Biology and medicine in the landscape of quantum advantages

Abstract

Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is typically contingent on a reduction in the consumption of a computational resource, such as time, space, or data. Here, we distill the concept of a quantum advantage into a simple framework that we hope will aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to i) assess the potential of quantum advantages in specific application areas and ii) identify gaps that may be addressed with novel quantum approaches. Bearing in mind the rapid pace of change in the fields of quantum computing and classical algorithms, we aim to provide an extensive survey of applications in biology and medicine that may lead to practical quantum advantages.

Quantum Kernels for Real-World Predictions Based on Electronic Health Records

“…Further work is needed to explore the applicability of different performance metrices for various domains. Given that there is a need to build robust machine learning models in medical settings where additional samples are costly or impossible to acquire, even a modest reduction in the number of samples required for training based on certain data distributions can yield considerable benefits for many prediction and inference problems in biology [28].…”

“…However, as in the field of practical classical algorithms [27], practitioners may use EQA to observe trends in empirical data. This is key in biology and medicine where both theoretical and operational factors must be considered, in general, when exploring the benefits of quantum algorithms for a given application [28].…”

A Scalable Approach to Quantum Simulation via Projection-based Embedding

“…Quantum computing promises to enable accurate simulation of chemical systems beyond the capabilities of classical methods. Whether this aim will be achieved with so-called Noisy Intermediate-scale Quantum (NISQ) processors, is still to be seen [1][2][3]. While devices are improving rapidly, NISQ applications also require algorithmic tools to mitigate noise and reduce required qubit counts.…”

Equivalence Checking of Quantum Circuits With the ZX-Calculus

“…Therefore, in order to execute a given circuit on quantum hardware, it needs to be compiled to a representation that adheres to all constraints imposed by the targeted device [8]- [11]. Since quantum computers are heavily affected by noise and decoherence, it is paramount to optimize circuits as much as possible in order to maximize the expected fidelity when running the circuit [12]- [16].…”