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Measurement-based quantum computing (MBQC) is a promising approach to reducing circuit depth in noisy intermediate-scale quantum algorithms such as the Variational Quantum Eigensolver (VQE). Unlike gate-based computing, MBQC employs local…

Quantum Physics · Physics 2023-12-21 Anna Schroeder , Matthias Heller , Mariami Gachechiladze

Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest. While general quantum simulation algorithms likely require error-corrected qubits, there may be applications of…

The Adaptive Derivative-Assembled Pseudo-Trotter Variational Quantum Eigensolver (ADAPT-VQE) has emerged as a pivotal promising approach for electronic structure challenges in quantum chemistry with noisy quantum devices. Nevertheless, to…

The variational quantum eigensolver (VQE) is one of the most promising quantum algorithms for the near-term noisy intermediate-scale quantum (NISQ) devices. The VQE typically involves finding the minimum energy of a quantum Hamiltonian…

Quantum Physics · Physics 2024-02-16 Qidong Xu , Kanav Setia

Establishing the nature of the ground state of the Heisenberg antiferromagnet (HAFM) on the kagome lattice is well known to be a prohibitively difficult problem for classical computers. Here, we give a detailed proposal for a Variational…

Quantum Physics · Physics 2022-12-26 Joris Kattemölle , Jasper van Wezel

Variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) computers. It is promising for quantum chemical calculations (QCC) because it can calculate the ground-state…

We present a new hybrid quantum-classical algorithm for optimizing unitary coupled-cluster (UCC) wave functions deemed the projective quantum eigensolver (PQE), amenable to near-term noisy quantum hardware. Contrary to variational quantum…

Quantum Physics · Physics 2021-08-10 Nicholas H. Stair , Francesco A. Evangelista

In the noisy-intermediate-scale-quantum era, Variational Quantum Eigensolver (VQE) is a promising method to study ground state properties in quantum chemistry, materials science, and condensed physics. However, general quantum eigensolvers…

Quantum Physics · Physics 2023-09-27 Yusen Wu , Zigeng Huang , Jinzhao Sun , Xiao Yuan , Jingbo B. Wang , Dingshun Lv

Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers by suppressing quantum noise with moderate resources. It is a key factor for successful and practical quantum algorithm implementations in the…

Quantum Physics · Physics 2023-08-28 Shi-Xin Zhang , Zhou-Quan Wan , Chang-Yu Hsieh , Hong Yao , Shengyu Zhang

Numerous methodologies have been proposed to implement imaginary time evolution (ITE) on quantum computers. Among these, variational ITE (VITE) methods for noisy intermediate-scale quantum (NISQ) computers have attracted much attention,…

Quantum Physics · Physics 2025-11-26 Hiroki Kuji , Tetsuro Nikuni , Yuta Shingu

In the current noisy intermediate scale quantum era of quantum computation, available hardware is severely limited by both qubit count and noise levels, precluding the application of many current hybrid quantum-classical algorithms to…

Quantum Physics · Physics 2024-03-05 Maria-Andreea Filip

In this study, we study the Variational Quantum Eigensolver (VQE) application for the Ising model as a test bed model, in which we pivotally delved into several optimization methods, both classical and quantum, and analyzed the quantum…

Quantum Physics · Physics 2025-04-21 Duc-Truyen Le , Vu-Linh Nguyen , Cong-Ha Nguyen , Quoc-Hung Nguyen , Van-Duy Nguyen

Despite the advantage quantum computers are expected to deliver when performing simulations compared to their classical counterparts, the current noisy intermediate-scale quantum (NISQ) devices remain limited in their capabilities. The…

Quantum Physics · Physics 2024-02-20 Clara Ferreira Cores , Kaur Kristjuhan , Mark Nicholas Jones

The variational quantum eigensolver (VQE), a variational algorithm to obtain an approximated ground state of a given Hamiltonian, is an appealing application of near-term quantum computers. The original work [A. Peruzzo et al.; \textit{Nat.…

Quantum Physics · Physics 2019-11-06 Ken M Nakanishi , Kosuke Mitarai , Keisuke Fujii

In this work, we explored and experimented with new forms of parameterized quantum circuits to be used as variational ansatzes for solving the bosonic and supersymmetric $SU(2)$ matrix models at different couplings using the Variational…

Quantum Physics · Physics 2025-07-02 H. L. Dao

Quantum simulation is one of the key applications of quantum computing, which accelerates research and development in the fields such as chemistry and material science. The recent development of noisy intermediate-scale quantum (NISQ)…

Quantum Amplitude Estimation (QAE) is a key primitive in quantum computing, but its standard implementation using Quantum Phase Estimation is resource-intensive, requiring a large number of coherent qubits in a single circuit block to…

Quantum Physics · Physics 2025-08-11 Alok Shukla , Prakash Vedula

Determination of molecular energetics and properties is one of the core challenges in the near-term quantum computing. To this end, hybrid quantum-classical algorithms are preferred for Noisy Intermediate Scale Quantum (NISQ) architectures.…

Quantum Physics · Physics 2024-10-22 Dibyendu Mondal , Chayan Patra , Dipanjali Halder , Rahul Maitra

We present and benchmark a type of variational quantum eigensolver (VQE), which we denote $\sigma$-VQE. It is designed to target mid-spectrum eigenstates and prepare quantum many-body scar states. The approach leverages the fact that noisy…

Quantum Physics · Physics 2026-05-18 Eoin Carolan , Nathan Keenan , Gabriele Cenedese , Giuliano Benenti

Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. Quantum computational algorithms have the potential to be an exciting new way of studying quantum cosmology. In quantum…

Quantum Physics · Physics 2019-12-03 Anirban Ganguly , Bikash K. Behera , Prasanta K. Panigrahi
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