Abstract DNA-based self-assembly enables the programmable arrangement of matter on a molecular scale. It holds promise as a means with which to fabricate high technology products. DNA-based self-assembly has been used to arrange chromophores (dye molecules) covalently linked to DNA to form Förster resonant energy transfer and exciton-based devices. Here we explore the possibility of making coherent exciton information processing devices, including quantum computers. The focus will be on describing the chromophore arrangements needed to implement a complete set of gates that would enable universal quantum computation.
Abstract DNA-based self-assembly enables the programmable arrangement of matter on a molecular scale. It holds promise as a means with which to fabricate high technology products. DNA-based self-assembly has been used to arrange chromophores (dye molecules) covalently linked to DNA to form Förster resonant energy transfer and exciton-based devices. Here we explore the possibility of making coherent exciton information processing devices, including quantum computers. The focus will be on describing the chromophore arrangements needed to implement a complete set of gates that would enable universal quantum computation.
Both DNA and quantum computers have the potential to exceed the power of conventional digital computers, though substantial technical difficulties first must be overcome. Through coherent superposition of states, quantum computers are more powerful than classical Turing machines. DNA computers are evolvable through biotechnology techniques. By combining DNA and quantum computers, both of these characteristics might be captured. DNA computers could be used to self-assemble quantum logic circuits from gates attached to DNA strands. Moreover, quantum computers might be implemented directly using the physical characteristics of the DNA molecule. 1
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.
This dissertation is split into two broad topics: the study of exciton transfer in a variety of organic systems, and the classical numerical simulation of quantum algorithms for chemistry. In both parts, the effects of environmental noise played an integral role. For the latter portion, a main focus was to model the implementation of a quantum algorithm on noisy quantum hardware. For the exciton studies, environmental noise was a defining characteristic of the dynamical process being studied. In both of these research areas, advanced numerical methods were often implemented for use on parallel hardware.\nThe first excitonic system we present is the chlorosome, a large photosynthetic antennae found in some species of green sulfur bacteria, containing tens of thousands of molecules. Using a method capable of simulating massive open quantum systems, we modeled the dynamics of an exciton's migration across this unusual supramolecular object. The chlorosome's unique concentric-roll structure leads to some anomalous effects with respect to initial conditions, and the study is a demonstration that these numerical methods can be directly applied to other very large excitonic simulations.\nIn a theoretical-experimental effort, our next system is a rationally designed DNA-based excitonic system. We designed a coherent "building block'' in which we demonstrated that an exciton can delocalize across multiple pseudoisocyanine molecules, which are non-covalently attached to the DNA backbone. With a combination of computational and experimental evidence, we show that these building blocks can be used to create complex DNA structures with somewhat tunable excitonic properties.\nIn the final excitonics project discussed in this dissertation, we designed a novel ``exciton gate.'' The design is similar in principle to a traditional transistor, and is the first excitonic transistor that can be activated by external light or via an auxiliary flow of excitons. This concept provides a method for a user to regulate the magnitude and direction of excitonic current in a chromophoric system. We show how to overcome the two potential challenges: short-lived second excited states and errors in the precision of orienting molecules relative to each other. Additionally, we show how a universal set of binary logic gates can be constructed, leading to universal classical computation. This new invention may lead to new developments in bioimaging, complex directed photocatalyis, phototherapeutics, and/or chemical sensing.\nIn the second portion of this dissertation, we developed two parallel software packages for the simulation of quantum algorithms. Such simulation packages are essential for the numerical study of quantum algorithms, since a viable large-scale quantum computer has not yet been built. The first package is called the Quantum High-Performance Software Testing Environment (qHiPSTER), which simulates the full Hilbert space of the quantum circuit's state. We simulated 40 qubits, which was the largest simulation of its kind at the time.\nThe second quantum computation software package, the Quantum TensOR Contraction Handler (qTorch), implements a tensor network contraction algorithm. Using the Quantum Approximate Optimization Algorithm (QAOA)/Max-Cut and the Hubbard model as test cases, we found heuristic trends that help a user determine which circuits will benefit most from a tensor network simulation approach.\nFinally, we investigated the effects of noise on chemical state preparation in quantum algorithms. Chemical state preparation is necessary for both of the main quantum algorithmic approaches to chemistry: the variational quantum eigensolver and the quantum phase estimation algorithm. Trends were elucidated with respect to mapping types (Jordan-Wigner versus Bravyi-Kitaev), noise types, and chemical properties.\nWe end with a short discussion of the future of rational excitonic design and classical simulation of quantum phenomena.
Quantum computation and quantum information processing are emerging technologies that have potential to overcome the physical limitation of traditional computation systems. Present quantum systems based on photons, atoms, and molecules, however, all face challenges such as short coherence time, requirement of ultralow temperature and/or high vacuum, and lack of scalability. We report new types of molecular qubits and nanoparticle ensembles based on thermally controllable transformation between J -aggregation and monomeric states of molecular chromophores using pyrrolopyrrole cyanine tethered with polymeric chains such as polycaprolactones as an example. Such supramolecular quantum systems, resembling some feature of light harvesting complexes in photosynthesis, provide new opportunities for manipulating quantum information under mild conditions, which do not require complicated ultracooling and/or high vacuum often involved in superconducting qubits or Rydberg atoms for quantum computation and information processing.
Rapid advances in DNA sequencing have converted genomics and made large strides in our expertise in genetic records and the way it pertain to evolution, health, and illness. DNA fragment meeting is the method of reassembling the small, fragmented reads that might be produced by using DNA sequencing to rebuild the authentic genome. DNA fragment meeting is a primary computational issue, especially for huge genomes, due to the full-size extent of statistics and the intrinsic complexity of correctly assembling genomes. The want for innovative solutions is highlighted by using the truth that traditional computational techniques, such as graph-primarily based totally and heuristic approaches, are resource-intensive, have scaling problems, and can be erroneous in complex genome regions. Because of its exquisite capability to do positive computations at formerly unheard-of quotes and efficiently manipulate hard optimization issues, quantum computing has simply come to mild as a likely road for overcoming those computational barriers. In computationally annoying responsibilities like combinatorial optimization and large-scale statistics processing, quantum algorithms just like the Quantum Approximate Optimization Algorithm (QAOA) and Grover`s set of rules for search optimization have verified the capacity to carry out higher than classical approaches. DNA fragments meeting with quantum computing have the potential to boost accuracy and performance even as additionally develop new possibilities for large-scale genomic study. The essential thoughts of quantum computing, current tendencies in quantum-primarily based totally algorithms and their relevance to the meeting system are all included in this chapter, which investigates the capability of quantum computing as an innovative device for DNA fragment assembly. This chapter intends to explore on how quantum strategies would possibly enhance the precision and effectiveness of DNA assembly, starting the door for brand new tendencies inside the subject of genomics with the aid of using reading the capability and constraints of quantum computing for genomics.