Quantum and DNA computing are distributing and parallel types of computing. They are useful for solving problems which require high complexity computations and (or) massive data set computations such as searching, sorting, merging, pattern recognition, image processing, encryption, etc. Quantum and DNA algorithms cannot be efficiently simulated on classical computers because classical computers cannot efficiently deal with the parallelism. The quantum circuit model is adequate to describe quantum algorithms whereas DNA circuit model is adequate to describe DNA algorithms. This thesis establishes the relationship between a quantum qubit and a DNA string using the Bloch sphere. The Bloch sphere is a convenient graphical representation of a qubit in a 3-dimensional space. The model used in this thesis presents one-to-one mapping between qubits, DNA strings, and points on the Bloch sphere. The model is implemented at the real computer - von Neumann machine, therefore, there are restrictions related to the precision of qubits value, the length of DNA strings, and the precisions of coordinates values. The Bloch sphere and the relations between qubits, DNA strings and points are implemented as "Quantum and DNA Computation Simulation Programming System". Quantum and DNA Computation Simulation Programming System is implemented in the Java programming language. This system is a modified and upgraded interactive Quantum Computation applet developed at the Johns Hopkins Center for Educational Resources. The modifications include connecting (a) DNA strings and the Bloch sphere and (b) DNA strings and qubits. There are many researches in Quantum Computing and DNA Computing as independent study fields, but this is the first research which tries to connect Quantum and DNA Computing together using the Bloch sphere.
Quantum and DNA computing are distributing and parallel types of computing. They are useful for solving problems which require high complexity computations and (or) massive data set computations such as searching, sorting, merging, pattern recognition, image processing, encryption, etc. Quantum and DNA algorithms cannot be efficiently simulated on classical computers because classical computers cannot efficiently deal with the parallelism. The quantum circuit model is adequate to describe quantum algorithms whereas DNA circuit model is adequate to describe DNA algorithms. This thesis establishes the relationship between a quantum qubit and a DNA string using the Bloch sphere. The Bloch sphere is a convenient graphical representation of a qubit in a 3-dimensional space. The model used in this thesis presents one-to-one mapping between qubits, DNA strings, and points on the Bloch sphere. The model is implemented at the real computer - von Neumann machine, therefore, there are restrictions related to the precision of qubits value, the length of DNA strings, and the precisions of coordinates values. The Bloch sphere and the relations between qubits, DNA strings and points are implemented as "Quantum and DNA Computation Simulation Programming System". Quantum and DNA Computation Simulation Programming System is implemented in the Java programming language. This system is a modified and upgraded interactive Quantum Computation applet developed at the Johns Hopkins Center for Educational Resources. The modifications include connecting (a) DNA strings and the Bloch sphere and (b) DNA strings and qubits. There are many researches in Quantum Computing and DNA Computing as independent study fields, but this is the first research which tries to connect Quantum and DNA Computing together using the Bloch sphere.
Quantum and DNA computing are both distributed and parallel types of computing. They're useful for tasks that require high-complexity computations and/or large data sets, such as searching, sorting, merging, pattern recognition, image processing, and encryption. Quantum and DNA algorithms cannot be efficiently simulated on classical computers because they are incapable of coping with parallelism. Quantum computers are more powerful than classical turing machines because of their coherent superposition of states. Biotechnology techniques can be used to evolve DNA computers. Both of these qualities could be captured by combining DNA and quantum computers. Self-assembling quantum logic circuits from gates attached to DNA strands could be achieved using DNA computers. In terms of how inputs are given and outputs are generated, multiple-valued Quantum and DNA computing can be combined in two ways. They can be named as: Multiple-Valued Quantum-DNA Computing, and Multiple-Valued DNA-Quantum Computing.
With the advent of quantum computers, applications of quantum computing have also begun to develop. Quantum computing is a difficult field for non-experts to understand. However, most of the ways to realize quantum computing are limited to books or software implementation. This study proposes a Bloch sphere interactive system to visualize the quantum computing simulation. Users can input the quantum bit and quantum gate for quantum computing by the mobile App. And then, parallel computing is performed by the FPGA to simulate quantum circuits using OpenCL. The resultant data is then sent to a microcontroller, which drives the motors to rotate the Bloch sphere to visualize the single-qubit quantum states.
In DNA computing models, the sugar–phosphate backbone exhibits structural asymmetry, where phosphate groups covalently connect the 5′ carbon of one deoxyribose to the 3′ carbon of the next, producing a directionally polarized framework. This molecular asymmetry serves not only as a fundamental basis for replication fidelity and biological transcription but also as a topological precursor for encoding quantum states. In this paper, we propose that the canonical DNA bases, embedded in this asymmetric backbone, can be modeled as qubits projected onto a Bloch sphere, whose coordinates—x, y, z—arise from the intrinsic asymmetry and base stacking interactions. These quantum models of DNA bases are elements of a two-dimensional Hilbert space H2, establishing a framework for linking DNA computation to artificial intelligence (AI), specifically through entanglement, quantum superposition, and state collapse. We explore the implications of this Bloch sphere–Hilbert space representation for information processing, quantum learning architectures, and AI-enhanced encryption systems.
Interferon-related research has important implications for understanding how biological immune systems function. With the development of DNA sequencing in recent years, interferon has been found in an increasing number of species, and many genome sequences remain to be annotated. Since BLAST is a commonly used sequence comparison method and conventional methods have difficulty to optimize the time complexity of the BLAST algorithm, quantum computing sheds light on accelerating BLAST for sequence annotation after DNA sequencing. Moreover, owing to the limitations of quantum devices, related quantum circuit optimization methods are needed to optimize the circuits of quantum algorithms to reduce the qubits required and complexity of the circuits. This work presents a quantum computing model for sequence annotation after DNA sequencing. First, we propose a quantum gene sequence alignment algorithm that optimizes the time complexity of the second step of BLAST from linear to semilinear by combining Grover's quantum search algorithm and the quantum 01 string alignment algorithm. Second, we propose a quantum circuit optimization method based on a truth table that successfully reduces the number of quantum resources needed to implement the quantum algorithm. Third, we propose a visualization and analysis website that provides online circuit generation and simple experiment running. The experimental results show that our proposed methods can be successfully performed both on virtual and real quantum computer, verifying the usability of the algorithm. Website: http://www.combio-lezhang.online/QGSA.
Abstract: Quantum computing is a new type of computing that uses the principles of quantum physics to perform calculations in ways that are not possible with traditional computers. Unlike classical computers, which process information using bits that are either 0 or 1, quantum computers use quantum bits (qubits) that can exist in multiple states at the same time. This ability allows quantum systems to solve certain complex problems much faster than classical machines. This paper presents a simple and human-centered explanation of quantum computing, focusing on how it works, why it matters, and how it can be applied in real-world situations. The study discusses key concepts such as superposition, entanglement, and quantum gates using easy-to-understand language. It also explores practical applications including cryptography, optimization, drug discovery, and material simulation. In addition, this paper examines the current challenges in quantum computing, such as hardware instability, error correction, limited qubit scalability, and high implementation cost. A conceptual hybrid classical–quantum system model is proposed to show how quantum and classical computers can work together to solve complex problems more efficiently. The goal of this research is to make quantum computing more understandable to students, educators, and decision-makers, and to encourage early awareness and responsible development of this powerful technology. Keywords — quantum computing, qubit, superposition, entanglement, quantum gate, error correction, hybrid computing
Quantum computing leverages fundamental principles of quantum mechanics to perform computations far exceeding classical architectures. At the core of single-qubit quantum state manipulation is the Bloch sphere, a geometric representation mapping the state space of two-level quantum systems onto a three-dimensional unit sphere. This paper presents a comprehensive theoretical framework of qubits, starting from single-qubit geometric state vectors and expanding to rigorous multi-qubit mathematical formulations. Furthermore, it analyzes single-qubit quantum gates as unitary rotations on the Bloch sphere, evaluates multi-qubit system scaling, and explicitly proves state normalization conditions using Born's rule across multi-dimensional Hilbert spaces.