Quantum gate algorithm for reference-guided DNA sequence alignment.

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 3.2B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4B bits or ∼760MB of storage (National Institute of General Medical Sciences, n.d.). Today's 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.

Quantum algorithm for de novo DNA sequence assembly based on quantum walks on graphs.

De novo DNA sequence assembly is based on finding paths in overlap graphs, which is a NP-hard problem. We developed a quantum algorithm for de novo assembly based on quantum walks in graphs. The overlap graph is partitioned repeatedly to smaller graphs that form a hierarchical structure. We use quantum walks to find paths in low rank graphs and a quantum algorithm that finds Hamiltonian paths in high hierarchical rank. We tested the partitioning quantum algorithm, as well as the quantum algorithm that finds Hamiltonian paths in high hierarchical rank and confirmed its correct operation using Qiskit. We developed a custom simulation for quantum walks to search for paths in low rank graphs. The approach described in this paper may serve as a basis for the development of efficient quantum algorithms that solve the de novo DNA assembly problem.

Extraction of the characteristics of Si nanocrystals by the charge pumping technique.

In this paper, the characteristics of silicon nanocrystals used as charge trapping centers in memory devices are examined using the two-level charge pumping (CP) technique performed as a function of frequency and energy filtered transmission electron microscopy (EFTEM). The parameters extracted from the two methods such as the depth location, density and effective diameter of the nanocrystals are in good quantitative agreement. These results validate the charge pumping approach as a non-destructive powerful technique to access most of the properties of nanocrystals embedded in dielectrics and located at injection distances from the substrate surface not limited to the direct tunneling regime.

Temperature-dependent low electric field charging of Si nanocrystals embedded within oxide-nitride-oxide dielectric stacks.

In this work we examine the current peaks and the negative differential resistance that appear in the low electric field regime of oxide-nitride-oxide structures with a two-dimensional band of silicon nanocrystals embedded in a nitride layer. The silicon nanocrystals were synthesized by low energy ion implantation (1 keV, 1.5 x 10(16) Si(+) cm(-2)) and subsequent thermal annealing (950 degrees C, 30 min). Electrical examination was performed at temperatures from 20 to 100 degrees C using constant voltage ramp-rate current measurements. This approach enables us to determine the origin of the observed current peaks as well as to extract the trapping location of the injected carriers within the dielectric stack. The results confirm that the carriers are trapped within the Si nanocrystal band, verifying that this region corresponds to energy minima of the dielectric stack.