Quantum computing in life sciences and health care

3 sector challenges quantum computing can help overcome

Quantum computing uses the laws of physics for calculations—opening up a new paradigm of possibility. This offers advantages over classical computers for certain types of business challenges. This emerging technology has the potential to revolutionize the way life sciences and health care companies operate. From modeling at the subatomic level to analytical machine learning capabilities—quantum computing can help address a range of common sector challenges.

Three of the largest areas of opportunity we see for quantum computing in life sciences and health care include:

1. Complex molecular interactions: Accurate Molecular simulations require highly complex computations that are sometimes inefficient or impossible to run on classical hardware.
2. High-dimensional medical datasets: As medicine has become more data-driven, datasets have become increasingly complex and multi-model. Training learning models and sorting this data proves challenging for classical computing approaches.
3. Synthetic data generation: Testing a new drug through clinical trials is an extremely time-consuming, and billions of dollars of investment. However, many clinical trials fail due to the missing data collected from patients. Synthetic data can be used to impute those missing records, but generating synthetic data by classical models is a challenging task because it requires a large volume of training data which typically don’t exist.

Life sciences use cases

Quantum computing could significantly impact how life science and pharmaceutical companies discover, test and manufacture new drugs —by improving their ability to simulate and model drugs properties and efficacy on a subatomic level, enhancing and speeding up the clinical processes, and optimizing the manufacturing and commercial processes to take a drug to market. Use cases for quantum computing in life sciences could include:

• Molecular discovery and simulation
• Protein folding
• Clinical trial optimization
• Multi-omics analysis
• Drug manufacturing optimization

Health care uses cases

Quantum computing’s machine learning and optimization capabilities can offer significant benefits for health care organizations. Quantum optimizations can accelerate various tasks within the organizations to improve operation and resource management. This can potentially accelerate tasks like patients’ and providers’ scheduling, which can help to improve patients’ satisfaction and treatment outcome. Quantum machine learning may require less training data than classical computing machine learning. It also has the potential to improve accuracy relative to classical techniques and enable high dimensional coding of data features. This could ultimately enable generation of better synthetic data when source data is sparse, which is important to develop new methods to treat rare diseases—and potentially predict the appropriate treatment based on patients’ complex historical medical record. Use cases in health care could include:

• Health care operations and resource allocation
• Medical imaging and diagnostics

Identify your use cases

Quantum computing continues to advance, and while its broad use is years out—there are steps life sciences and health care leaders can take today to define where business value might exist with the technology. Working with a provider who understands where quantum computing is heading can help you define your use case and formulate proof of concept.