Quantum technology field comprises four domains: Quantum Communication, where an individual or entangled photons are used to transmit data in a provably secure way; Quantum Simulation, where well-controlled quantum systems are used to reproduce the behavior of other, less accessible quantum systems; Quantum Computation, which employs quantum effects to dramatically speed up certain calculations, such as number factoring; and Quantum Sensing & Metrology, where the high sensitivity of coherent quantum systems to external perturbations is exploited to enhance the performance of measurements of physical quantities.
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. As a result, there is now a race toward quantum applications. 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.
Quantum computers have already begun permeating our healthcare systems. 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.
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 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
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
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.
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
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
3. 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
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