Accurately modelling many-body quantum transport systems poses a challenge both conceptually and computationally due to the growth of the Hilbert space and the multi-scale nature of the geometries and couplings present in most naturally occurring networks. A compounding complexity of such systems is that the environment typically plays a key role in the transport dynamics. Utilising variational unitary transformations that displace environmental degrees of freedom allows for the deployment of a second-order master equation capable of capturing the dynamics of intermediate and strongly coupled systems, which are ubiquitous in microscopic energy transport systems. However, direct implementations of this approach suffer from fundamental scalability issues due to the complexity of the self-consistent equations required to solve for the variational parameters. Here, we present an efficient partitioning scheme that leverages the inherent multi-scale nature of natural energy transport networks. This enables scaling of the variational polaron framework to quantum energy transport systems, constituting hundreds to thousands of sites. Our work unlocks the physically motivated exploration of large transport networks, for example, those present within light-harvesting complexes and exciton transport in disordered semiconductors.
Accurately modelling many-body quantum transport systems poses a challenge both conceptually and computationally due to the growth of the Hilbert space and the multi-scale nature of the geometries and couplings present in most naturally occurring networks. A compounding complexity of such systems is that the environment typically plays a key role in the transport dynamics. Utilising variational unitary transformations that displace environmental degrees of freedom allows for the deployment of a second-order master equation capable of capturing the dynamics of intermediate and strongly coupled systems, which are ubiquitous in microscopic energy transport systems. However, direct implementations of this approach suffer from fundamental scalability issues due to the complexity of the self-consistent equations required to solve for the variational parameters. Here, we present an efficient partitioning scheme that leverages the inherent multi-scale nature of natural energy transport networks. This enables scaling of the variational polaron framework to quantum energy transport systems, constituting hundreds to thousands of sites. Our work unlocks the physically motivated exploration of large transport networks, for example, those present within light-harvesting complexes and exciton transport in disordered semiconductors.
Transport phenomena are ubiquitous throughout the science, engineering and technology disciplines as it concerns energy, mass, charge and information exchange between systems.In particular, energy transport in the nanoscale regime has attracted significant attention within the physical science community due to its potential to explain complex phenomena like the electronic energy transfer in molecular crystals or the Fenna-Matthews-Olson (FMO) / light harvesting complexes in photosynthetic bacteria with long time coherences.Energy transport in these systems is highly affected by environmental noise but surprisingly not always in a detrimental way.It was recently found that situations exist where noise actually enhances the transport phenomena.Such noise can take many forms, but can be characterised in three basic behaviours: quantum, correlation in time, or space.All have been shown potential to offer an energy transport enhancement.The focus of this work is on quantum transport caused by stochastic environment with spatio-temporal correlation.We consider a multi-site nearest neighbour interaction model with pure dephasing environmental noise with spatio-temporal correlation and show how an accelerated rate for the energy transfer results especially under negative spatial correlation (anti-correlation).Spatial anti-correlation provides another control parameter to help one establish the most efficient transfer of energy and may provide new insights into the working of exciton transport in photosynthetic complexes.Further the usage of spatio-temporal correlated noise may be a beneficial resource for efficient transport in large scale quantum networks.
Abstract Transport phenomena are ubiquitous throughout the science, engineering, and technology disciplines as it concerns energy, mass, charge, and information exchange between systems. In particular, energy transport in the nanoscale regime has attracted significant attention within the physical science community due to its potential to explain complex phenomena like the electronic energy transfer in molecular crystals or the Fenna-Matthews-Olson (FMO)/light harvesting complexes in photosynthetic bacteria with long time coherences. Energy transport in these systems is highly affected by environmental noise but surprisingly not always in a detrimental way. It was recently found that situations exist where noise actually enhances the transport phenomena. Such noise can take many forms, but can be characterised in three basic behaviors: quantum, correlation in time, or space. All have been shown potential to offer an energy transport enhancement. The focus of this work is on quantum transport caused by stochastic environment with spatio-temporal correlation. We consider a multi-site nearest neighbor interaction model with pure dephasing environmental noise with spatio-temporal correlation and show how an accelerated rate for the energy transfer results especially under negative spatial correlation (anti-correlation). Spatial anti-correlation provides another control parameter to help one establish the most efficient transfer of energy and may provide new insights into the working of exciton transport in photosynthetic complexes. Further the usage of spatio-temporal correlated noise may be a beneficial resource for efficient transport in large scale quantum networks.
For many years, the fields of quantum optics and biology have rarely shared a common path. In quantum optics, most of the concepts and techniques developed over the years stand for systems where only a few degrees of freedom are considered and, more importantly, where the systems under study are assumed to be completely isolated from their surrounding environment. This situation is far from what we can find in nature. Biological complexes are, by definition, warm, wet and noisy systems subjected to environmental fluctuations, where quantum phenomena are unlikely to be observed. Notwithstanding, in recent years, this paradigm has begun to be questioned by several works where quantum-mechanical concepts have been introduced in order to describe the dynamics of important biological processes, such as energy transport in photosynthetic light-harvesting complexes. The goal of this thesis is twofold. Firstly, we will investigate how ideas and techniques routinely used in quantum optics can be exploited in order to develop new quantum-based spectroscopy techniques and, secondly, we will examine to what extent microscopic quantum phenomena could impact on the efficient transport behavior of photosynthetic light-havesting complexes. This problem is particularly relevant, because the understanding of the fundamental mechanisms that enable the highly efficient transport of energy in photosynthetic systems could lead us to the design of future quantum-inspired light-harvesting technologies, such as high-efficiency organic solar cells.
Recent development of theoretical method for dissipative quantum transport has achieved notable progresses in the weak or strong electron-phonon coupling regime. However, a generalized theory for dissipative quantum transport in a wide parameter space had not been established. In this work, a variational polaron theory for dissipative quantum transport in a wide range of electron-phonon coupling is developed. The optimal polaron transformation is determined by the optimization of the Feynman-Bogoliubov upper bound of free energy. The free energy minimization ends up with an optimal mean-field Hamiltonian and a minimal interaction Hamiltonian. Hence, second-order perturbation can be applied to the transformed system, resulting in an accurate and efficient method for the treatment of dissipative quantum transport with different electron-phonon coupling strength. Numerical benchmark calculation on a single site model coupled to one phonon mode is presented.
Light-harvesting in nature is known to work differently than conventional man-made solar cells. Recent studies found electronic excitations, delocalised over several chromophores, and a soft, vibrating structural environment to be key schemes that might protect and direct energy transfer yielding increased harvest efficiencies even under adversary conditions. Unfortunately, testing realistic models of noise assisted transport at the quantum level is challenging due to the intractable size of the environmental wave function. I developed a powerful tree tensor network states (TTNS) method that finds an optimally compressed explicit representation of the combined electronic and vibrational quantum state. With TTNS it is possible to simulate exciton-phonon quantum dynamics from small molecules to larger complexes, modelled as an open quantum system with multiple bosonic environments. After benchmarking the method on the minimal spin-boson model by reproducing ground state properties and dynamics that have been reported using other methods, the vibrational quantum state is harnessed to investigate environmental dynamics and its correlation with the spin system. To enable simulations of realistic non-Born-Oppenheimer molecular quantum dynamics, a clustering algorithm and novel entanglement renormalisation tensors are employed to interface TTNS with ab initio density functional theory (DFT). A thereby generated model of a pentacene dimer containing 252 vibrational normal modes was simulated with TTNS reproducing exciton dynamics in agreement with experimental results. Based on the environmental state, the (potential) energy surfaces, underlying the observed singlet fission dynamics, were calculated yielding unprecedented insight into the super-exchange mediated avoided crossing mechanism that produces ultrafast and high yield singlet fission. This combination of DFT and TTNS is a step towards large scale material exploration that can accurately predict excited states properties and dynamics. Furthermore, application to biomolecular systems, such as photosynthetic complexes, may give valuable insights into novel environmental engineering principles for the design of artificial light-harvesting systems.
Multi-scale modeling of coupled thermal, mechanical, and electromagnetic phenomena presents one of the most computationally demanding challenges in scientific simulation. These interactions often span quantum-level processes, mesoscopic transport behavior, and macroscopic material responses, requiring integrated frameworks capable of resolving complex nonlinearity, strong coupling, and multi-physics feedback loops. Quantum computing offers a transformative pathway toward more accurate and scalable simulations of such systems. This study investigates quantum-enhanced approaches for modeling heat transfer, stress evolution, electromagnetic field propagation, and their mutual interactions across scales. By integrating quantum algorithms for partial differential equations, quantum linear solvers, and quantum-accelerated spectral and tensor representations, the research explores how quantum models can capture high-dimensional dependencies and strong correlations that classical methods struggle to resolve. The analysis highlights the potential of hybrid quantum–classical workflows to accelerate multi-physics simulation, improve predictive fidelity, and support the design of advanced materials, energy devices, and engineering systems that rely on thermal–mechanical–electromagnetic coupling.