Quantum computing uses quantum mechanics principles to solve certain complex mathematical problems faster than classical computers. Whilst classical computers use binary “bits” to perform calculations, quantum computers use quantum bits (“qubits”). The value of a bit can only be zero or one, whereas a qubit can exist as zero, one, or a combination of both states (a phenomenon known as superposition) allowing quantum computers to solve certain problems exponentially faster than classical computers.
The potential applications of quantum computing are wide-ranging and industry-agnostic. For instance, they could be used to enhance the analysis of large, complex data sets, optimize supply-chain processes, and enhance artificial intelligence (“AI”) technologies and improve machine learning algorithms.
Given the potential applications, quantum computing could have a significant impact on companies in the life sciences sector, and more specifically could be used to improve:
1. Drug discovery
Classical computational methods play a crucial role in the drug discovery and design process by providing tools and techniques to model, predict and analyze the behavior of chemical systems. Quantum computing technologies have the potential to offer a more powerful, accurate and efficient alternative to classical computers, and can simulate more intricate chemical structures and interactions, leading to more optimized drug design.
Quantum computing can also be used as a complementary technology in conjunction with AI, which is already being used for drug discovery, thus exponentially increasing the accuracy and speed of the drug discovery process and potentially reducing the associated costs.
2. Clinical development
Quantum computers are far superior to classical computers when handling problems with multiple variables and complex datasets and it is exactly these capabilities that can be leveraged in clinical development phases. For example, quantum computing can be used to optimize the design of clinical trials; to enhance the data analysis and modelling process post-clinical trial by quickly detecting complex patterns and correlations; to monitor and adapt clinical trials in real-time, allowing for dynamic adjustments to protocols to maximize individual patient outcomes and address emerging safety concerns.
Quantum computers can be used in conjunction with AI, specifically machine learning, to analyze genetic and biomolecular data in order to better predict individual responses to specific treatments and create optimal treatment plans tailored to the genetic makeup and health condition a specific patient.
3. Diagnostics
Quantum computing technologies have the potential to enhance patient diagnosis and personalized care. Not only can quantum computing improve image reconstruction and quality, but it also has the potential to aid faster, more accurate and more precise image analysis and interpretation by considering multiple data points at the same time, when compared to classical computing methods.
In additions, quantum sensing is an emerging technology that uses quantum technology principles to detect or ‘sense’ changes in physical qualities such as temperature, motion, light and chemical composition. The high sensitivity and resolution of this technology offers the possibility of more efficient and accurate medical diagnosis, and it is likely we will see more of quantum sensing not just in classical medical diagnostic imaging settings, but also in wearables and health monitors.
For life sciences companies operating in the medical imaging field, quantum computing offers a real and unprecedented opportunity to tackle complex diagnostic challenges. For patients, the exploitation of quantum technologies could dramatically improve disease diagnosis and prognosis.
4. Therapy
Quantum computers have the potential to vastly advance the field of personalized medicine. The exponential power of a quantum computer, compared to a classical computer, can be utilized to more efficiently and accurately calculate dosing regimens, improve treatment plans, and predict and improve individual patient outcomes.
5. Manufacturing and supply chain processes
Quantum computing technologies have the potential to optimize both the manufacturing and distribution of products. Quantum computing’s near real-time data processing capabilities can be used for more effective inventory management, demand forecasting and route planning, leading to a more robust supply chain particularly in the context of the pharmaceutical cold chain.
6. Generation of Synthetic Data
As the world becomes increasingly driven by data, the most valuable resource of the future will likely be data itself. One potential issue is that there will not be enough credible, high-quality data to meet demand. Quantum computing has the power to generate higher-quality, more accurate synthetic data that simulates real-world data, thereby working towards alleviating the data scarcity issue.
Quantum computing and emerging quantum technologies have the potential to reduce the costs associated with research and development, and thus has the potential to open up research in rare, complex, and underfunded disease areas. However, the benefits of quantum computing are not without risk. Most significantly for the life sciences sector, there is a concern that in the future, quantum technologies may have the ability to solve the complex mathematical problems that underpin currently used cryptography methods, posing a threat to personal and sensitive patient data.
It remains to be seen how and when the field of quantum computing will develop, and how its potential impacts will be seen and felt. Quantum computing is still in the early stages of development and government policy, investment and regulation will likely play a crucial role in the growth of this technology in the EU, UK, U.S. and beyond.
Covington is monitoring developments globally in this fast-growing area.
Visit Covington’s Quantum Computing web page for additional updates. Please reach out to a member of the team with any inquiries.