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Transcorrelated methods provide an efficient way of partially transferring the description of electronic correlations from the ground state wavefunction directly into the underlying Hamiltonian. In particular, Dobrautz et al. [Phys. Rev. B,…

Quantum Physics · Physics 2023-03-03 Igor O. Sokolov , Werner Dobrautz , Hongjun Luo , Ali Alavi , Ivano Tavernelli

Dynamic quantum circuits combine mid-circuit measurement with classical feed-forward, enabling circuit constructions with reduced entangling-gate depth. Here, we investigate their use in Quantum Imaginary Time Evolution (QITE), where…

Quantum Physics · Physics 2026-03-06 Albert Lund , Erika Magnusson , Werner Dobrautz , Laura García-Álvarez

We consider a quantum mechanical system represented in phase space (referred to hereafter as "Wigner space"), coupled to a harmonic oscillator bath. We derive quantum hierarchal Fokker-Planck (QHFP) equations not only in real time, but also…

Statistical Mechanics · Physics 2015-04-16 Yoshitaka Tanimura

Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems…

The time evolution operator plays a crucial role in the precise computation of chemical experiments on quantum computers and holds immense promise for advancing the fields of physical and computer sciences, with applications spanning…

The current generation of noisy intermediate scale quantum computers introduces new opportunities to study quantum many-body systems. In this paper, we show that quantum circuits can provide a dramatically more efficient representation than…

Quantum Physics · Physics 2021-03-24 Sheng-Hsuan Lin , Rohit Dilip , Andrew G. Green , Adam Smith , Frank Pollmann

The field of artificial neural networks is expected to strongly benefit from recent developments of quantum computers. In particular, quantum machine learning, a class of quantum algorithms which exploit qubits for creating trainable neural…

Quantum Physics · Physics 2022-01-12 Marco Maronese , Claudio Destri , Enrico Prati

Near term quantum computers suffer from a degree of decoherence which is prohibitive for high fidelity simulations with deep circuits. An economical use of circuit depth is therefore paramount. For digital quantum simulation of quantum…

Strongly Correlated Electrons · Physics 2024-05-21 Maurits S. J. Tepaske , Dominik Hahn , David J. Luitz

A quantum algorithm for simulating multidimensional scalar transport problems using a time-marching strategy is presented. A direct unitary block encoding of the explicit time-marching operator is constructed, resulting in the intrinsic…

Quantum Physics · Physics 2025-07-09 Paul Over , Sergio Bengoechea , Peter Brearley , Sylvain Laizet , Thomas Rung

The infinite time-evolving block decimation (iTEBD) algorithm [Phys. Rev. Lett. 98, 070201 (2007)] allows to simulate unitary evolution and to compute the ground state of one-dimensional quantum lattice systems in the thermodynamic limit.…

Statistical Mechanics · Physics 2009-11-13 Roman Orus , Guifre Vidal

We develop two cutting-edge approaches to construct deep neural networks representing the purified finite-temperature states of quantum many-body systems. Both methods commonly aim to represent the Gibbs state by a highly expressive…

Strongly Correlated Electrons · Physics 2021-08-10 Yusuke Nomura , Nobuyuki Yoshioka , Franco Nori

The Fermi-Hubbard model is a fundamental model in condensed matter physics that describes strongly correlated electrons. On the other hand, quantum computers are emerging as powerful tools for exploring the complex dynamics of these quantum…

Quantum Physics · Physics 2026-05-27 Talal Ahmed Chowdhury , Vladimir Korepin , Vincent R. Pascuzzi , Kwangmin Yu

The growing computational demands of classical neural networks have intensified the search for energy-efficient and powerful computational alternatives. Quantum neural networks (QNNs) implemented on integrated photonic platforms offer a…

The accurate computation of Hamiltonian ground, excited, and thermal states on quantum computers stands to impact many problems in the physical and computer sciences, from quantum simulation to machine learning. Given the challenges posed…

Developing scalable quantum algorithms to study finite-temperature physics of quantum many-body systems has attracted considerable interest due to recent advancements in quantum hardware. However, such algorithms in their present form…

Imaginary time evolution is a powerful technique for computing the ground state of quantum Hamiltonians, where the convergence to ground state in asymptotic imaginary time is guaranteed. However, implementing this method on quantum…

Quantum Physics · Physics 2025-06-17 S. Alipour , T. Ojanen

We propose a hybrid quantum-classical algorithm for solving QUBO problems using an Imaginary Time Evolution-Mimicking Circuit (ITEMC). The circuit parameters are optimized to closely mimic imaginary time evolution, using only single- and…

Quantum Physics · Physics 2025-06-19 Yahui Chai , Alice Di Tucci

The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the many-body wave functions with high complexity. Quantum neural network provides a…

Quantum Physics · Physics 2020-09-01 Yusen Wu , Chunyan Wei , Sujuan Qin , Qiaoyan Wen , Fei Gao

A successful quantum error correction protocol would allow quantum computers to run algorithms without suffering from the effects of noise. However, fully fault-tolerant quantum error correction is too resource intensive for existing…

Quantum Physics · Physics 2024-07-29 Chris N. Self , Marcello Benedetti , David Amaro

We develop a unified spectral-semigroup framework that connects real-time and imaginary-time quantum dynamics through analytic continuation. Within this formulation, evolution is expressed as an exponential reweighting of spectral…

Applied Physics · Physics 2026-05-12 Pengfei Zhu