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Two dimensional tensor networks such as projected entangled pairs states (PEPS) are generally hard to contract. This is arguably the main reason why variational tensor network methods in 2D are still not as successful as in 1D. However,…

Quantum Physics · Physics 2016-12-07 Anurag Anshu , Itai Arad , Aditya Jain

Preparation of quantum state lies at the heart of quantum information processing. The greedy algorithm provides a potential method to effectively prepare quantum states. However, the standard greedy algorithm, in general, cannot take the…

Quantum Physics · Physics 2022-07-12 Run-Hong He , Hai-Da Liu , Sheng-Bin Wang , Jing Wu , Shen-Shuang Nie , Zhao-Ming Wang

We propose a quantum algorithm for many-body state preparation. It is especially suited for injective PEPS and thermal states of local commuting Hamiltonians on a lattice. We show that for a uniform gap and sufficiently smooth paths, an…

Quantum Physics · Physics 2016-03-08 Yimin Ge , András Molnár , J. Ignacio Cirac

We present a probabilistic quantum algorithm for preparing mixed states which, in expectation, are proportional to the solutions of Lyapunov equations -- linear matrix equations ubiquitous in the analysis of classical and quantum dynamical…

Quantum Physics · Physics 2026-04-20 Marcello Benedetti , Ansis Rosmanis , Matthias Rosenkranz

We present a protocol for generating multiqubit quantum states with translationally invariant pairwise entanglement. Our approach is tailored for digital quantum computers with restricted qubit connectivity, a common limitation in…

Quantum Physics · Physics 2025-08-29 Francisco Albarrán-Arriagada , Guillermo Romero , Juan Carlos Retamal

We present a scheme to prepare quantum correlated states of two mechanical systems based on the pouring of pre-available all-optical entanglement into the state of two micro-mirrors belonging to remote and non-interacting optomechanical…

Quantum Physics · Physics 2015-05-27 L. Mazzola , M. Paternostro

We present a scheme of remote preparation of the two-particle state by using two Einstein-Podolsky-Rosen pairs or two partial entangled two-particle states as the quantum channel. The probability of the successful remote state preparation…

Quantum Physics · Physics 2009-03-10 Yan Feng-Li , Zhang Guo-Hua

Recently a scheme has been proposed for constructing quantum error-correcting codes that embed a finite-dimensional code space in the infinite-dimensional Hilbert space of a system described by continuous quantum variables. One of the…

Quantum Physics · Physics 2009-11-07 B. C. Travaglione , G. J. Milburn

Quantum reservoir computing is a machine-learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided…

Quantum Physics · Physics 2023-03-29 Pere Mujal

In this work we provide a method for generating quantum circuits preparing maximally multipartite entangled states using genetic programming. The presented method is faster that known realisations thanks to the applied fitness function and…

Quantum Physics · Physics 2016-10-27 Przemysław Sadowski

A typical quantum state obeying the area law for entanglement on an infinite 2D lattice can be represented by a tensor network ansatz -- known as an infinite projected entangled pair state (iPEPS) -- with a finite bond dimension $D$. Its…

Strongly Correlated Electrons · Physics 2018-07-11 Piotr Czarnik , Jacek Dziarmaga

Preparing matrix product states (MPSs) on quantum computers is an essential routine in the simulation of many-body physics. However, widely-used schemes based on staircase circuits are often too deep to execute on current hardware. Here we…

The study of tensor network theory is an important field and promises a wide range of experimental and quantum information theoretical applications. Matrix product state is the most well-known example of tensor network states, which…

Quantum Physics · Physics 2019-04-30 Amandeep Singh Bhatia , Mandeep Kaur Saggi

Calculation of observables with three-dimensional projected entangled pair states is generally hard, as it requires a contraction of complex multi-layer tensor networks. We utilize the multi-layer structure of these tensor networks to…

Strongly Correlated Electrons · Physics 2024-08-21 Illia Lukin , Andrii Sotnikov

We present a protocol to store a polynomial number of arbitrary bit strings, encoded as spin configurations, in the approximately degenerate low-energy manifold of an all-to-all connected Ising spin glass. The iterative protocol is inspired…

Quantum Physics · Physics 2019-03-29 Clemens Dlaska , Lukas M. Sieberer , Wolfgang Lechner

We consider the preparation of matrix product states (MPS) on quantum devices via quantum circuits of local gates. We first prove that faithfully preparing translation-invariant normal MPS of $N$ sites requires a circuit depth…

Quantum Physics · Physics 2024-01-26 Daniel Malz , Georgios Styliaris , Zhi-Yuan Wei , J. Ignacio Cirac

Quantum Phase Estimation (QPE), the quantum algorithm for estimating eigenvalues of a given Hermitian matrix and preparing its eigenvectors, is considered the most promising approach to finding the ground states and their energies of…

We introduce an efficient algorithm for the systematic design of shallow-depth quantum circuits capable of preparing many-body quantum states represented as Matrix Product States (MPS). The proposed method leverages Schmidt spectrum…

Quantum Physics · Physics 2025-12-24 Josh Green , Joshua Snow , Jingbo B Wang

We present an efficient quantum algorithm for preparing a pure state on a quantum computer, where the quantum state corresponds to that of a molecular system with a given number $m$ of electrons occupying a given number $n$ of spin…

Quantum Physics · Physics 2009-05-01 Hefeng Wang , S. Ashhab , Franco Nori

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that…