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This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining projective measurements with conditional feedback, the protocol…

Quantum Physics · Physics 2025-07-16 Daniel Alcalde Puente , Matteo Rizzi

Finding the ground state of a Hamiltonian system is of great significance in many-body quantum physics and quantum chemistry. We propose an improved iterative quantum algorithm to prepare the ground state of a Hamiltonian. The crucial point…

Quantum Physics · Physics 2022-10-25 Jin-Min Liang , Qiao-Qiao Lv , Shu-Qian Shen , Ming Li , Zhi-Xi Wang , Shao-Ming Fei

Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propose a quantum circuit learning algorithm that can be used to…

Emerging quantum hardware provides new possibilities for quantum simulation. While much of the research has focused on simulating closed quantum systems, the real-world quantum systems are mostly open. Therefore, it is essential to develop…

Quantum Physics · Physics 2024-02-14 Huo Chen , Niladri Gomes , Siyuan Niu , Wibe Albert de Jong

One of the key applications for the emerging quantum simulators is to emulate the ground state of many-body systems, as it is of great interest in various fields from condensed matter physics to material science. Traditionally, in an analog…

Quantum Physics · Physics 2020-09-18 Chufan Lyu , Victor Montenegro , Abolfazl Bayat

Long-range entanglement is an important quantum resource, particularly for topological orders and quantum error correction. In reality, preparing long-range entangled states requires a deep unitary circuit, which poses significant…

Quantum Physics · Physics 2025-05-29 Yuxuan Yan , Muzhou Ma , You Zhou , Xiongfeng Ma

Motivated by far-reaching applications ranging from quantum simulations of complex processes in physics and chemistry to quantum information processing, a broad effort is currently underway to build large-scale programmable quantum systems.…

We introduce an efficient and versatile quantum teleportation protocol for specific types of n-qubit entangled states. By employing a partially entangled Greenberger-Horne-Zeilinger (GHZ) state as the quantum channel and an optimal Positive…

Current noise levels in physical realizations of qubits and quantum operations limit the applicability of conventional methods to characterize entanglement. In this adverse scenario, we follow a quantum variational approach to estimate the…

Quantum Physics · Physics 2022-09-16 A. D. Muñoz-Moller , L. Pereira , L. Zambrano , J. Cortés-Vega , A. Delgado

The investigation of the behavior of both classical and quantum systems on non-Euclidean surfaces near the phase transition point represents an interesting research area of modern physics. In the case of classical spin systems, a…

Statistical Mechanics · Physics 2020-03-30 Michal Daniška , Andrej Gendiar

Quantum state preparation is an important class of quantum algorithms that is employed as a black-box subroutine in many algorithms, or used by itself to generate arbitrary probability distributions. We present a novel state preparation…

Quantum Physics · Physics 2020-06-02 Yutaro Iiyama

Graph states are an important class of multipartite entangled states. Previous experimental generation of graph states and in particular the Greenberger-Horne-Zeilinger (GHZ) states in linear optics quantum information schemes is subjected…

Quantum Physics · Physics 2022-03-01 Sheng Zhang , Yu-Kai Wu , Chang Li , Nan Jiang , Yun-Fei Pu , Lu-Ming Duan

We present a quantum-classical hybrid algorithm that simulates electronic structures of periodic systems such as ground states and quasiparticle band structures. By extending the unitary coupled cluster (UCC) theory to describe crystals in…

Quantum Physics · Physics 2022-02-16 Nobuyuki Yoshioka , Takeshi Sato , Yuya O. Nakagawa , Yu-ya Ohnishi , Wataru Mizukami

Quantum state preparation is a fundamental component of quantum algorithms, particularly in quantum machine learning and data processing, where classical data must be encoded efficiently into quantum states. Existing amplitude encoding…

Quantum Physics · Physics 2026-02-11 Carsten Blank , Israel F. Araujo

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for current and near-term quantum devices. Despite its initial success, there is a lack of understanding involving several of its key aspects. There…

Quantum Physics · Physics 2023-03-22 Manpreet Singh Jattana , Fengping Jin , Hans De Raedt , Kristel Michielsen

Quantum state tomography (QST) is plagued by the ``curse of dimensionality'' due to the exponentially-scaled complexity in measurement and data post-processing. Efficient QST schemes for large-scale mixed states are currently missing. In…

Quantum Physics · Physics 2023-08-15 Wen-jun Li , Kai Xu , Heng Fan , Shi-ju Ran , Gang Su

Quantum computing offers potential solutions for finding ground states in condensed-matter physics and chemistry. However, achieving effective ground state preparation is also computationally hard for arbitrary Hamiltonians. It is necessary…

The conventional method for generating entangled states in qubit systems relies on applying precise two-qubit entangling gates alongside single-qubit rotations. However, achieving high-fidelity entanglement demands high accuracy in…

Fault-tolerant quantum computations require alternating quantum and classical computations, where the classical computations prove vital in detecting and correcting errors in the quantum computation. Recently, interest in using these…

Quantum Physics · Physics 2025-09-09 Niels M. P. Neumann

We propose a quantum-classical hybrid algorithm to simulate the non-equilibrium steady state of an open quantum many-body system, named the dissipative-system Variational Quantum Eigensolver (dVQE). To employ the variational optimization…

Quantum Physics · Physics 2020-12-07 Nobuyuki Yoshioka , Yuya O. Nakagawa , Kosuke Mitarai , Keisuke Fujii