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Quantum algorithms offer significant speedups over their classical counterparts for a variety of problems. The strongest arguments for this advantage are borne by algorithms for quantum search, quantum phase estimation, and Hamiltonian…

Quantum Physics · Physics 2021-12-14 John M. Martyn , Zane M. Rossi , Andrew K. Tan , Isaac L. Chuang

The Quantum Singular Value Transformation (QSVT) is a recent technique that gives a unified framework to describe most quantum algorithms discovered so far, and may lead to the development of novel quantum algorithms. In this paper we…

Quantum Physics · Physics 2024-01-05 Sevag Gharibian , François Le Gall

Quantum computing is powerful because unitary operators describing the time-evolution of a quantum system have exponential size in terms of the number of qubits present in the system. We develop a new "Singular value transformation"…

Quantum Physics · Physics 2020-02-21 András Gilyén , Yuan Su , Guang Hao Low , Nathan Wiebe

We study quantum speedups in quantum machine learning (QML) by analyzing the quantum singular value transformation (QSVT) framework. QSVT, introduced by [GSLW, STOC'19, arXiv:1806.01838], unifies all major types of quantum speedup; in…

Quantum Physics · Physics 2024-07-30 Ainesh Bakshi , Ewin Tang

We present a simplified exposition of some pieces of [Gily\'en, Su, Low, and Wiebe, STOC'19, arXiv:1806.01838], which introduced a quantum singular value transformation (QSVT) framework for applying polynomial functions to block-encoded…

Quantum Physics · Physics 2023-10-31 Ewin Tang , Kevin Tian

Block encoding is a key ingredient in the recently developed quantum singular value transformation (QSVT) framework, which provides a unifying description for many quantum algorithms. Initially introduced to simplify and optimize resource…

Quantum Physics · Physics 2025-04-01 Nhat A. Nghiem , Tzu-Chieh Wei

We introduce the first randomized algorithms for Quantum Singular Value Transformation (QSVT), a unifying framework for many quantum algorithms. Standard implementations of QSVT rely on block encodings of the Hamiltonian, which are costly…

We address the problem of solving a system of linear equations via the Quantum Singular Value Transformation (QSVT). One drawback of the QSVT algorithm is that it requires huge quantum resources if we want to achieve an acceptable accuracy.…

Quantum Physics · Physics 2026-03-20 Océane Koska , Marc Baboulin , Arnaud Gazda

This research extends quantum singular value transformation (QSVT) for general bounded operators embedded in unitary operators on possibly infinite-dimensional Hilbert spaces. Through in-depth mathematical exploration, we have achieved a…

Quantum Physics · Physics 2024-01-18 Chusei Kiumi , Akito Suzuki

In this paper, we introduce a major advancement in Quantum Singular Value Transformation (QSVT) through the development of Feedforward QSVT (FQSVT), a framework that significantly enhances the efficiency and robustness of quantum algorithm…

Quantum Physics · Physics 2024-08-16 Yulong Dong , Dong An , Murphy Yuezhen Niu

The quantum singular value transformation has revolutionised quantum algorithms. By applying a polynomial to an arbitrary matrix, it provides a unifying picture of quantum algorithms. However, polynomials are restricted to definite parity…

Quantum Physics · Physics 2023-12-04 Christoph Sünderhauf

Singular value thresholding (SVT) operation is a fundamental core module in many mathematical models in computer vision and machine learning, particularly for many nuclear norm minimizing-based problems. We presented a quantum SVT (QSVT)…

Quantum Physics · Physics 2019-01-23 Bojia Duan , Jiabin Yuan , Ying Liu , Dan Li

We develop new algorithms for Quantum Singular Value Transformation (QSVT), a unifying framework that encapsulates most known quantum algorithms and serves as the foundation for new ones. Existing implementations of QSVT rely on block…

This work presents a quantum algorithm for solving linear systems of equations of the form $\mathbf{A}{\frac{\mathbf{\partial f}}{\mathbf{\partial x}}} = \mathbf{B}\mathbf{f}$, based on the Quantum Singular Value Transformation (QSVT). The…

Quantum Physics · Physics 2025-07-18 Gal G. Shaviner , Ziv Chen , Steven H. Frankel

The Quantum Singular Value Transformation (QSVT) is a technique that provides a unified framework for describing many of the quantum algorithms discovered to date. We implement a noise-free simulation of the technique to investigate how it…

Quantum Physics · Physics 2023-07-26 Alexis Ralli , Gabriel Greene-Diniz , David Muñoz Ramo , Nathan Fitzpatrick

Quantum signal processing (QSP) and generalized quantum signal processing (GQSP) are essential tools for implementing the block encoding of matrix functions. The achievable polynomials of QSP have restrictions on parity, while GQSP…

Numerical Analysis · Mathematics 2026-04-20 Yu-Qiu Liu , Hefeng Wang , Hua Xiang

Many problems in linear algebra -- such as those arising from non-Hermitian physics and differential equations -- can be solved on a quantum computer by processing eigenvalues of the non-normal input matrices. However, the existing Quantum…

Quantum Physics · Physics 2026-03-27 Guang Hao Low , Yuan Su

Quantum singular value transformation (QSVT) enables the application of polynomial functions to the singular values of near arbitrary linear operators embedded in unitary transforms, and has been used to unify, simplify, and improve most…

Quantum Physics · Physics 2023-04-28 Zane M. Rossi , Victor M. Bastidas , William J. Munro , Isaac L. Chuang

Quantum signal processing (QSP) and the quantum singular value transformation (QSVT) are pivotal tools for simplifying the development of quantum algorithms. These techniques leverage polynomial transformations on the eigenvalues or…

Quantum Physics · Physics 2024-07-02 Lorenzo Laneve

Quantum signal processing and quantum singular value transformation are powerful tools to implement polynomial transformations of block-encoded matrices on quantum computers, and has achieved asymptotically optimal complexity in many…

Quantum Physics · Physics 2026-04-01 Xi Lu , Yuan Liu , Hongwei Lin
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