arXiv subjects

Daniel Gunlycke

Publications and source records attributed to Daniel Gunlycke.

At least 19 recordsLinked to original sources

arXiv2026

Universal tuning of F\"{o}rster resonance energy transfer in gate-programmable conductor-dielectric-conductor heterostructures

We develop a quantum-electrodynamical theory for universal tuning of spontaneous emission and F\"{o}rster resonance energy transfer (FRET) in a material-agnostic conductor-dielectric-conductor heterostructure. The platform consists of a dielectric spacer of thickness $W$ bounded by two gate-tunable two-dimensional conductors. The only microscopic input from the surrounding materials is the transverse-magnetic and transverse-electric reflection amplitudes $r_{\TM/\TE}(q_\rho,\omega)$ of the two sheets. Starting from the QED photon propagator, we derive the retarded Maxwell dyadic, the vacuum/Hadamard field propagator, and the time-ordered Feynman propagator in the same geometry. The spontaneous-emission rate is controlled by the local vacuum spectral density, while FRET is controlled by the nonlocal retarded/advanced product $\obG_{\text R}(\bx_D,\bx_A;\omega_D)\Im\,\balpha_A(\omega_D)\obG_{\text A}(\bx_A,\bx_D;\omega_D)$. In the transparent limit $r_{\TM}\to 0$, the near-field FRET rate recovers the bulk $x_\rho^{-6}$ law. In the Dirichlet/PEC branch $r_{\TM}\to -1$, the gapless transverse mode is removed and the donor-acceptor coupling acquires a Bessel-$K$ envelope, giving an exponentially screened FRET rate $\Gamma_{D\to A}\propto\exp(-2\pi x_\rho/W)$ at large lateral separation. In the opposite Neumann/PMC-like branch $r_{\TM}\to 1^{-}$, a nearly gapless transverse mode survives and produces a wide quasi-two-dimensional logarithmic propagator, enhancing the nonlocal electromagnetic coupling over a gate-programmable range $x_{\rho,*}\sim W/(1-r_{\TM})$. For graphene-Er implementations, the same retarded Green tensor also separates dissipative on-shell Er-to-graphene decay, governed by its absorptive part, from dispersive virtual-plasmon-mediated Er-Er coupling, governed by its reactive part; a plasmonic band gap can suppress the former while retaining the latter.

cond-mat.mes-hall↗

arXiv2026

Scalable quantum circuit knitting using a weak-coupling approximation

We present a method for performing distributed quantum computing with controlled approximations. Exact distributed quantum computing requires exponential classical information to reconstruct the quantum process. However, we show how the classical cost is reduced to polynomial if the quantum procedure can be partitioned between a qubit that is weakly coupled the other qubits. We demonstrate our method for a layered circuit based on the circuits used for the quantum approximate optimization algorithm.

quant-ph↗

arXiv2026

Quantum Data Loading for Carleman Linearized Systems: Application to the Lattice-Boltzmann Equation

Nonlinear ordinary and partial differential equations are ubiquitous in science and engineering, yet finding their solutions is often computationally intractable for classical hardware. To determine if quantum computers can offer a practical advantage, one critical challenge that must be solved is determining how to efficiently load exponentially sized matrices onto quantum hardware. In this article, we introduce an alternative linear combination of unitaries (LCU) strategy which relies on an intermediate linear combination of non-unitaries (LCNU) and a systematic embedding procedure. One advantage of this LCU strategy is that it maintains the exact number of terms as in the LCNU. Therefore, this approach offers a data loading framework for matrices that lack an efficient decomposition using the standard LCU alone. Using this approach, we construct a generalized LCNU framework for any Carleman linearized autonomous dynamical system having a polynomial nonlinearity. To demonstrate the effectiveness of our approach, we construct an LCNU for the 3D Carleman linearized lattice Boltzmann equation (LBE). Here, we find that the number of terms in the decomposition scales like $N_s\sim\mathcal{O}(\alpha^2Q^2)$, where $\alpha$ is the Carleman truncation order and $Q$ is the number of discrete velocities. Importantly, $N_s$ is independent of the number of spatial and temporal discretization points. We then perform a resource estimation of our LCNU's T gate cost when combined with the (1) PREP and SELECT block encoding oracles, and (2) variational quantum linear solver. In the former, the T cost scales like $\mathcal{O}(\alpha^3Q^2(\log_2n)^2)$, where $n$ is the total number of spatial grid points. The latter requires exactly $N_s^2(\log_2 (2n_tn^\alpha)+1)$ circuits per iteration for $n_t$ time steps, with a worst case T gate cost of $\mathcal{O}(\alpha (\log_2Qn)^2)$ among them.

quant-ph↗

arXiv2026

Probability Distribution Analysis of the Cascaded Variational Quantum Eigensolver

The cascaded variational quantum eigensolver (CVQE) circumvents the need for iterative communication between the quantum and classical processing units that is necessary in the conventional VQE algorithm. While CVQE offers complete freedom to choose the guiding state as input, not all guiding states suffice for solution accuracy, as well as resource efficiency. Our work presents a process based on trapezoidal-state preparation for selecting guiding states that yield accurate many-electron ground-state solutions with minimal resource consumption. By analyzing the state probability distributions at different stages of the CVQE calculations, we determine the optimal guiding-state parameters for given resource constraints. We demonstrate the process by comparing electronic energies along the minimal-energy path for a prototypical bimolecular reaction, $\mathrm{H}_2 + \mathrm{H}_2^+ \rightarrow \mathrm{H}_3^+ + \mathrm{H}$, using Noisy Intermediate-Scale Quantum (NISQ) computing.

quant-ph↗

arXiv2026

Ground-state energies of Ising models calculated using the samples from a quantum computer that simulates short-time evolution

We find the ground-state energy of the Ising model using the Cascaded Variational Quantum Eigensolver (CVQE) algorithm with the Guided-Sampling Ansatz (GSA) using up to 63 qubits on a quantum computer. We study a heavy-hex lattice to match the qubit architecture, allowing us to perform calculations in the quantum utility regime. We study both a homogeneous and random-coupling model. We locate the boundary of acceptable quantum errors as a function of the number of qubits and coupling strength. An entropic analysis is performed giving insights into the quantum computing performance. A subspace analysis is performed that suggests that the Ising model is especially suited for near-term quantum computing.

quant-ph↗

arXiv2026

Auto-regressive Neural Quantum State Sampling for Selected Configuration Interaction

Accurate ground-state energy calculations remain a central challenge in quantum chemistry due to the exponential scaling of the many-body Hilbert space. Variational Monte Carlo and variational quantum eigensolvers offer promising ansatz optimization approaches but face limitations in convergence as well as hardware constraints. We introduce a particular Selected Configuration Interaction (SCI) algorithm that uses auto-regressive neural networks (ARNNs) to guide subspace expansion for ground-state search. Leveraging the unique properties of ARNNs, our algorithm efficiently constructs compact variational subspaces from learned ground-state statistics, which in turn accelerates convergence to the ground-state energy. Benchmarks on molecular systems demonstrate that ARNN-guided subspace expansion combines the strengths of neural-network representations and classical subspace methods, providing a scalable framework for classical and hybrid quantum-classical algorithms.

quant-ph↗

arXiv2026

Universal tuning of quantum electrodynamic interactions from power laws to exponential screening and logarithmic antiscreening

We introduce a material-agnostic platform for \emph{universal tuning of quantum electrodynamic interactions from power laws to exponential screening and logarithmic antiscreening}, realized in a dielectric spacer bounded by two gate-tunable two-dimensional conductors. The structured electromagnetic environment is completely specified by the transverse-magnetic and transverse-electric reflection amplitudes $r_{\mathrm{TM/TE}}(q_\perp,\omega)$ of the sheets. Starting from the QED action and a Green-function formulation, we resum the multiple-reflection series and show that the interactions are governed by a discrete set of transverse cavity harmonics. In the transparent limit $r_{\rm TM}\to 0$, the interactions reduce to bulk power laws $U(\rho)\propto \rho^{-\alpha}$. In the reflective limit $|r_{\rm TM}|\to 1$, the \emph{phase/parity} of $r_{\rm TM}$ selects two qualitatively distinct branches: a Dirichlet/PEC (screening) branch $r_{\rm TM}\to -1$ that removes the gapless transverse mode and yields an evanescent Bessel-$K$ function $U(\rho)\propto e^{-\pi\rho/d}/\sqrt{\rho/d}$ at $\rho\gg d$, and an opposite Neumann/PMC-like (antiscreening) branch $r_{\rm TM}\to +1$ that retains a gapless mode and can strongly enhance the long-range tail. Thus, the same heterostructure provides in situ electrical control over both the \emph{range} and the \emph{strength} of mediated interactions.

cond-mat.mes-hall↗

arXiv2026

Toward Quantum-Optimized Flow Scheduling in Multi-Beam Digital Satellites

Data flow scheduling for high-throughput multibeam satellites is a challenging NP-hard combinatorial optimization problem. As the problem scales, traditional methods, such as Mixed-Integer Linear Programming and heuristic schedulers, often face a trade-off between solution quality and real-time feasibility. In this paper, we present a hybrid quantum-classical framework that improves scheduling efficiency by casting Multi-Beam Time-Frequency Slot Assignment (MB-TFSA) as a Quadratic Unconstrained Binary Optimization (QUBO) problem. We incorporate the throughput-maximization objective and operational constraints into a compact QUBO via parameter rescaling to keep the formulation tractable. To address optimization challenges in variational quantum algorithms, such as barren plateaus and rugged loss landscapes, we introduce a layer-wise training strategy that gradually increases circuit depth while iteratively refining the solution. We evaluate solution quality, runtime, and robustness on quantum hardware, and benchmark against classical and hybrid baselines using realistic, simulated satellite traffic workloads.

quant-ph↗

arXiv2026

A Linear Combination of Unitaries Decomposition for the Laplace Operator

We provide novel linear combination of unitaries decompositions for a class of discrete elliptic differential operators. Specifically, Poisson problems augmented with periodic, Dirichlet, Neumann, Robin, and mixed boundary conditions are considered on the unit interval and on higher-dimensional rectangular domains. The number of unitary terms required for our decomposition is independent of the number of grid points used in the discretization and scales linearly with the spatial dimension. Explicit circuit constructions for each unitary are given and their complexities analyzed. The worst case depth and elementary gate cost of any such circuit is shown to scale at most logarithmically with respect to number of grid points in the underlying discrete system. We also investigate the cost of using our method within the Variational Quantum Linear Solver algorithm and show favorable scaling. Finally, we extend the proposed decomposition technique to treat problems that include first-order derivative terms with variable coefficients.

quant-ph↗

arXiv2025

Hybrid VQE-CVQE algorithm using diabatic state preparation

We propose a hybrid variational quantum algorithm that has variational parameters used by both the quantum circuit and the subsequent classical optimization. Similar to the Variational Quantum Eigensolver (VQE), this algorithm applies a parameterized unitary operator to the qubit register. We generate this operator using diabatic state preparation. The quantum measurement results then inform the classical optimization procedure used by the Cascaded Variational Quantum Eigensolver (CVQE). We demonstrate the algorithm on a system of interacting electrons and show how it can be used on long-term error-corrected as well as short-term intermediate-scale quantum computers. Our simulations performed on IBM Brisbane produced energies well within chemical accuracy.

quant-ph↗

arXiv2025

Guided sampling ans\"atzes for variational quantum computing

Quantum computing is a promising technology because of the ability of quantum computers to process vector spaces with dimensions that increase exponentially with the simulated system size. Extracting the solution, however, is challenging as the number of quantum gate operations and quantum circuit executions must still scale at most polynomially. Consequently, choosing a good ansatz--a polynomial subset of the exponentially many possible solutions--will be critical to maintain accuracy for larger systems. To address this challenge, we introduce a class of guided sampling ans\"atzes (GSAs) that depend on the system interactions and measured state samples as well as a parameter space. We demonstrate a minimal ansatz for the hydronium cation H$_3$O$^+$ and found that with only 200 circuit executions per structure on the IonQ Aria quantum computer, our calculations produced total energies around the relaxed structure with errors well below $1.59\times10^{-3}$ Ha, thus exceeding chemical accuracy.

quant-ph↗

arXiv2025

An Efficient Decomposition of the Carleman Linearized Burgers' Equation

Herein, we present a polylogarithmic decomposition method to load the matrix from the linearized 1-dimensional Burgers' equation onto a quantum computer. First, we use the Carleman linearization method to map the nonlinear Burgers' equation into an infinite linear system of equations, which is subsequently truncated to order $\alpha$. This new finite linear system is then embedded into a larger system of equations with the key property that its matrix can be decomposed into a linear combination of $\mathcal{O}(\log n_t + \alpha^2\log n_x)$ terms for $n_t$ time steps and $n_x$ spatial grid points. While the terms in this linear combination are not unitary, each can be implemented using a simple block encoding procedure. A numerical simulation is performed by combining our approach with the variational quantuam linear solver demonstrating that accurate solutions are possible. Finally, a resource estimate shows that the upper bound of the Clifford and T gate counts scale like $\mathcal{O}(\alpha(\log n_x)^2)$ and $\mathcal{O}((\log n_x)^2)$, respectively. This is therefore the first explicit polylogarithmic data loading method with respect to $n_x$ and $n_t$ for a Carleman linearized system.

quant-ph↗

arXiv2025

QGeo: A Python package for calculating geodesic control functions for quantum computing

We present a new Python package that uses the established notion of geometric quantum complexity to numerically compute the difficulty associated with preparing a given unitary transformation on a quantum computer. The numerical procedure we implement is presented and discussed. Analyzed quantum circuits include: the quantum fourier transform for up to four qubits, a random circuit with depth 100, and a circuit for analyzing the evolution of a fermionic chain with several lattice sites. This package can be found for download at https://github.com/JAGDiaz/quantum-geodesics

quant-ph↗

arXiv2025

Method for simulating open-system dynamics using mid-circuit measurements on a quantum computer

We present a method for simulating the dynamics of an open electronic system on a quantum computer. This approach entails mid-circuit measurements and resets to simulate the addition or removal of electrons from the system. Our method provides a way to apply non-reversible operations to a quantum computer without the need for additional qubits. Using this method, we simulate the dynamics of an open electronic system consisting of a chain of electrons positioned between two conductive leads on the $ibm\_torino$ quantum computer. We expect the method to be generally applicable to open systems.

quant-ph↗

arXiv2023

Implementing Jastrow--Gutzwiller operators on a quantum computer using the cascaded variational quantum eigensolver algorithm

A Jastrow--Gutzwiller operator adds many-body correlations to a quantum state. However, the operator is non-unitary, making it difficult to implement directly on a quantum computer. We present a novel implementation of the Jastrow--Gutzwiller operator using the cascaded variational quantum eigensolver algorithm. We demonstrate the method on IBM Q Lagos for a Hubbard model.

quant-ph↗

arXiv2023

Cascaded variational quantum eigensolver algorithm

We present a cascaded variational quantum eigensolver algorithm that only requires the execution of a set of quantum circuits once rather than at every iteration during the parameter optimization process, thereby increasing the computational throughput. This algorithm uses a quantum processing unit to probe the needed probability mass functions and a classical processing unit perform the remaining calculations, including the energy minimization. The ansatz form does not restrict the Fock space and provides full control over the trial state, including the implementation of symmetry and other physically motivated constraints.

quant-ph↗

arXiv2022

Variational Quantum Solutions to the Advection-Diffusion Equation for Applications in Fluid Dynamics

Constraints in power consumption and computational power limit the skill of operational numerical weather prediction by classical computing methods. Quantum computing could potentially address both of these challenges. Herein, we present one method to perform fluid dynamics calculations that takes advantage of quantum computing. This hybrid quantum-classical method, which combines several algorithms, scales logarithmically with the dimension of the vector space and quadratically with the number of nonzero terms in the linear combination of unitary operators that specifies the linear operator describing the system of interest. As a demonstration, we apply our method to solve the advection-diffusion equation for a small system using IBM quantum computers. We find that reliable solutions of the equation can be obtained on even the noisy quantum computers available today. This and other methods that exploit quantum computers could replace some of our traditional methods in numerical weather prediction as quantum hardware continues to improve.

quant-ph↗

arXiv2022

Expanding variational quantum eigensolvers to larger systems by dividing the calculations between classical and quantum hardware

We present a hybrid classical/quantum algorithm for efficiently solving the eigenvalue problem of many-particle Hamiltonians on quantum computers with limited resources by splitting the workload between classical and quantum processors. This algorithm reduces the needed number of qubits at the expense of an increased number of quantum evaluations. We demonstrate the method for the Hubbard model and show how the conservation of the z-component of the total spin allows the spin-up and spin-down configurations to be computed on classical and quantum hardware, respectively. Other symmetries can be exploited in a similar manner.

quant-ph↗