arXiv subjects

Xinchi Huang

Publications and source records attributed to Xinchi Huang.

At least 19 recordsLinked to original sources

arXiv2026

An efficient quantum Hadamard product algorithm for functions

We propose an efficient quantum algorithm for preparing the Hadamard product state of two quantum states whose amplitudes are generated by functions on a uniform grid with grid number $N$. As the Hadamard product operation is non-unitary, the conventional approach generally suffer from a success probability that scales as $O(1/N)$, leading to an $O(\sqrt{N})$ query complexity even with quantum amplitude amplification. Our method exploits the Fourier-space representation of the input functions, where the Hadamard product can be treated through a convolution structure and approximated using localized Fourier coefficients. The resulting quantum circuit has complexity governed by the Fourier regularity of the underlying functions rather than directly by the grid number. In particular, when either of the input functions has finitely many non-zero Fourier coefficients, the algorithm prepares the exact quantum Hadamard product state under $N$-independent query complexity. Moreover, we also propose a novel quantum circuit for the partial inner product as one of its applications.

quant-ph↗

arXiv2026

Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition

Quantum computing is a promising technology for accelerating partial differential equation solvers applied to large-scale real-world problems. However, reconstructing a classical representation of the solution from the quantum state remains a significant bottleneck. We propose a problem-specific method, called proper orthogonal decomposition-based readout (PODR), to improve readout efficiency by precomputing characteristic features of the solution. The present method consists of an offline stage and an online stage. In the offline stage, a set of basis functions representing the dominant features of the target problem is constructed from representative solution data using classical computations. In the online stage, the quantum state is projected onto this reduced basis, and only the minimal set of weight coefficients is extracted to reconstruct the solution. Since the offline stage is carried out only once, the proposed PODR method is especially advantageous for simulations with varying parameters, which are common in computational fluid dynamics (CFD). Futhermore, we apply the proposed method to benchmark problems in fluid dynamics and demonstrate that PODR significantly reduces both the number of measurements and the computational resources in the online stage compared with conventional readout methods.

quant-ph↗

arXiv2026

Approximate Amplitude Encoding with the Adaptive Interpolating Quantum Transform

Amplitude encoding of real-world data on quantum computers is often the workflow bottleneck: direct amplitude encoding scales poorly with input size and can offset any speedups in subsequent processing. Fourier-based sparse amplitude encoding lowers cost by retaining only a small subset of dominant coefficients, but its fixed, non-adaptive basis leads to significant information loss. In this work, we replace the Fourier transform with the adaptive interpolating quantum transform (AIQT) in the sparse amplitude encoding workflow. The AIQT learns a data-adapted basis that concentrates information into a small number of coefficients. Consequently, at matched sparsity, the AIQT retains more information and achieves lower reconstruction error compared to the Fourier baseline. On financial time-series data, the AIQT reduces reconstruction error by 40% relative to the Fourier baseline, and on image datasets the reduction is up to 50% at the same sparsity level, with nearly identical encoding gate cost. Crucially, the approach preserves the efficiency of Fourier-based methods: the AIQT is built on the structure of the quantum Fourier transform circuit. Its gate count scales quadratically with the number of qubits, while classical evaluation can be carried out in quasilinear time. In addition, the AIQT is trained without labels and does not require sampling from quantum hardware or a simulator, removing a major bottleneck in data-driven amplitude-encoding methods.

quant-ph↗

arXiv2025

Real and Fourier space readout methods: Comparison of complexity and applications to CFD problems

Quantum computing is a promising technology that accelerates the partial differential equations solver for practical problems. The reconstruction of solutions (i.e., the readout of quantum states) remains a crucial problem, although numerous efficient quantum algorithms have been proposed. In this paper, we propose and compare several efficient readout methods in the real and the Fourier space. The Fourier space readout (FSR) and the proposed approximate real space readout (ARSR) methods are currently the most efficient and practical ones for the purpose of reconstructing continuous real-valued functions. In contrast, the quantum amplitude estimation (QAE) based methods (especially in the Fourier space) are favorable for mid-term/far-term quantum devices. Besides, we apply the methods for benchmark solutions in computational fluid dynamics (CFD) and demonstrate great improvements compared to the conventional sampling method for large grid numbers. Equipped with efficient readout methods, we further show that a 2D Burgers' equation can be solved efficiently without using the expensive strategy of linearization. It suggests the potential quantum advantages for some practical applications on mid-term quantum devices.

quant-ph↗

arXiv2025

Conditional Stability and Numerical Reconstruction of a Parabolic Inverse Source Problem Using Carleman Estimates

In this work we develop a new numerical approach for recovering a spatially dependent source component in a standard parabolic equation from partial interior measurements. We establish novel conditional Lipschitz stability and Hölder stability for the inverse problem with and without boundary conditions, respectively, using suitable Carleman estimates. Then we propose a numerical approach for solving the inverse problem using conforming finite element approximations in both time and space. Moreover, by utilizing the conditional stability estimates, we prove rigorous error bounds on the discrete approximation. We present several numerical experiments to illustrate the effectiveness of the approach.

math.NA↗

arXiv2025

Fourier space readout method for efficiently recovering functions encoded in quantum states

Applying quantum computing in the computer-aided engineering (CAE) problems are highly expected since quantum computers yield potential exponential speedups for the operations between extremely large matrices and vectors. Although efficient quantum algorithms for the above problems have been intensively investigated, it remains a crucial task to extract all the grid-point values encoded in the prepared quantum states, which was believed to eliminate the achieved quantum advantage. In this paper, we propose a quantum-classical hybrid Fourier space readout (FSR) method to efficiently recover the underlying function from its corresponding quantum state. We provide explicit quantum circuits, followed by theoretical and numerical discussions on its complexity. In particular, the complexity on quantum computers has only a logarithmic dependence on the grid number, while the complexity on classical computers has a linear dependence on the number of target points instead of the grid number. Our result implies that the achieved quantum speedups are not necessarily ruined when we read out the solutions to the CAE problems.

quant-ph↗

arXiv2025

Well-posedness of initial-boundary value problem for time-fractional diffusion-wave equation with time-dependent coefficients

We consider the well-posedness of the initial-boundary value problem for a time-fractional partial differential equation with the fractional order lying in (1,2]. For the case of time-dependent coefficients, it is difficult to give an explicit solution formula by the eigenfunction expansion method. In order to deal with the case of time-varying coefficients, we first show the unique existence and regularity of solution to a system of time-fractional ordinary differential equations. Then the unique existence of the weak solution to the time-fractional partial differential equation and improved regularity are derived by using the Galerkin method.

math.AP↗

arXiv2025

Tensor decomposition technique for qubit encoding of maximal-fidelity Lorentzian orbitals in real-space quantum chemistry

To simulate the real- and imaginary-time evolution of a many-electron system on a quantum computer based on the first-quantized formalism, we need to encode molecular orbitals (MOs) into qubit states for typical initial-state preparation. We propose an efficient scheme for encoding an MO as a many-qubit state from a Gaussian-type solution that can be obtained from a tractable solver on a classical computer. We employ the discrete Lorentzian functions (LFs) as a fitting basis set, for which we maximize the fidelity to find the optimal Tucker-form state to represent a target MO. For $n_{\mathrm{prod}}$ three-dimensional LFs, we provide the explicit circuit construction for the state preparation involving $\mathcal{O} (n_{\mathrm{prod}})$ CNOT gates. Furthermore, we introduce a tensor decomposition technique to construct a canonical-form state to approximate the Tucker-form state with controllable accuracy. Rank-$R$ decomposition reduces the CNOT gate count to $\mathcal{O} (R n_{\mathrm{prod}}^{1/3}).$ We demonstrate via numerical simulations that the proposed scheme is a powerful tool for encoding MOs of various quantum chemical systems, paving the way for first-quantized calculations using hundreds or more logical qubits.

quant-ph↗

arXiv2025

Quantum State Readout via Overlap-Based Feature Extraction

In this study, a method for quantum state readout and feature extraction is developed using quantum overlap-based fitting of function expansions. The approach involves the quantum calculation of quantum overlaps between a target quantum state and a linear combination of basis functions, such as Lorentzian functions, via measurements, and classical optimization of the parameters in the function expansion. This method is particularly effective in scenarios where the quantum state is approximately represented as a continuous function and expressed as a combination of localized functions. The proposed method involves a quantum state readout for both the raw and absolute values of the amplitudes in the quantum state. Preliminary numerical simulations were performed to reconstruct the grid-based wave function and X-ray absorption spectra from a quantum state, and the results show that our proposed method requires fewer measurements compared to conventional quantum state measurement techniques.

quant-ph↗

arXiv2025

A probabilistic imaginary-time evolution quantum algorithm for advection-diffusion equation: Explicit gate-level implementation and comparisons to quantum linear system algorithms

Simulating differential equations on classical computers becomes an intractable problem if the grid size is extremely large. Quantum computers are believed to achieve a possibly exponential speedup in the matrix operation. In this paper, we propose a quantum algorithm for solving the advection-diffusion-reaction equation by employing a novel approximate probabilistic imaginary-time evolution (PITE) operator. First, the effectiveness of the proposed approximate PITE operator is justified by the theoretical evaluation of the error. Next, we construct the explicit quantum circuit to realize the imaginary-time evolution of the Hamiltonian coming from the advection-diffusion equation, whose gate complexity is logarithmic regarding the size of the discretized Hamiltonian matrix. Compared to the existing algorithms for the quantum linear system problem, our algorithm achieves an exponential speedup regarding the matrix size at the cost of a worse dependence on the error bound. Moreover, numerical simulations using gate-based quantum emulator for 1D/2D examples are also provided to verify our algorithm. Finally, we extend our algorithm to the coupled system of advection-diffusion equations to show the prospects for practical applications.

quant-ph↗

arXiv2024

Approximate real-time evolution operator for potential with one ancillary qubit and application to first-quantized Hamiltonian simulation

In this article, we compare the methods implementing the real-time evolution operator generated by a unitary diagonal matrix where its entries obey a known underlying real function. When the size of the unitary diagonal matrix is small, a well-known method based on Walsh operators gives a good and precise implementation. In contrast, as the number of qubits grows, the precise one uses exponentially increasing resources, and we need an efficient implementation based on suitable approximate functions. Using piecewise polynomial approximation of the function, we summarize the methods with different polynomial degrees. Moreover, we obtain the overheads of gate count for different methods concerning the error bound and grid parameter (number of qubits). This enables us to analytically find a relatively good method as long as the underlying function, the error bound, and the grid parameter are given. This study contributes to the problem of encoding a known function in the phase factor, which plays a crucial role in many quantum algorithms/subroutines. In particular, we apply our methods to implement the real-time evolution operator for the potential part in the first-quantized Hamiltonian simulation and estimate the resources (gate count and ancillary qubits) regarding the error bound, which indicates that the error coming from the approximation of the potential function is not negligible compared to the error from the Trotter-Suzuki formula.

quant-ph↗

arXiv2024

Optimized synthesis of circuits for diagonal unitary matrices with reflection symmetry

During the noisy intermediate-scale quantum (NISQ) era, it is important to optimize the quantum circuits in circuit depth and gate count, especially entanglement gates, including the CNOT gate. Among all the unitary operators, diagonal unitary matrices form a special class that plays a crucial role in many quantum algorithms/subroutines. Based on a natural gate set {CNOT, Rz}, quantum circuits for general diagonal unitary matrices were discussed in several previous works, and an optimal synthesis algorithm was proposed in terms of circuit depth. In this paper, we are interested in the implementation of diagonal unitary matrices with reflection symmetry, which has promising applications, including the realization of real-time evolution for first quantized Hamiltonians by quantum circuits. Owing to such a symmetric property, we show that the quantum circuit in the existing work can be further simplified and propose a constructive algorithm that optimizes the entanglement gate count. Compared to the previous synthesis methods for general diagonal unitary matrices, the quantum circuit by our proposed algorithm achieves nearly half the reduction in both the gate count and circuit depth.

quant-ph↗

arXiv2023

Blow-up for time-fractional diffusion equations with superlinear convex semilinear terms

This article is concerned with a semilinear time-fractional diffusion equation with a superlinear convex semilinear term in a bounded domain $Ω$ with the homogeneous Dirichlet, Neumann, Robin boundary conditions and non-negative and not identically vanishing initial value. The order of the fractional derivative in time is between $1$ and $0$, and the elliptic part is with time-independent coefficients. We prove (i) The solution with any initial value blow-up if the eigenvalue $λ_1$ of the elliptic operator with the minimum real part is non-positive. (ii) Otherwise, the solution blows up if a weighted $L^1$-norm of initial value is greater than some critical value give by $λ_1$. We provide upper estimates of the blow-up times. The key is a comparison principle for time-fractional ordinary differential equations.

math.AP↗

arXiv2023

Initial-boundary value problems for coupled systems of time-fractional diffusion equations

This article deals with the initial-boundary value problem for a moderately coupled system of time-fractional diffusion equations. Defining the mild solution, we establish fundamental unique existence, limited smoothing property and long-time asymptotic behavior of the solution, which mostly inherit those of a single equation. Owing to the coupling effect, we also obtain the uniqueness for an inverse problem on determining all the fractional orders by the single point observation of a single component of the solution.

math.AP↗

arXiv2022

Long-time asymptotic estimate and a related inverse source problem for time-fractional wave equations

Lying between traditional parabolic and hyperbolic equations, time-fractional wave equations of order $α\in(1,2)$ in time inherit both decaying and oscillating properties. In this article, we establish a long-time asymptotic estimate for homogeneous time-fractional wave equations, which readily implies the strict positivity/negativity of the solution for $t\gg1$ under some sign conditions on initial values. As a direct application, we prove the uniqueness for a related inverse source problem on determining the temporal component.

math.AP↗

arXiv2022

Carleman estimates for a magnetohydrodynamics system and application to inverse source problems

In this article, we consider a linearized magnetohydrodynamics system for incompressible flow in a three-dimensional bounded domain. We first prove two kinds of Carleman estimates. This is done by combining the Carleman estimates for the parabolic and the elliptic equations. Then we apply the Carleman estimates to prove Holder type stability results for some inverse source problems.

math.AP↗

arXiv2021

Determination of source and initial values for acoustic equations with a time-fractional attenuation

We consider the inverse problem of determining the initial states or the source term of a hyperbolic equation damped by some non-local time-fractional derivative. This framework is relevant to medical imaging such as thermoacoustic or photoacoustic tomography. We prove a stability estimate for each of these two problems, with the aid of a Carleman estimate specifically designed for the governing equation.

math.AP↗

arXiv2021

Well-posedness and asymptotic estimate for a diffusion equation with time-fractional derivative

In this paper, we study the asymptotic estimate of solution for a mixed-order time-fractional diffusion equation in a bounded domain subject to the homogeneous Dirichlet boundary condition. Firstly, the unique existence and regularity estimates of solution to the initial-boundary value problem are considered. Then combined with some important properties, including a maximum principle for a time-fractional ordinary equation and a coercivity inequality for fractional derivatives, the energy method shows that the decay in time of the solution is dominated by the term $t^{-α}$ as $t\to\infty$.

math.AP↗