Matrix Low-dimensional Qubit Casting Based Quantum Electromagnetic Transient Network Simulation Program

Abstract: In modern power systems, the integration of converter-interfaced generations requires the development of electromagnetic transient network simulation programs (EMTP) that can capture rapid fluctuations. However, as the power system scales, the EMTP’s computing complexity increases exponentially, leading to a curse of dimensionality that hinders its practical application. Facing this challenge, quantum computing offers a […]

Multilevel gate set optimization of quantum circuits for partial differential equations

Abstract: Solving partial differential equations (PDEs), which is pervasive in science and engineering, is emerging as a promising application area for quantum computing because it can be reduced to Hamiltonian simulation. Unlike quantum chemistry, Pauli-term expansion is not useful for a PDE Hamiltonian, since it ends up with an exponential number of Pauli terms. Recently, […]

Emergent Bifurcations in Quantum Circuit Stability from Hidden Parameter Statistics

Abstract: Circuit compression is a key requirement for near-term quantum computing, yet the factors that govern stability under gate removal are not fully understood. We study this problem via a large-scale numerical analysis of 300 structurally uniform circuits across 10, 12, and 14 qubits. Despite identical macroscopic resources, each ensemble separates into two stability classes […]

Quantum Circuit-Based Adaptation for Credit Risk Analysis

Abstract: Noisy and Intermediate-Scale Quantum, or NISQ, processors are sensitive to noise, prone to quantum decoherence, and are not yet capable of continuous quantum error correction for fault-tolerant quantum computation. Hence, quantum algorithms designed in the pre-fault-tolerant era cannot neglect the noisy nature of the hardware, and investigating the relationship between quantum hardware performance and […]

QCHFT: Quantum Cross-Hybrid Fine-Tuning for LLMs

Abstract: When full-parameter updates are impractical for large language models (LLMs), parameter-efficient fine-tuning (PEFT) is commonly employed to reduce the number of trainable parameters. Classical PEFT methods, such as low-rank adaptation (LoRA), are limited to linear transformations and may not capture complex, high-order feature interactions under stringent parameter constraints. In contrast, parameterized quantum circuits (PQCs) […]

Reducing Maximum Subcircuits Depth in Quantum Circuit Cutting

Abstract: Noisy intermediate-scale quantum (NISQ) devices with limited qubit count and connectivity limit the scale of quantum circuits that can be executed. Circuit cutting methods simulate larger quantum computers by decomposing large circuits into small subcircuits that can be executed on NISQ hardware. Existing circuit cutting methods only consider the qubit count of the target […]

Quantum Communication Complexity of Regularized Linear Regression Protocols

Abstract: Linear regression is fundamental to statistical analysis and machine learning, but its application to large-scale datasets necessitates distributed computing. The problem also arises in quantum computing, where handling extensive data requires distributed approaches. This paper investigates distributed linear regression in the quantum coordinator model. Building upon the distributed quantum least squares protocol developed by […]

Cut&Shoot: Distributed Execution of Quantum Circuit Fragments

Abstract: Quantum computing is progressing at a rapid pace, although still constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, such as restricted qubit counts and high susceptibility to noise. To address these constraints, researchers have begun adapting classical software engineering principles to the quantum realm, giving rise to the field of quantum software […]

Quantum compressed sensing tomographic reconstruction algorithm

Abstract: Computed tomography (CT) is a non-destructive technique for observing internal images and has proven highly valuable in medical diagnostics. Recent advances in quantum computing have begun to influence tomographic reconstruction techniques. The quantum tomographic reconstruction algorithm is less affected by artifacts or noise than classical algorithms by using the square function of the difference […]

Grover Adaptive Search Based Hybrid Benders Decomposition for Mixed-Integer Linear Programs

Abstract: Mixed-integer linear programs are widely used to model optimization problems involving both discrete and continuous variables, but remain computationally challenging due to the combinatorial complexity. To exploit the potential advantages of quantum computing in tackling the combinatorial optimization part, recent efforts have explored hybrid quantum-classical Benders decomposition frameworks, which delegate the discrete master problem […]