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Simulation of quantum systems is challenging due to the exponential size of the state space. Tensor networks provide a systematically improvable approximation for quantum states. 2D tensor networks such as Projected Entangled Pair States…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-09-04 Yuchen Pang , Tianyi Hao , Annika Dugad , Yiqing Zhou , Edgar Solomonik

We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the tensor network, propagating their effect forward along the…

Quantum Physics · Physics 2025-10-09 Darren Pereira , Leonardo Banchi

Until fault-tolerance becomes implementable at scale, quantum computing will heavily rely on noise mitigation techniques. While methods such as zero noise extrapolation with probabilistic error amplification (ZNE-PEA) and probabilistic…

Quantum Physics · Physics 2023-07-25 Sergei Filippov , Matea Leahy , Matteo A. C. Rossi , Guillermo García-Pérez

Tensor networks establish an adaptable framework for the emulation of quantum circuits. By partitioning exponentially large registers and gates into smaller tensors, this unlocks fast transformations through tensor algebra, and grants fine…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-04-13 Jakub Adamski , Oliver Thomson Brown

We present an efficient tensor-network-based approach for simulating large-scale quantum circuits, demonstrated using Quantum Support Vector Machines (QSVMs). Our method effectively reduces exponential runtime growth to near-quadratic…

Recent work has shown that for one-dimensional quantum states that can be effectively approximated by matrix product operators (MPOs), a polynomial number of copies of the state suffices for reconstruction. Compared to MPOs in one…

Quantum Physics · Physics 2025-09-23 Zhen Qin , Zhihui Zhu

Although tensor networks are powerful tools for simulating low-dimensional quantum physics, tensor network algorithms are very computationally costly in higher spatial dimensions. We introduce quantum gauge networks: a different kind of…

Quantum Physics · Physics 2023-09-20 Kevin Slagle

Tensor network algorithms have proven to be very powerful tools for studying one- and two-dimensional quantum many-body systems. However, their application to three-dimensional (3D) quantum systems has so far been limited, mostly because…

Strongly Correlated Electrons · Physics 2021-05-26 Patrick C. G. Vlaar , Philippe Corboz

We investigate quantum algorithms derived from tensor networks to simulate the static and dynamic properties of quantum many-body systems. Using a sequentially prepared quantum circuit representation of a matrix product state (MPS) that we…

Quantum Physics · Physics 2024-12-04 Michael L. Wall , Aidan Reilly , John S. Van Dyke , Collin Broholm , Paraj Titum

Variational algorithms are a promising paradigm for utilizing near-term quantum devices for modeling electronic states of molecular systems. However, previous bounds on the measurement time required have suggested that the application of…

Projected entangled pair states (PEPS) offer memory-efficient representations of some quantum many-body states that obey an entanglement area law, and are the basis for classical simulations of ground states in two-dimensional (2d)…

We present a high-accuracy procedure for electronic structure calculations of strongly correlated materials. To address limitations in current electronic structure methods, we employ density functional theory in combination with the…

Designing superconducting quantum hardware requires simulation tools that can account for various deviations from ideal scenarios. This, in turn, requires approaches that automatically detect certain structures and leverage them to make the…

Quantum Physics · Physics 2026-05-28 Adrien Moulinas , Xavier Waintal

Classical simulations of quantum circuits play a vital role in the development of quantum computers and for taking the temperature of the field. Here, we classically simulate various physically-motivated circuits using 2D tensor network…

Quantum Physics · Physics 2025-09-16 Manuel S. Rudolph , Joseph Tindall

The constantly increasing dimensionality of artificial quantum systems demands for highly efficient methods for their characterization and benchmarking. Conventional quantum tomography fails for larger systems due to the exponential growth…

Quantum Physics · Physics 2023-09-04 Sergei S. Kuzmin , Varvara I. Mikhailova , Ivan V. Dyakonov , Stanislav S. Straupe

Determination and characterization of criticality in two-dimensional (2D) quantum many-body systems belong to the most important challenges and problems of quantum physics. In this paper we propose an efficient scheme to solve this problem…

Strongly Correlated Electrons · Physics 2017-04-19 Shi-Ju Ran , Cheng Peng , Wei Li , Maciej Lewenstein , Gang Su

Characterization of noise in current near-term quantum devices is of paramount importance to fully use their computational power. However, direct quantum process tomography becomes unfeasible for systems composed of tens of qubits. A…

Tensor networks such as matrix product states (MPS) and projected entangled pair states (PEPS) are commonly used to approximate quantum systems. These networks are optimized in methods such as DMRG or evolved by local operators. We provide…

Numerical Analysis · Mathematics 2020-01-07 Yifan Zhang , Edgar Solomonik

The simulation of lattice gauge theories with tensor network (TN) methods is becoming increasingly fruitful. The vision is that such methods will, eventually, be used to simulate theories in $(3+1)$ dimensions in regimes difficult for other…

High Energy Physics - Lattice · Physics 2017-09-21 Kai Zapp , Roman Orus

Large-scale tensor network simulations are crucial for developing robust complexity-theoretic bounds on classical quantum simulation, enabling circuit cutting approaches, and optimizing circuit compilation, all of which aid efficient…

Quantum Physics · Physics 2026-01-09 Aaron C. Hoyt , Jonathan S. Bersson , Sean Garner , Chenxu Liu , Ang Li
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