual variability. For example, only a third of patients respond to drug-based cancer therapies. In some cases, the consequences of drug therapies can be disastrous; in Europe alone, up to 200,000 people die each year due to adverse drug reactions.


A key aspect of tailoring medical approaches is proactivity. Early treatments and preventive interventions tend to drastically improve outcomes and optimize costs. Classical machine learning has already shown some promise in predicting the risk of future diseases for a range of patient groups based on EHRs. Nevertheless, challenges remain due to the characteristics of EHRs and other health-relevant data, including the level of noise, size of the relevant feature space, and complexity of interactions among the features. This suggests supervised and unsupervised quantum-enhanced machine learning techniques could allow earlier, more accurate, and more granular risk predictions.


Just as important is knowing how to effectively medically intervene for any given individual. One avenue in this endeavor is the study of drug sensitivity at the cellular level. For example, by taking into account the genomic features of cancer cells and the chemical properties of
drugs, models that can predict the effectiveness of cancer drugs at a granular level are already being investigated. Quantum-enhanced machine learning could support further breakthroughs in this area and ultimately enable causal inference models for drugs


Quantum tech could allow scientists to understand the minute workings of the human body all the way down to the subatomic level, leading to a more personalized approach to medicine. One day, we may be able to input a patient’s medical and anatomical data into a quantum computer to create a “digital twin,” a virtual replica of the person. Using this digital twin as a test subject, doctors could run simulations with different drugs to see which one works best for that individual patient.


If you’ve ever had to go through rounds of different medications to find the one that works best for you, you know how frustrating that process can be. With quantum technology, your digital twin could do all the experimenting for you, so you can find the right drug without frustration.


3. Patient Care in Healthcare Facilities

With viruses like COVID-19 overwhelming hospital systems, many experts are looking to quantum computing to deal with the bottlenecks of more patients than doctors or hospital beds. The time to fix these bottlenecks is more apparent than ever, as the World Health Organization (WHO) predicts that by 2030, there will be a deficit of almost 10 million health providers, including doctors and nurses. This bottleneck may only get worse if another global pandemic hits, creating a pressing need to use quantum technology to help solve those issues. Because many of these bottlenecks can be solved using optimization algorithms, quantum computing can help to create more efficient care processes, as well as better allocation of resources like medicine or hospital beds.


“Quantum can help this whole domain,” Naveh added. Amir Naveh, Co-Founder and Chief Product Officer (CPO) of Classiq Technologies, a market-leading quantum computing company. “The actual care for patients, which is everything from being able to give optimal support for patients to be able to make sure there is fraud detection in these facilities.” With medical records containing sensitive patient information, quantum computing can provide an extra level of security via encryption processes. As quantum computing is being hailed as a threat to current levels of cybersecurity, it can help pave the way for newer levels of digital safety, especially for patient records and other types of sensitive information.


4. Pricing: Optimize insurance premiums and pricing.

One key area in which quantum computing may help optimize pricing is risk analysis. Quantum computing could help better assess the risk a
given patient has for a given medical condition. Leveraging these insights about disease risk at the population level, and combining them with quantum risk models that can compute financial risk more efficiently, could allow health plans to achieve improved risk and pricing models.


Classical data mining techniques already help with detecting and reducing healthcare fraud; nevertheless, more computationally efficient methods are needed.  Quantum algorithms could enable superior classification and pattern detection and thus help uncover anomalous behavior and eliminate fraudulent medical claims. This is expected to allow health plans to further optimize pricing strategies and offer reduced premiums as a result of having lower costs associated with fraud loss and prevention schemes


Partnerships between healthcare facilities and quantum technology companies have begun to emerge, showing healthy growth for this subsector of the quantum industry. One of these partnerships is between the Cleveland Clinic and IBM. IBM has worked for a long time with the Cleveland Clinic, but only recently did they partner to develop a center called the Discovery Accelerator, which will use quantum technology, such as quantum computing, to advance discoveries in medicine and the life sciences. This partnership will allow for faster and more efficient drug trials, as well as detection for diseases.


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