arXiv subjects

Zi-Ming Li

Publications and source records attributed to Zi-Ming Li.

4 recordsLinked to original sources

arXiv2026

Full-quantum variational dynamics simulation for time-dependent Hamiltonians with global spectral discretization

The most widely used approach for simulating the dynamics of time-dependent Hamiltonians via quantum computation depends on the quantum-classical hybrid variational quantum time evolution algorithm, in which ordinary differential equations of the variational coefficients for determining time evolution are solved via classical simulations with a time discretization method. We here present a full-quantum approach, in which ordinary differential equations of the variational coefficients are transformed into static linear equations via the Chebyshev spectral discretization method and then solved via the quantum singular value transformation algorithm. Our full quantum algorithm avoids classical feedback, achieves exponential convergence for smooth Hamiltonians, and yields a quantum circuit depth that is independent of the number of time steps. We demonstrate two implementation strategies, with a global formulation designed for fault-tolerant architectures and a sequential formulation tailored to near-term devices, and validate the approach through numerical simulations of proton-hydrogen charge-transfer dynamics, a prototypical time-dependent quantum chemistry problem. This work establishes a systematic pathway from quantum-classical hybrid variational quantum algorithms to full-quantum solvers for general time-dependent Hamiltonians, particularly those whose dynamics admit compact variational descriptions, opening a route toward full quantum computational advantages in time-dependent simulations.

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arXiv2025

Efficient Preparation of Decoherence Free Subspace Basis States

Decoherence-free subspace (DFS) provides a crucial mechanism for passive error mitigation in quantum computation by encoding information within symmetry-protected subspaces of the Hilbert space, which are immune from collective decoherence. Constructing a complete set of orthogonal basis states for the DFS is essential to realize fault-tolerant quantum computation by using the DFS codes. However, existing methods for preparing these basis states are often non-scalable, platform-specific, or yield mixed states. Here, we propose a deterministic approach to prepare pure, orthogonal and complete DFS basis states for systems of arbitrary size composed of qubits. Our method employs projective measurements and quantum circuits with single-qubit, two-qubit and Toffoli gates. We provide a rigorous resource cost analysis both mathematically and numerically. Meanwhile, we demonstrate the realizability of our method on NISQ devices by discussing how to implement our method on a superconducting chip. The proposed method offers a universal solution for preparing the DFS basis states across diverse quantum computing platforms and system sizes, which is realizable in the NISQ era.

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arXiv2025

Speedup Chip Yield Analysis by Improved Quantum Bayesian Inference

The semiconductor chip manufacturing process is complex and lengthy, and potential errors arise at every stage. Each wafer contains numerous chips, and wafer bin maps can be generated after chip testing. By analyzing the defect patterns on these wafer bin maps, the steps in the manufacturing process where errors occurred can be inferred. In this letter, we propose an improved quantum Bayesian inference to accelerate the identification of error patterns on wafer bin maps, thereby assisting in chip yield analysis. We outline the algorithm for error identification and detail the implementation of improved quantum Bayesian inference. Our results demonstrate the speed advantage of quantum computation over classical algorithms with a real-world problem, highlighting the practical significance of quantum computation.

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arXiv2025

Quantum Algorithm for Vector Set Orthogonal Normalization and Matrix QR Decomposition with Polynomial Speedup

Vector set orthogonal normalization and matrix QR decomposition are fundamental problems in matrix analysis with important applications in many fields. We know that Gram-Schmidt process is a widely used method to solve these two problems. However, the existing methods, including Gram-Schmidt process have problems of high complexity, scaling $O(N^3)$ in the system dimension $N$, which leads to difficulties when calculating large-scale or ill-conditioned problems. With the development of quantum information processing, a series of quantum algorithms have been proposed, providing advantages and speedups over classical algorithms in many fields. In this paper, we propose quantum algorithms to solve these two problems based on the idea of Gram-Schmidt process and quantum phase estimation. The complexity of proposed quantum algorithms is also theoretically and numerically analyzed. We find that our algorithms provide polynomial acceleration over the best-known classical and quantum algorithms on these two problems, scaling $O(N^2\mathrm{poly}(\log N))$ in the dimension $N$ of the system.

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