arXiv subjects

Chih-Chieh Chen

Publications and source records attributed to Chih-Chieh Chen.

13 recordsLinked to original sources

arXiv2026

Tree Tensor Network Reservoir Computing: Hierarchical Ensemble with Invariant Phase Boundaries

We propose Tree Tensor Network Reservoir Computing (TTN-RC), a quantum-inspired reservoir computing framework for time-series prediction that uses the hierarchical structure of Tree Tensor Networks as a random reservoir. To control the exponential concentration or divergence of TTN outputs, we introduce a hierarchical ensemble method that partitions a fixed-size reservoir into multiple independent sub-reservoirs. In the tested NARMA benchmarks, TTN-RC achieves competitive or improved performance compared with conventional Echo State Networks, especially for tasks requiring higher-order nonlinear processing and longer contextual dependence. We also derive an expected contraction rate based on the reservoir Jacobian and develop a mean-field description of the reservoir-state statistics. These analyses identify an asymptotic stability boundary at $σ_{T}=\sqrt{2}$ in the large per-tree-size limit, where several theoretical indicators converge. Our results provide a design principle for tensor-network-based reservoir computing and clarify how hierarchical reservoir topology controls stability and nonlinear information processing.

quant-ph↗

arXiv2025

Quantum-Circuit Framework for Two-Stage Stochastic Programming via QAOA Integrated with a Quantum Generative Neural Network

Two-stage stochastic programming often discretizes uncertainty into scenarios, but scenario enumeration makes expected recourse evaluation scale at least linearly in the scenario count. We propose qGAN-QAOA, a unified quantum-circuit workflow in which a pre-trained quantum generative adversarial network encodes the scenario distribution and QAOA optimizes first-stage decisions by minimizing the full two-stage objective, including expected recourse cost. With the qGAN parameters fixed after training, we evaluate the objective as the expectation value of a problem Hamiltonian and optimize only the QAOA variational parameters. We interpret non-anticipativity as a condition on measurement outcome statistics and prove that the first-stage measurement marginal is independent of the scenario. For uniformly discretized uncertainty, the diagonal operator encoding the uncertainty admits a sparse Pauli-Z expansion via the Walsh--Hadamard transform, yielding polylogarithmic scaling of gate count and circuit depth with the number of scenarios. Numerical experiments on the stochastic unit commitment problem (UCP) with photovoltaic (PV) uncertainty compare the expected cost of the proposed method with classical expected-value and two-stage stochastic programming baselines, demonstrating the effectiveness of qGAN-QAOA as a two-stage decision model.

quant-ph↗

arXiv2025

Hamiltonian-Driven Architectures for Non-Markovian Quantum Reservoir Computing

We propose a Hamiltonian-level framework for non-Markovian quantum reservoir computing directly tailored for analog hardware implementations. By dividing the reservoir into a system block and an environment block and evolving their joint state under a unified Hamiltonian, our architecture naturally embeds memory backflow by harnessing entanglement-induced information backflow with tunable coupling strengths. Numerical benchmarks on short-term memory tasks demonstrate that operating in non-Markovian regimes yields significantly slower memory decay compared to the Markovian limit. Further analyzing the echo-state property (ESP), showing that the non-Markovian quantum reservoir evolves from two different initial states, they do not converge to the same trajectory even after a long time, strongly suggesting that the ESP is effectively violated. Our work provides the first demonstration in quantum reservoir computing that strong non-Markovianity can fundamentally violate the ESP, such that conventional linear-regression readouts fail to deliver stable training and inference. Finally, we experimentally showed that, with an appropriate time-evolution step size, the non-Markovian reservoir exhibits superior performance on higher-order nonlinear autoregressive moving-average(NARMA) tasks.

quant-ph↗

arXiv2025

Improving the quasi-biennial oscillation via a surrogate-accelerated multi-objective optimization

Simulating the QBO remains a formidable challenge partly due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM to yield a more realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency of the underlying stochastic process from complex observations, we extract a concise set of interpretable and physically meaningful quantities of interest capturing key attributes, such as oscillation amplitude and period. Building on this, we train a probabilistic surrogate model. Leveraging the Karhunen-Loeve decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, thereby capturing the cross-correlations among multiple physics quantities evaluated at different stratospheric pressure levels, and enabling rapid surrogate-based inference at a fraction of the computational cost of inference reliant only on full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution that simultaneously satisfies both objectives. To navigate this challenge, we quantify the bi-criteria trade-off and generate a representative set of Pareto optimal physics parameter values that balance the conflicting objectives. This integrated workflow not only improves the fidelity of QBO simulations but also advances toward a practical framework for tuning modes of variability and quasi-periodic phenomena, offering a versatile template for uncertainty quantification in complex geophysical models.

physics.ao-ph↗

arXiv2020

Integer Programming from Quantum Annealing and Open Quantum Systems

While quantum computing proposes promising solutions to computational problems not accessible with classical approaches, due to current hardware constraints, most quantum algorithms are not yet capable of computing systems of practical relevance, and classical counterparts outperform them. To practically benefit from quantum architecture, one has to identify problems and algorithms with favorable scaling and improve on corresponding limitations depending on available hardware. For this reason, we developed an algorithm that solves integer linear programming problems, a classically NP-hard problem, on a quantum annealer, and investigated problem and hardware-specific limitations. This work presents the formalism of how to map ILP problems to the annealing architectures, how to systematically improve computations utilizing optimized anneal schedules, and models the anneal process through a simulation. It illustrates the effects of decoherence and many body localization for the minimum dominating set problem, and compares annealing results against numerical simulations of the quantum architecture. We find that the algorithm outperforms random guessing but is limited to small problems and that annealing schedules can be adjusted to reduce the effects of decoherence. Simulations qualitatively reproduce algorithmic improvements of the modified annealing schedule, suggesting the improvements have origins from quantum effects.

quant-ph↗

arXiv2019

Hybrid classical-quantum linear solver using Noisy Intermediate-Scale Quantum machines

We propose a realistic hybrid classical-quantum linear solver to solve systems of linear equations of a specific type, and demonstrate its feasibility using Qiskit on IBM Q systems. This algorithm makes use of quantum random walk that runs in $\mathcal{O}(N\log(N))$ time on a quantum circuit made of $\mathcal{O}(\log(N))$ qubits. The input and output are classical data, and so can be easily accessed. It is robust against noise, and ready for implementation in applications such as machine learning.

quant-ph↗

arXiv2017

Large enhancement in thermoelectric efficiency of quantum dot junction due to increase of level degeneracy

It is theoretically demonstrated that the figure of merit ($ZT$) of quantum dot (QD) junctions can be significantly enhanced when the degree of degeneracy of the energy levels involved in electron transport is increased. The theory is based on the the Green-function approach in the Coulomb blockade regime by including all correlation functions resulting from electron-electron interactions associated with the degenerate levels ($L$). We found that electrical conductance ($G_e$) as well as electron thermal conductance ($κ_e$) are highly dependent on the level degeneracy ($L$), whereas the Seebeck coefficient ($S$) is not. Therefore, the large enhancement of $ZT$ is mainly attributed to the increase of $G_e$ when the phonon thermal conductance ($κ_{ph}$) dominates the heat transport of QD junction system. In the serially coupled double-QD case, we also obtain a large enhancement of $ZT$ arising from higher $L$. Unlike $G_e$ and $κ_e$, $S$ is found almost independent on electron inter-dot hopping strength.

cond-mat.mes-hall↗

arXiv2017

Topological Charge/spin density Wave in InAs/GaSb Quantum Wells under an In-plane Magnetic Field

We study interaction effect of quantum spin Hall state in InAs/GaSb quantum wells under an in-plane magnetic field by using the self-consistent mean field theory. We construct a phase diagram as a function of intra-layer and inter-layer interactions, and identify two novel phases, a charge/spin density wave phase and an exciton condensate phase. The charge/spin density wave phase is topologically non-trivial with helical edge transport at the boundary, while the exciton condensate phase is topologically trivial. The Zeeman effect is strongly renormalized due to interaction in certain parameter regimes of the system, leading to a much smaller g-factor, which may stabilize the helical edge transport.

cond-mat.mes-hall↗

arXiv2016

Thermoelectric efficiency of quantum dot molecules at a high temperature bias: the role of thermal-induced voltage

The nonlinear electron and heat currents of quantum dot molecules (QDMs) under a temperature bias are theoretically investigated, including all correlation functions arising from electron Coulomb interactions in QDMs. Unlike the case of double QDs, the maximum efficiency of serially coupled triple QDs (SCTQD) occurs in the orbital depletion regime owing to the interdot Coulomb blockade. The electron current in SCTQD shows a bipolar oscillatory behavior with respect to the variation of QD energy levels, whereas the heat current does not show such a behavior. This is mainly attributed to thermal-induced bias. In addition, we illustrate how the efficiency of SCTQD is influenced by the external load resistance, and phonon heat flow. Finally, a direction-dependent electron current driven by a temperature bias has been demonstrated for a SCTQD with staircase-like energy levels.

cond-mat.mes-hall↗

arXiv2016

A heat engine made of quantum dot molecules with high figure of merits

The transport of electrons through serially coupled quantum dot molecules (SCQDM) is investigated theoretically for application as an energy harvesting engine (EHE), which converts thermal heat to electrical power. We demonstrate that the charge current driven by a temperature bias shows bipolar oscillatory behavior with respect to gate voltage due to the unbalance between electrons and holes, which is different from the charge current driven by an applied bias. In addition, we reveal a Lenz's law between the charge current and the thermal induced voltage. The efficiency of EHE is higher for SCQDM in the orbital depletion situation rather than the orbital filling situation, owing to the many-body effect. The EHE efficiency is enhanced with increasing temperature bias, but suppressed as the electron hopping strength reduces. The fluctuation of QD energy levels at different sites also leads to a reduction of EHE efficiency. Finally, we demonstrate direction-dependent charge currents driven by the temperature bias for application as a novel charge diode.

cond-mat.mes-hall↗

arXiv2015

Theoretical Studies of Graphene Nanoribbon Quantum Dot Qubits

Graphene nanoribbon quantum dot qubits have been proposed as promising candidates for quantum computing applications to overcome the spin-decoherence problems associated with typical semiconductor (e.g. GaAs) quantum dot qubits. We perform theoretical studies of the electronic structures of graphene nanoribbon quantum dots by solving the Dirac equation with appropriate boundary conditions. We then evaluate the exchange splitting based on an unrestricted Hartree-Fock method for the Dirac particles. The electronic wave function and long-range exchange coupling due to the Klein tunneling and the Coulomb interaction are calculated for various gate configurations. It is found that the exchange coupling between qubits can be significantly enhanced by the Klein tunneling effect. The implications of our results for practical qubit construction and operation are discussed.

cond-mat.mes-hall↗

arXiv2015

Quantum interference and structure-dependent orbital-filling effects on the thermoelectric properties of quantum dot molecules

The quantum interference and orbital filling effects on the thermoelectric (TE) properties of quantum dot molecules with high figure of merit are illustrated via the full solution to the Hubbard- Anderson model in the Coulomb blockade regime. It is found that under certain condition in the triangular QD molecule (TQDM), destructive quantum interference (QI) can occur, which leads to vanishing small electrical conductance, while the Seebeck coefficient is modified dramatically. When TQDM is in the charge localization state due to QI, the Seebeck coefficient is seriously suppressed at low temperature, but highly enhanced at high temperature. Meanwhile, the behavior of Lorenz number reveals that it is easier to block charge transport via destructive QI than the electron heat transport at high temperatures. The maximum power factor (PF) in TQDM occurs at full-filling condition. Nevertheless, low-filling condition is preferred for getting maximum PF in serially coupled triple QDs in general. In double QDs, the maximum PF can be achieved either with orbital-depletion or orbital-filling as a result of electron-hole symmetry. Our theoretical work provides a useful guideline for advancing the nanoscale TE technology.

cond-mat.mes-hall↗

arXiv2015

Quantum interference and electron correlation in charge transport through triangular quantum dot molecules

We study the charge transport properties of triangular quantum dot molecule (TQDM) connected to metallic electrodes, taking into account all correlation functions and relevant charging states. The quantum interference (QI) effect of TQDM resulting from electron coherent tunneling between quantum dots is revealed and well interpreted by the long distance coherent tunneling mechanism. The spectra of electrical conductance of TQDM with charge filling from one to six electrons clearly depict the many-body and topological effects. The calculated charge stability diagram for conductance and total occupation numbers match well with the recent experimental measurements. We also demonstrate that the destructive QI effect on the tunneling current of TQDM is robust with respect to temperature variation, making the single electron QI transistor feasible at higher temperatures.

cond-mat.mes-hall↗