The 20th century saw the first revolution of quantum mechanics, setting the rules for our understanding of light, matter, and their interaction. The 21st century is focused on using these quantum mechanical laws to develop technologies which allows us to solve challenging practical problems. One of the directions is the use quantum devices which promise to surpass the best computers and best known classical algorithms for solving certain tasks. Crucial to the design of realistic devices and technologies is to account for the open nature of quantum systems and to cope with their interactions with the environment. In the first part of this dissertation, we show how to tackle classical optimization problems of interest in the physical sciences within one of these quantum computing paradigms, known as quantum annealing (QA). We present the largest implementation of QA on a biophysical problem (six different experiments with up to 81 superconducting quantum bits). Although the cases presented here can be solved on a classical computer, we present the first implementation of lattice protein folding on a quantum device under the Miyazawa-Jernigan model. This is the first step towards studying optimization problems in biophysics and statistical mechanics using quantum devices. In the second part of this dissertation, we focus on the problem of excitonic energy transfer. We provide an intuitive platform for engineering exciton transfer dynamics and we show that careful consideration of the properties of the environment leads to opportunities to engineer the transfer of an exciton. Since excitons in nanostructures are proposed for use in quantum information processing and artificial photosynthetic designs, our approach paves the way for engineering a wide range of desired exciton dy- namics. Finally, we develop the theory for a two-dimensional electronic spectroscopic technique based on fluorescence (2DFS) and challenge previous theoretical results claiming its equivalence to the two-dimensional photon echo (2DPE) technique which is based on polarization. Experimental realization of this technique confirms our the- oretical predictions. The new technique is more sensitive than 2DPE as a tool for conformational determination of excitonically coupled chromophores and o↵ers the possibility of applying two-dimensional electronic spectroscopy to single-molecules.
The 20th century saw the first revolution of quantum mechanics, setting the rules for our understanding of light, matter, and their interaction. The 21st century is focused on using these quantum mechanical laws to develop technologies which allows us to solve challenging practical problems. One of the directions is the use quantum devices which promise to surpass the best computers and best known classical algorithms for solving certain tasks. Crucial to the design of realistic devices and technologies is to account for the open nature of quantum systems and to cope with their interactions with the environment. In the first part of this dissertation, we show how to tackle classical optimization problems of interest in the physical sciences within one of these quantum computing paradigms, known as quantum annealing (QA). We present the largest implementation of QA on a biophysical problem (six different experiments with up to 81 superconducting quantum bits). Although the cases presented here can be solved on a classical computer, we present the first implementation of lattice protein folding on a quantum device under the Miyazawa-Jernigan model. This is the first step towards studying optimization problems in biophysics and statistical mechanics using quantum devices. In the second part of this dissertation, we focus on the problem of excitonic energy transfer. We provide an intuitive platform for engineering exciton transfer dynamics and we show that careful consideration of the properties of the environment leads to opportunities to engineer the transfer of an exciton. Since excitons in nanostructures are proposed for use in quantum information processing and artificial photosynthetic designs, our approach paves the way for engineering a wide range of desired exciton dy- namics. Finally, we develop the theory for a two-dimensional electronic spectroscopic technique based on fluorescence (2DFS) and challenge previous theoretical results claiming its equivalence to the two-dimensional photon echo (2DPE) technique which is based on polarization. Experimental realization of this technique confirms our the- oretical predictions. The new technique is more sensitive than 2DPE as a tool for conformational determination of excitonically coupled chromophores and o↵ers the possibility of applying two-dimensional electronic spectroscopy to single-molecules.
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.
Lattice protein folding models are a cornerstone of computational biophysics. Although these models are a coarse grained representation, they provide useful insight into the energy landscape of natural proteins. Finding low-energy threedimensional structures is an intractable problem even in the simplest model, the Hydrophobic-Polar (HP) model. Description of protein-like properties are more accurately described by generalized models, such as the one proposed by Miyazawa and Jernigan (MJ), which explicitly take into account the unique interactions among all 20 amino acids. There is theoretical and experimental evidence of the advantage of solving classical optimization problems using quantum annealing over its classical analogue (simulated annealing). In this report, we present a benchmark implementation of quantum annealing for lattice protein folding problems (six different experiments up to 81 superconducting quantum bits). This first implementation of a biophysical problem paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.
Quantum computing has progressed from a theoretical concept to a practical experimental technology. By processing information according to quantum mechanics rather than classical rules, quantum computers may offer advantages for certain problems. Although a few algorithms have proven speedups, it remains unclear where quantum computers will be practically useful, especially since current devices are noisy, small, and not yet fault tolerant. This thesis explores quantum computing for biophysically motivated combinatorial problems, with a focus on lattice protein models. In these simplified models, a protein is represented as a chain on a discrete lattice. Despite their simplicity, lattice proteins retain important features of real proteins, such as such as the folding of a chain with exponentially many possible shapes to a unique minimum-energy structure and steric constraints.To run lattice protein problems on quantum hardware, they must first be formulated using binary variables and objective functions. The latter typically contain soft constraints needed to ensure physical solutions. In Papers~\I--\II, we developed and extended a coordinate-based mapping for lattice protein folding, enabling D-Wave's hybrid quantum-classical annealer to fold both 2D hydrophobic-polar and 3D Miyazawa-Jernigan lattice proteins. In Paper~\III, we adapted the approach to the gate-based paradigm and showed how quadratic soft constraints could be removed by a mixer developed for the maximum-independent-set problem. Papers~\IV--\V addressed lattice protein design using quantum annealing, quantum approximate optimization algorithm, quantum alternating operator ansatz and hardware-efficient ansatz, while Paper~\VI extended the framework to multi-chain thermodynamics and the reconstruction of densities of states and heat-capacity features.Together, these papers show how folding, design, and multi-chain thermodynamics can be formulated, implemented, and analyzed using present-day quantum and hybrid hardware. A recurring limitation is the treatment of soft constraints. In quantum annealing, their high connectivity leads to error accumulation. On gate-based hardware, they increase circuit depth and sensitivity to noise. At the same time, temporary constraint violations can provide useful freedom during optimization. Further progress will therefore require improved formulations, algorithms and hardware that reduce the cost of enforcing physical solutions while retaining this flexibility.
Ultrafast spectroscopy experiments show that photosynthetic systems can preserve quantum beats in the process of electronic energy transfer between pigments, even at room temperature. But what does this discovery imply for biology - and for quantum mechanics? This dissertation examines photosynthesis through the tools of quantum information science. We evaluate to what extent photosynthesis can be thought of as a type of quantum technology, and consider how we can apply experimental tools widely used for quantum technologies (state tomography and coherent control) to photosynthesis. Throughout, we use the Fenna-Matthews-Olson (FMO) complex of greens sulfur bacteria as a model photosynthetic pigment-protein complex.In Part I, we evaluate two mechanisms for the possible biological relevance of quantum coherent motion. First, we consider the extent to which dynamics in light-harvesting systems exhibit the quantum speedups characteristic of quantum algorithms and quantum walks. Second, we demonstrate a ratchet effect in electronic energy transfer enabled by partially coherent dynamics. To do so, we build a new model of energy transfer between weakly coupled light-harvesting complexes to understand how electronic coherence arises under natural conditions.In Part II, we present proposals for experimental probes of light harvesting dynamics with ultrafast spectroscopy. We show how the signal from pump-probe spectroscopy can be formally inverted to determine the excited state electronic density matrix. Finally, we examine the feasiblity of coherent control experiments on light-harvesting systems, and provide two realistic targets feasible with present-day technology.
In the first part of the dissertation, we develop a theoretical approach to analyze nonlinear spectroscopy experiments based on the formalism of quantum state (QST) and process tomography (QPT). In it, a quantum system is regarded as a black box which can be systematically tested in its performance, very much like an electric circuit is tested by sending a series of inputs and measuring the corresponding outputs, but in the quantum sense. We show how to collect a series of pump-probe or photon-echo experiments, and by varying polarizations and frequency components of the perturbations, reconstruct the quantum state (density matrix) of the probed system for a set of different initial conditions, hence simultaneously achieving QST and QPT. Furthermore, we establish the conditions under which a set of two-dimensional optical spectra also yield the desired results. Simulations of noisy experiments with inhomogeneous broadening show the feasibility of the protocol. A spin-off of this work is our suggestion of a witness that distinguishes between spectroscopic time-oscillations corresponding to vibronic only coherences against their electronic counterparts. We conclude by noting that the QST/QPT approach to nonlinear spectroscopy sheds light on the amount of quantum information contained in the output of an experiment, and hence, is a convenient theoretical and experimental paradigm even when the goal is not to perform a full QPT. In the second part of the thesis, we discuss a methodology to study the electronic dynamics of complex molecular systems, such as photosynthetic units, in the framework of time-dependent density functional theory (TD-DFT). By treating the electronic degrees of freedom as the system and the nuclear ones as the bath, we develop an open quantum systems (OQS) approach to TD-DFT. We formally extend the theoretical backbone of TD-DFT to OQS, and suggest a Markovian bath functional which can be readily included in electronic structure codes.
Quantum annealing is a computational paradigm in which optimisation problems are mapped onto the energy landscape of an interacting quantum system and explored through its dynamical evolution. By continuously transforming a simple initial Hamiltonian into one whose ground state encodes the solution, the system traverses a complex landscape via a combination of quantum fluctuations, tunnelling processes, and dissipative dynamics. Unlike gate-based quantum computing, quantum annealing is a specialised and near-term approach aimed primarily at discrete optimisation and sampling tasks. While it is not expected to provide polynomial-time solutions to NP-hard problems in the worst case, it offers a physically motivated heuristic for navigating rugged energy landscapes that arise across science and engineering. Modern quantum annealers realise programmable spin systems with thousands of qubits, placing them among the largest controllable quantum devices currently available. As a result, their significance extends beyond optimisation: they also function as experimental platforms for studying non-equilibrium many-body quantum dynamics in regimes that are difficult to access using classical simulation. In this review we present an accessible introduction to the principles of quantum annealing, describe the main hardware platforms and algorithmic techniques, and analyse how tunnelling, spectral gaps, and open-system effects shape computational performance. We survey applications ranging from optimisation and machine learning to quantum simulation and many-body physics, and discuss the central challenges in benchmarking, scaling, and control. These perspectives position quantum annealing as a distinctive framework at the interface of optimisation, stochastic sampling, and programmable quantum dynamics, with a role that is complementary to both classical algorithms and gate-based quantum computing.