Decades after researches first sequenced the human genome , scientists throughout the world are still working to understand it. Despite diligent global efforts to link uncommon variations in DNA sequences with human disease, progress has been slow, in large part due to limitations in scientific understanding and in part due to limitations in computational technologies.
Artificial intelligence has the potential to help scientists decipher the millions of genetic variations present in the genomes of different people in order to identify which ones lead to disease and which ones do not. In order to fully exploit the power of AI, however, scientists need to compare the genomes of thousands or tens of thousands of people. This task not only requires intense computational effort, it is also prone to error and will take years to complete .
Quantum computing has the potential to facilitate that process. We are researchers with a long-standing interest in finding ways to use genetics in the clinic and developing new technologies to study the human genome. Combining quantum computing with AI has the potential to accelerate genomic analysis far beyond traditional methods. For time-sensitive medical conditions, faster decoding of genetic information can directly inform urgent treatment decisions and, in some cases, be lifesaving.
Conventional vs. quantum computing
In conventional computing , individual bits of information - binary digits, also called bits - can represent only two states: namely, 0 and 1.
However, the qubits used in quantum computing can have more than two distinct states. Adding qubits together increases the number of states exponentially. The power of quantum computers lies in being able to check all the possibilities at once for problems with large numbers of variables, rather than one at a time like even the fastest possible classical computer must do. This allows quantum computers to solve certain types of problems, such as factoring large numbers for today's encryption schemes and performing combinatorial optimization to find the best route through a large number of points.
Still, quantum computing is currently in its infancy. Despite the enormous potential of this technology, computer scientists are dealing with challenges related to its scalability, error correction, hardware development and the setting of standards.
There are also significant time and cost constraints associated with ameliorating these challenges. Experts in the field estimate that it may be at least a decade before quantum computing will be truly useful outside of the laboratory.
Bigger and better data analysis
If researchers are able to overcome these challenges, combining AI and quantum computing may not only enable scientists and clinicians to better understand the human genome but also to leverage that understanding to improve patient care.
Currently, researchers are able to use AI to analyze genomic data in combination with limited amounts of other biological information, such as gene activity , epigenomics, RNA signatures and protein function . Quantum computing could allow AI to process increasingly more massive and highly detailed datasets.
This might look like integrating large-scale genetic, protein and spatial datasets with clinical, demographic and real-time physiological data. This systems-level approach enables a more comprehensive and accurate understanding of complex biological systems beyond DNA sequence alone that could be used to improve public health.
In other words, quantum computing could make it possible to sequence a patient's genome and combine that information with other information about how their body works at the molecular level to improve the accuracy of diagnoses and determine the best course of treatment in hours instead of months.
Challenges in access and privacy
Like many burgeoning technologies, combining AI with quantum computing has inherent and inescapable challenges. In particular, there are several ethical issues related to healthcare access.
One will be the cost. New technologies are typically expensive and that will likely widen the gap between those who can afford the best healthcare and those who cannot. Anticipating these costs and finding preemptive creative solutions is necessary to allow everyone to benefit equally .
While there are likely many approaches to reducing out-of-pocket expenses for healthcare, federal legislation could mandate affordable or free genetic information-based care to those in greatest financial need. Similar to the 2008 Genetic Information Nondiscrimination Act , which prohibits discrimination based on genetics, a new law could prohibit healthcare providers from withholding genetic information-based care from those who cannot afford it.
Another challenge will be availability. These technologies will likely first be available at only the top medical centers in the country, which traditionally have the research funding and the cadre of skilled scientists and clinicians needed to develop new diagnostic methods and treatments. Consequently, the latest advances in health technology will be unavailable to people who physically or financially cannot travel to receive the best medical care.
A combination of telemedicine, centralized laboratories and shared data could potentially help make new technologies more accessible .
There are also privacy concerns intrinsic to sharing personal health data . Truly anonymizing personal information remains a challenge, and privacy concerns are likely to prevent some people from taking advantage of potentially lifesaving technologies.
One approach that may quell these fears is a model called federated blockchain governance . This approach involves sharing control of a blockchain, which is a digital ledger used to track transactions, among a small group of institutions rather than a single entity or the general public. Limiting the number of trusted curators of genetic data reduces the risk of privacy violations or security breaches and subsequently increases the chance that patient data will remain private.
Improving public health
Despite these challenges, combining advances in quantum computing and AI has the potential to significantly drive innovation and improve public health.
When scientists and clinicians are able to accurately identify the genetic basis of disease and potential risk factors , they will not only be able to develop better treatments but also help patients and healthcare providers know what symptoms to look for among those predisposed to certain conditions.
Taken together, this knowledge can improve public health, reduce the cost of healthcare and improve quality of life.
The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
