13 research outputs found
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
The state of quantum computing applications in health and medicine
Quantum computing hardware and software have made enormous strides over the
last years. Questions around quantum computing's impact on research and society
have changed from "if" to "when/how". The 2020s have been described as the
"quantum decade", and the first production solutions that drive scientific and
business value are expected to become available over the next years. Medicine,
including fields in healthcare and life sciences, has seen a flurry of
quantum-related activities and experiments in the last few years (although
medicine and quantum theory have arguably been entangled ever since
Schr\"odinger's cat). The initial focus was on biochemical and computational
biology problems; recently, however, clinical and medical quantum solutions
have drawn increasing interest. The rapid emergence of quantum computing in
health and medicine necessitates a mapping of the landscape. In this review,
clinical and medical proof-of-concept quantum computing applications are
outlined and put into perspective. These consist of over 40 experimental and
theoretical studies from the last few years. The use case areas span genomics,
clinical research and discovery, diagnostics, and treatments and interventions.
Quantum machine learning (QML) in particular has rapidly evolved and shown to
be competitive with classical benchmarks in recent medical research. Near-term
QML algorithms, for instance, quantum support vector classifiers and quantum
neural networks, have been trained with diverse clinical and real-world data
sets. This includes studies in generating new molecular entities as drug
candidates, diagnosing based on medical image classification, predicting
patient persistence, forecasting treatment effectiveness, and tailoring
radiotherapy. The use cases and algorithms are summarized and an outlook on
medicine in the quantum era, including technical and ethical challenges, is
provided
Genome assembly using quantum and quantum-inspired annealing
Recent advances in DNA sequencing open prospects to make whole-genome
analysis rapid and reliable, which is promising for various applications
including personalized medicine. However, existing techniques for {\it de novo}
genome assembly, which is used for the analysis of genomic rearrangements,
chromosome phasing, and reconstructing genomes without a reference, require
solving tasks of high computational complexity. Here we demonstrate a method
for solving genome assembly tasks with the use of quantum and quantum-inspired
optimization techniques. Within this method, we present experimental results on
genome assembly using quantum annealers both for simulated data and the X
174 bacteriophage. Our results pave a way for an increase in the efficiency of
solving bioinformatics problems with the use of quantum computing and, in
particular, quantum annealing. We expect that the new generation of quantum
annealing devices would outperform existing techniques for {\it de novo} genome
assembly. To the best of our knowledge, this is the first experimental study of
de novo genome assembly problems both for real and synthetic data on quantum
annealing devices and quantum-inspired techniques.Comment: 9 pages, 4 figure
Classical-to-Quantum Sequence Encoding in Genomics
DNA sequencing allows for the determination of the genetic code of an
organism, and therefore is an indispensable tool that has applications in
Medicine, Life Sciences, Evolutionary Biology, Food Sciences and Technology,
and Agriculture. In this paper, we present several novel methods of performing
classical-to-quantum data encoding inspired by various mathematical fields, and
we demonstrate these ideas within Bioinformatics. In particular, we introduce
algorithms that draw inspiration from diverse fields such as Electrical and
Electronic Engineering, Information Theory, Differential Geometry, and Neural
Network architectures. We provide a complete overview of the existing data
encoding schemes and show how to use them in Genomics. The algorithms provided
utilise lossless compression, wavelet-based encoding, and information entropy.
Moreover, we propose a contemporary method for testing encoded DNA sequences
using Quantum Boltzmann Machines. To evaluate the effectiveness of our
algorithms, we discuss a potential dataset that serves as a sandbox environment
for testing against real-world scenarios. Our research contributes to
developing classical-to-quantum data encoding methods in the science of
Bioinformatics by introducing innovative algorithms that utilise diverse fields
and advanced techniques. Our findings offer insights into the potential of
Quantum Computing in Bioinformatics and have implications for future research
in this area.Comment: 58 pages, 14 figure
ChemiQ: A Chemistry Simulator for Quantum Computer
Quantum computing, an innovative computing system carrying prominent
processing rate, is meant to be the solutions to problems in many fields. Among
these realms, the most intuitive application is to help chemical researchers
correctly de-scribe strong correlation and complex systems, which are the great
challenge in current chemistry simulation. In this paper, we will present a
standalone quantum simulation tool for chemistry, ChemiQ, which is designed to
assist people carry out chemical research or molecular calculation on real or
virtual quantum computers. Under the idea of modular programming in C++
language, the software is designed as a full-stack tool without third-party
physics or chemistry application packages. It provides services as follow:
visually construct molecular structure, quickly simulate ground-state energy,
scan molecular potential energy curve by distance or angle, study chemical
reaction, and return calculation results graphically after analysis.Comment: software,7 pages, 5 figure
Quantum Computing for Molecular Biology
Molecular biology and biochemistry interpret microscopic processes in the
living world in terms of molecular structures and their interactions, which are
quantum mechanical by their very nature. Whereas the theoretical foundations of
these interactions are very well established, the computational solution of the
relevant quantum mechanical equations is very hard. However, much of molecular
function in biology can be understood in terms of classical mechanics, where
the interactions of electrons and nuclei have been mapped onto effective
classical surrogate potentials that model the interaction of atoms or even
larger entities. The simple mathematical structure of these potentials offers
huge computational advantages; however, this comes at the cost that all quantum
correlations and the rigorous many-particle nature of the interactions are
omitted. In this work, we discuss how quantum computation may advance the
practical usefulness of the quantum foundations of molecular biology by
offering computational advantages for simulations of biomolecules. We not only
discuss typical quantum mechanical problems of the electronic structure of
biomolecules in this context, but also consider the dominating classical
problems (such as protein folding and drug design) as well as data-driven
approaches of bioinformatics and the degree to which they might become amenable
to quantum simulation and quantum computation.Comment: 76 pages, 7 figure