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As communications and monitoring technology expand into more diverse sectors and use cases, complex systems are becoming a part of everyday life. With an unprecedented amount of data at our fingertips, the impetus to make sense of these complex patterns by predicting and optimizing the information within them is becoming increasingly pronounced. These efforts can range from predicting traffic flow to analyzing disease patterns in healthcare or even forecasting logistics operations in industrial sectors.
Predicting the behavior of complex systems with a high degree of accuracy is difficult, which can add challenges to decision-making that is based on outcomes deduced from an abundance of data. Two key technologies have made great strides in deducing patterns and predicting behaviors: artificial intelligence (AI) and quantum computing.
First, let’s examine why AI and quantum computing are well suited to gain insights, predict behavior, optimize outcomes, and develop solutions for some of the most complicated issues. AI, with an array of algorithms, machine learning (ML) methods, and predictive modeling, has become a critical technology for identifying patterns and predicting outcomes. However, AI is energy intensive, so quantum computing emerges as a less energy intensive option that requires less memory. While this emergence is not yet here, with quantum computing only being theoretical presently, quantum does hold promise for processing massive amounts of data with a high degree of accuracy by moving beyond classical bits and leveraging complicated factors such as superposition, entanglement, and interference.
The ability to simulate complex quantum systems will enable developers to gain insights into phenomenon that are not seen in classical computing systems. This development will be essential to developing new materials and technologies that rely on quantum effects. In many cases, it might be beneficial to partner AI and machine learning with quantum computing to optimize the information gained from behavior analysis and develop models to solve complex problems in today’s world. This is where quantum machine learning comes in. Here, we look at some of the notable applications where quantum computing holds great potential to predict complex behaviors and outcomes in society today.
Because quantum computers are expected to handle much larger and complex datasets than classical computers—especially at higher qubit levels—there are many areas of the healthcare industry that stand to benefit. While quantum computing could be used on the analysis side in medical imaging, potential exists to use quantum computing to predict the threat of many diseases.
The COVID-19 pandemic revealed the importance of predicting the spread of diseases, for both saving lives and protecting healthcare systems. However, it’s not just disease spread that can be modeled by quantum computing. Early experimentation shows quantum computing’s ability to dig into various datasets containing specific biomarkers, spotting trends and patterns that might show if a person is susceptible to a disease. Such predictive capabilities could be vital for providing preemptive treatments rather than reactive ones that make discoveries too late oftentimes.
For example, quantum computing could possibly simulate molecular interactions, protein folding, and genetic variations at large-scale to predict behaviors within the biological environment of a patient, as well as sift through immense datasets to spot anomalies and genetic biomarkers associated with a disease or ailment. At an individual patient level, quantum computing—either by itself or in conjunction with quantum machine learning algorithms—may even assist with the following healthcare needs:
While many predictions can be made at the individual patient level, quantum computing will likely also be used on a wider and global scale, tracking and predicting disease patterns and outbreaks. With these insights, quantum could potentially suggest the best medical approaches for reducing the spread of disease. The faster speed expected of quantum computers could help improve the efficiency and accuracy of these analyses, leading to swift interventions. For large-scale healthcare challenges, quantum computing could also predict and model complex interactions within societal populations by using public health data to predict and manage various medical crises beyond disease outbreaks.
Even though technical challenges will inevitably exist for integrating quantum computing into existing healthcare computing systems, hybrid approaches could first be explored to allow for gradual integration and transition of quantum computing into healthcare analytics and existing infrastructure. These hybrid approaches may also help their adoption on a much larger scale in the future.
While quantum computing may eventually be used to predict disease outbreaks, it stands to make great gains in medical experts’ efforts to predict effective treatments for numerous cancers. Much like developing new materials and new drugs, synthetic approaches to cancer treatment require a lot of trial-and-error, which is where quantum computing could lend a tremendous helping hand. Currently, trialing treatment efforts has started to give way to computational modeling and AI that can predict the best approach to developing new medicines and therapies. Having the ability to trial more treatments and gain deeper insights from each one before synthetically making a few selective drugs saves time and money.
Quantum computing is predicted to be greatly beneficial in this predictive approach to analyzing all potential variables and chemical group effects that could help tackle cancer. From there, quantum could likely provide a good assessment of the best therapies, leading to more personalized medicine. This personalized care includes identifying the exact chemical structures to target a specific cancer site based on both biological and chemical data, e.g., predicting how a tumor will react to a specific therapy based on the genetic markers of a patient or predicting how a therapy will react with other drugs if a patient has a complex medical history.
Overall, like advanced computational modeling and AI, quantum computing could offer researchers the ability to reduce the trial-and-error approach that has been prevalent in pharmaceutical research and development. The advanced prediction capabilities that quantum computing promises in the future can improve survival rates, most importantly, but also reduce healthcare costs by minimizing ineffective treatments, helping make healthcare systems more efficient through quicker and more streamlined treatments.
While there’s a lot of potential in medical applications, quantum computing is anticipated to predict complex behaviors in a host of other areas, and they don’t get much more complex than the various weather patterns and weather systems around the world that dynamically change on rapid timescales.
Quantum computing will be used to perform numerical weather prediction (NWP) models. These models can have billions of variables that account for the temperature, humidity, wind speed, and other weather conditions across the surface of the Earth and into the stratosphere. Even modern day quantum computers that are still in relative infancy can run these models in a basic form, because only a 30-qubit quantum computer is required. When more advanced and higher qubit quantum computers become commonplace, they will provide quicker and more robust models that can account for more variables that outpace classical computers.
It will be some time before we see very large qubit quantum computers; likely not until they become much more efficient than today’s advanced classical computers. Instead, specific quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and the Quantum-enhanced Markov Chain Monte Carlo (QMCMC), will be used to perform specialized prediction tasks within NWPs and other climate models until a time comes when quantum computers are ready to take over advanced weather predictions.
When that day finally comes, quantum computers could be used in weather prediction models to provide faster and more accurate updates, especially in locations with limited computational resources. Key examples of such applications include space weather phenomena and predicting extreme weather events across the globe
Additionally, potential exists for quantum computing in the prediction, management, and optimization of different operations within logistics and supply chain industries. Quantum computers will be able to solve logistics and supply chain optimization problems faster than classical computers and will be particularly useful for optimizing supply chain routes and schedules for different vehicle fleets. Quantum computers could be used to find the most optimal transport route to reduce time, emissions, and fuel consumption.
Aside from optimizing supply chain logistics, quantum computing may also be used for inventory management within the supply chain. Warehouses and storage facilities sending inventory over long distances to a range of businesses and consumers use large amounts of data in managing these operations. Quantum computers will use this data to better manage inventory levels and reduce stockouts by identifying patterns and trends not always obvious to classical computers.
Furthermore, quantum computers could possibly analyze large datasets to spot complex patterns in consumer behavior, allowing logistics companies to perform better dynamic demand forecasting operations, anticipate changes, and adjust the supply chains much quicker. Finally, quantum computers will improve risk management operations by simulating complex supply chain scenarios and assessing any potential risks (including cybersecurity), so that more effective mitigation strategies can be developed. All these factors will lead to a much more robust, efficient, and effective supply chain.
Quantum computing is poised to revolutionize many industries, and its growth in recent years has already been astounding. While quantum computing is expected to speed up many general computing operations, its extra processing capabilities will enable a wide range of predictive tasks, including simulating complex behaviors that are either difficult to compute with classical systems or require a lot of computational power to do so. Predicting and managing diseases, developing cancer therapies, forecasting extreme weather conditions, and handling vast supply chain management operations are some of the areas set to benefit from quantum computing’s predictive capabilities, but if quantum’s theoretical promise holds true, there will be many more industries making significant gains in the coming years.
Liam Critchley is a writer, journalist and communicator who specializes in chemistry and nanotechnology and how fundamental principles at the molecular level can be applied to many different application areas. Liam is perhaps best known for his informative approach and explaining complex scientific topics to both scientists and non-scientists. Liam has over 350 articles published across various scientific areas and industries that crossover with both chemistry and nanotechnology.
Liam is Senior Science Communications Officer at the Nanotechnology Industries Association (NIA) in Europe and has spent the past few years writing for companies, associations and media websites around the globe. Before becoming a writer, Liam completed master’s degrees in chemistry with nanotechnology and chemical engineering.
Aside from writing, Liam is also an advisory board member for the National Graphene Association (NGA) in the U.S., the global organization Nanotechnology World Network (NWN), and a Board of Trustees member for GlamSci–A UK-based science Charity. Liam is also a member of the British Society for Nanomedicine (BSNM) and the International Association of Advanced Materials (IAAM), as well as a peer-reviewer for multiple academic journals.