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Digital quantum simulation has broad applications in approximating unitary evolution of Hamiltonians. In practice, many simulation tasks for quantum systems focus on quantum states in the low-energy subspace instead of the entire Hilbert…

Quantum Physics · Physics 2024-07-16 Weiyuan Gong , Shuo Zhou , Tongyang Li

The utility of effective model spaces in quantum simulations of non-relativistic quantum many-body systems is explored in the context of the Lipkin-Meshkov-Glick model of interacting fermions. We introduce an iterative…

Quantum Physics · Physics 2023-08-25 Caroline E. P. Robin , Martin J. Savage

We solve a problem, which while not fitting into the usual paradigm, can be viewed as a quantum computation. Suppose we are given a quantum system described by an N dimensional Hilbert space with a Hamiltonian of the form $E |w >< w|$ where…

Quantum Physics · Physics 2007-05-23 Edward Farhi , Sam Gutmann

Efficient simulation of interacting fermionic systems is a key application of near-term quantum computers, but is hindered by the overhead required to encode fermionic operators on qubit hardware. Here, we consider models with $N$ fermionic…

Quantum Physics · Physics 2026-04-30 Reinis Irmejs , J. Ignacio Cirac

The physics of quantum mechanics is the inspiration for, and underlies, quantum computation. As such, one expects physical intuition to be highly influential in the understanding and design of many quantum algorithms, particularly…

Quantum Physics · Physics 2017-01-06 Guang Hao Low , Isaac L. Chuang

The aim of this paper is to develop novel quantum algorithms for Gaussian process quadrature methods. Gaussian process quadratures are numerical integration methods where Gaussian processes are used as functional priors for the integrands…

Computation · Statistics 2025-02-21 Cristian A. Galvis-Florez , Ahmad Farooq , Simo Särkkä

Quantum computers can outperform classical computers in certain tasks. However, there are still many challenges to the current quantum computers such as decoherence and fault tolerance, and other drawbacks such as portability and…

Quantum Physics · Physics 2022-11-08 Sharan Mourya

In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of the widely studied problem of learning Gaussian graphical models. We obtain efficient protocols,…

Quantum Physics · Physics 2025-04-08 Marco Fanizza , Cambyse Rouzé , Daniel Stilck França

We construct a new model of the quantum oscillator, whose energy spectrum is equally-spaced and lower-bounded, whereas the spectra of position and momentum are a denumerable non-degenerate set of points in [-1,1] that depends on the…

Mathematical Physics · Physics 2009-11-13 Natig M. Atakishiyev , Anatoliy U. Klimyk , Kurt Bernardo Wolf

The adiabatic quantum algorithm has drawn intense interest as a potential approach to accelerating optimization tasks using quantum computation. The algorithm is most naturally realised in systems which support Hamiltonian evolution, rather…

Quantum Physics · Physics 2019-10-02 Liming Zhao , Carlos A. Perez-Delgado , Simon C. Benjamin , Joseph F. Fitzsimons

We present an efficient method for estimating the eigenvalues of a Hamiltonian $H$ from the expectation values of the evolution operator for various times. For a given quantum state $\rho$, our method outputs a list of eigenvalue estimates…

Quantum Physics · Physics 2020-09-08 Rolando D. Somma

We present an algorithm for measurement of $k$-local operators in a quantum state, which scales logarithmically both in the system size and the output accuracy. The key ingredients of the algorithm are a digital representation of the…

Quantum Physics · Physics 2018-10-16 Apoorva Patel , Anjani Priyadarsini

Parameterized quantum circuits are a promising technology for achieving a quantum advantage. An important application is the variational simulation of time evolution of quantum systems. To make the most of quantum hardware, variational…

Quantum Physics · Physics 2021-07-28 Marcello Benedetti , Mattia Fiorentini , Michael Lubasch

The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are relevant to many important problems in physics and chemistry. In the low temperature regime, algorithms for these tasks often suffer from intractabilities, for…

We study quantum anomaly detection with density estimation and multivariate Gaussian distribution. Both algorithms are constructed using the standard gate-based model of quantum computing. Compared with the corresponding classical…

Quantum Physics · Physics 2019-06-26 Jin-Ming Liang , Shu-Qian Shen , Ming Li , Lei Li

We introduce an efficient stable algorithm for transforms associated with expansions in Hermite functions interpolated at Hermite polynomial roots. The Hermite transform matrix can be factorised into a diagonal component and an orthogonal…

Numerical Analysis · Mathematics 2026-05-07 Marcus Webb , Georg Maierhofer

We analyze the possibility of modifying the original Farhi-Gutmann Hamiltonian algorithm in order to speed up the procedure for producing a suitably distributed unknown normalized quantum mechanical state. Such a modification is feasible…

Quantum Physics · Physics 2019-09-04 Carlo Cafaro , Paul M. Alsing

We introduce novel algorithms for the quantum simulation of molecular systems which are asymptotically more efficient than those based on the Trotter-Suzuki decomposition. We present the first application of a recently developed technique…

An algorithm to classify a general Hermitian matrix according to its signature (positive semi-definite, negative or indefinite) is presented. It builds on the Quantum Phase Estimation algorithm, which stores the sign of the eigenvalues of a…

Quantum Physics · Physics 2022-11-28 Andrés Gómez , Javier Mas

Simulating the unitary dynamics of a quantum system is a fundamental problem of quantum mechanics, in which quantum computers are believed to have significant advantage over their classical counterparts. One prominent such instance is the…

Quantum Physics · Physics 2024-09-04 John M. Martyn , Yuan Liu , Zachary E. Chin , Isaac L. Chuang
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