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Matrix product state approximations to quantum states of low energy variance [PDF]
QuantumWe show how to efficiently simulate pure quantum states in one dimensional systems that have both finite energy density and vanishingly small energy fluctuations.
Kshiti Sneh Rai +2 more
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New Journal of PhysicsRunning quantum algorithms often involves implementing complex quantum circuits with such a large number of multi-qubit gates that the challenge of tackling practical applications appears daunting.
A Termanova +8 more
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Matrix-product-state compression of quantum-encoded vocal-tract transfer functions [PDF]
JASA Express LettersVocal-tract transfer functions are densely sampled acoustic representations, making direct quantum-state preparation costly. In this paper, we investigate whether their resonant structure permits compact quantum-compatible encoding.
Sheng Li, Jiyi Li
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Random matrix product state models of gravitationally prepared states
Journal of High Energy PhysicsGravitationally prepared states are quantum field theoretic states prepared by gravitational path integrals with spatial boundaries that have fixed boundary conditions for gravity but not for matter fields.
Sunghoon Jung +3 more
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Systematic construction of density functionals based on matrix product state computations
New Journal of Physics, 2016 We propose a systematic procedure for the approximation of density functionals in density functional theory that consists of two parts. First, for the efficient approximation of a general density functional, we introduce an efficient ansatz whose non ...
Michael Lubasch +5 more
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Simulation of Limited Entangled Quantum Fourier Transform Based on Matrix Product State [PDF]
Jisuanji kexueUnlike classical computing,qubits in quantum computing can be in the superposition state and entangled state can be formed between multiple qubits.Representing a quantum state composed of n qubits requires storing 2 to the nth power amplitudes.The ...
LIU Xiaonan, LIAN Demeng, DU Shuaiqi, LIU Zhengyu
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Quantum-Inspired Models for Classical Time Series
Machine Learning and Knowledge ExtractionWe present a model of classical binary time series derived from a matrix product state (MPS) Ansatz widely used in one-dimensional quantum systems. We discuss how this quantum Ansatz allows us to generate classical time series in a sequential manner. Our
Zoltán Udvarnoki, Gábor Fáth
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Efficient Description of Many-Body Systems with Matrix Product Density Operators
PRX Quantum, 2020 Matrix product states form a powerful ansatz for the simulation of a wide range of one-dimensional quantum systems that are in a pure state. Their power stems from the fact that they faithfully approximate states with a low amount of entanglement, the ...
Jiří Guth Jarkovský +3 more
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Planning with tensor networks based on active inference
Machine Learning: Science and TechnologyTensor networks (TNs) have seen an increase in applications in recent years. While they were originally developed to model many-body quantum systems, their usage has expanded into the field of machine learning.
Samuel T Wauthier +3 more
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Quantum Gaussian process state: A kernel-inspired state with quantum support data
Physical Review Research, 2022 We introduce the quantum Gaussian process state, motivated via a statistical inference for the wave function supported by a data set of unentangled product states.
Yannic Rath, George H. Booth
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