Quantum gate algorithm for reference-guided DNA sequence alignment
- URL: http://arxiv.org/abs/2308.04525v1
- Date: Tue, 8 Aug 2023 18:41:24 GMT
- Title: Quantum gate algorithm for reference-guided DNA sequence alignment
- Authors: G. D. Varsamis, I. G. Karafyllidis, K. M. Gilkes, U. Arranz, R.
Martin-Cuevas, G. Calleja, P. Dimitrakis, P. Kolovos, R. Sandaltzopoulos, H.
C. Jessen, J. Wong
- Abstract summary: We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing.
The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Reference-guided DNA sequencing and alignment is an important process in
computational molecular biology. The amount of DNA data grows very fast, and
many new genomes are waiting to be sequenced while millions of private genomes
need to be re-sequenced. Each human genome has 3.2 B base pairs, and each one
could be stored with 2 bits of information, so one human genome would take 6.4
B bits or about 760 MB of storage (National Institute of General Medical
Sciences). Today most powerful tensor processing units cannot handle the volume
of DNA data necessitating a major leap in computing power. It is, therefore,
important to investigate the usefulness of quantum computers in genomic data
analysis, especially in DNA sequence alignment. Quantum computers are expected
to be involved in DNA sequencing, initially as parts of classical systems,
acting as quantum accelerators. The number of available qubits is increasing
annually, and future quantum computers could conduct DNA sequencing, taking the
place of classical computing systems. We present a novel quantum algorithm for
reference-guided DNA sequence alignment modeled with gate-based quantum
computing. The algorithm is scalable, can be integrated into existing classical
DNA sequencing systems and is intentionally structured to limit computational
errors. The quantum algorithm has been tested using the quantum processing
units and simulators provided by IBM Quantum, and its correctness has been
confirmed.
Related papers
- Compare Similarities Between DNA Sequences Using Permutation-Invariant Quantum Kernel [3.8926796690238694]
We propose a permutation-invariant variational quantum kernel method specifically designed for DNA comparison.
We show that our novel encoding method and parameterized layers used in the quantum kernel model can effectively capture the symmetric characteristics of the pairwise DNA sequence comparison task.
arXiv Detail & Related papers (2025-03-07T14:35:38Z) - QuantumDNA: A Python Package for Analyzing Quantum Charge Dynamics in DNA and Exploring Its Biological Relevance [0.0]
The study of DNA charge dynamics is a highly interdisciplinary field that bridges physics, chemistry, biology, and medicine.
We present QuantumDNA, an open-source Python package for simulating DNA charge transfer (CT) and excited states using quantum-physical methods.
arXiv Detail & Related papers (2025-02-08T16:48:16Z) - Deterministic Storage of Quantum Information in the Genetic Code [0.0]
DNA has been proposed as a chemical platform for computing and data storage.
This paper explores DNA base pairs as elementary units for a scalable nuclear magnetic resonance quantum computer.
arXiv Detail & Related papers (2024-12-10T13:36:31Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
We show that a quantum processor can correctly solve the basic classification task considered.
With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
arXiv Detail & Related papers (2024-06-17T18:20:51Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Variational Quantum Algorithms for Chemical Simulation and Drug
Discovery [9.862947257151113]
We use quantum computing to solve the problem of protein folding.
A moderate protein has about 100 amino acids, and the number of combinations one needs to verify to find the stable structure is enormous.
We compare the results of different quantum hardware and simulators and check how error mitigation affects the performance.
arXiv Detail & Related papers (2022-11-15T02:34:36Z) - Quantum neural networks [0.0]
This thesis combines two of the most exciting research areas of the last decades: quantum computing and machine learning.
We introduce dissipative quantum neural networks (DQNNs), which are capable of universal quantum computation and have low memory requirements while training.
arXiv Detail & Related papers (2022-05-17T07:47:00Z) - Quantum Algorithms for Unsupervised Machine Learning and Neural Networks [2.28438857884398]
We introduce quantum algorithms to solve tasks such as matrix product or distance estimation.
These results are then used to develop new quantum algorithms for unsupervised machine learning.
We will also present new quantum algorithms for neural networks, or deep learning.
arXiv Detail & Related papers (2021-11-05T16:36:09Z) - Efficient approximation of DNA hybridisation using deep learning [0.0]
We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation.
We introduce a synthetic hybridisation dataset of over 2.5 million data points, enabling the use of a wide range of machine learning algorithms.
arXiv Detail & Related papers (2021-02-19T19:23:49Z) - Quantum Computing without Quantum Computers: Database Search and Data
Processing Using Classical Wave Superposition [101.18253437732933]
We present experimental data on magnetic database search using spin wave superposition.
We argue that in some cases the classical wave-based approach may provide the same speedup in database search as quantum computers.
arXiv Detail & Related papers (2020-12-15T16:21:53Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - Quantum Discriminator for Binary Classification [0.0]
We propose a novel quantum machine learning model called the Quantum Discriminator.
We show that the quantum discriminator can attain 99% accuracy in simulation.
arXiv Detail & Related papers (2020-09-02T19:00:23Z) - Quantum Gram-Schmidt Processes and Their Application to Efficient State
Read-out for Quantum Algorithms [87.04438831673063]
We present an efficient read-out protocol that yields the classical vector form of the generated state.
Our protocol suits the case that the output state lies in the row space of the input matrix.
One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure.
arXiv Detail & Related papers (2020-04-14T11:05:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.