Intel report: Quantum advantage will greatly promote medical and biological research
A report published by Oregon Health and Science University and Intel Corporation on the ArXiv website, "Prospects for Quantum Advantage in Biology and Medicine" [1] shows that if a wide range of quantum advantages are achieved, it will greatly benefit certain medical and biological research. The report pointed out that one of the advantages of pursuing excellence in quantum algorithms is the development of better classical algorithms and hybrid quantum-classical algorithms.
The report pointed out that as the first generation of NISQ (noisy medium-scale quantum) equipment moved from the laboratory to the cloud, it is now computational scientists in the fields of biology and medicine that are beginning to explore the value that quantum methods may bring to their research toolboxes. Great time.
In the past three decades, biology and medicine have developed into highly quantitative fields. However, although the expansion of computing methods and high-performance computing (HPC) environments has facilitated substantial progress, basic limitations on the ability to understand biological and clinical systems still exist.
System complexity is an example. Practical algorithms usually manage system complexity by simplifying the framework, resulting in the existing computational models often failing to capture and coordinate important system dynamics. If a sufficiently powerful quantum computer can be made, it is expected to radically reduce the complexity of the algorithm. In turn, many difficult problems will be simulated with higher efficiency, which may reduce the calculation time and increase the fidelity of the actual model.
The second limitation is scale. In terms of health care alone, as many as 153 EB of data were generated in 2013, and the compound annual growth rate is expected to be 48%. Based on this growth rate, the data generated in 2020 may exceed 2300 EB. The high-throughput sequencing revolution in biology has produced a large number of highly complex genome, epigenome, transcriptome, proteome, and metabolome data types (and other data types). These massive data resources are essential for reusing high-value data in secondary analysis and reproducibility research. However, even if HPC infrastructure is widely used, large-scale bioinformatics and computational biology workflows usually last for days, weeks, or longer.
Although quantum computing technology is not expected to solve scalability limitations in the short term, in the long run, FTQC (Fault Tolerant Quantum Computing) devices may provide partial solutions to some of these challenges.
How do we define quantum advantage from a theoretical perspective? The report pointed out that theoretical quantum advantage is defined by four key attributes:
1. Problem: A formal calculation problem.
2. Algorithm: a classical algorithm and a quantum algorithm, each algorithm solves computational problems.
3. Resources: One or more resources consumed by classical algorithms and quantum algorithms, such as time, space or data.
4. Boundary: the analysis boundary of the resource consumption of classical algorithms and quantum algorithms (for example, the time complexity boundary in the worst case).
Prospects for Quantum Advantage in Biology and Medicine
1. Simulate quantum physics
Simulating microscopic properties and processes at the atomic level is a key area of computational biology research. These tasks usually require quantum mechanical simulation, which is a classic problem for all quantum systems except the smallest quantum system. These inherent limitations mean that most classical methods are approximate and usually provide a qualitative understanding. In contrast, quantum mechanical simulation of many of the same problems is a natural task of quantum computers. After the NISQ era, simulations of large quantum systems are expected to be used to predict biochemical properties and behaviors that cannot be effectively calculated by classical devices.
2. Simulate classical physics
Classical simulation of quantum algorithms. Many targeted quantum algorithms have been proposed for finding low-energy conformations and searching for candidate molecules, many of which are specifically developed for protein folding. More generally, amplitude amplification can be used to explore the conformational space of classical variables with a square advantage. In addition, other optimization-related subroutines also show theoretical quantum advantages, such as the escape saddle point in the optimization scenario.
3. Bioinformatics
For the problems in bioinformatics, a small number of quantum algorithms have been proposed. These include theoretical algorithms developed by FTQC equipment for NP-hard problems, such as sequence alignment, evolutionary tree inference using amplitude amplification and quantum walking. In order to make these theoretical quantum algorithms practical, it is expected that these theoretical quantum algorithms need a lot of improvement and effort.
4. Quantum machine learning
Variational quantum machine learning (QML) is expected to provide a methodological toolbox for a wide range of biological research and clinical applications.
5. Quantum data structure
In bioinformatics and computational biology, non-traditional data structures have long been used by classical algorithms. For example, many of the most advanced algorithms for error correction sequence data use Bloom Filters, whose core advantage is that they can exchange significant memory savings with a lower probability of false positive search-this is a large-scale bioinformatics pipeline Common restrictions in.
link：https://arxiv.org/pdf/2112.00760.pdf
