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Variational Quantum optimization algorithms, such as the Variational Quantum Eigensolver (VQE) or the Quantum Approximate Optimization Algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an…

Quantum Physics · Physics 2022-10-24 David Winderl , Nicola Franco , Jeanette Miriam Lorenz

The Variational Quantum Eigensolver (VQE) is one the most perspective algorithms for simulation of quantum many body physics that have recently attached a lot of attention and believed would be practical for implementation on the near term…

Quantum Physics · Physics 2021-05-03 Belozerova Polina , Shangareev Arthur , Zotov Yuriy , Yung Manhong , lv Dingshun

In this work, we present the integration of Qiskit Nature's quantum chemistry solvers into the Atomic Simulation Environment (ASE), enabling hybrid quantum-classical workflows for force-driven atomistic simulations. This coupling allows the…

Quantum Physics · Physics 2026-02-04 Wilke Dononelli

By design, the variational quantum eigensolver (VQE) strives to recover the lowest-energy eigenvalue of a given Hamiltonian by preparing quantum states guided by the variational principle. In practice, the prepared quantum state is…

Quantum Physics · Physics 2021-06-29 Daniel Claudino , Jerimiah Wright , Alexander J. McCaskey , Travis S. Humble

Variational quantum eigensolver (VQE) is a promising algorithm suitable for near-term quantum machines. VQE aims to approximate the lowest eigenvalue of an exponentially sized matrix in polynomial time. It minimizes quantum resource…

Accurate prediction of protein active-site structures remains a central challenge in structural biology, particularly for short and flexible peptide fragments where conventional and simulation-based methods often fail. Here, we present a…

We present a quantum information-inspired ansatz for the variational quantum eigensolver (VQE) and demonstrate its efficacy in calculating ground-state energies of atomic systems. Instead of adopting a heuristic approach, we start with an…

Quantum Physics · Physics 2025-08-19 Abdul Kalam , Prasenjit Deb , Akitada Sakurai , B. K. Sahoo , V. S. Prasannaa , B. P. Das

Quantum chemistry and materials is one of the most promising applications of quantum computing. Yet much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them. Most…

Solving electronic structure problems is considered one of the most promising applications of quantum computing. However, due to limitations imposed by the coherence time of qubits in the Noisy Intermediate Scale Quantum (NISQ) era or the…

Quantum Physics · Physics 2025-03-20 Shuo Sun , Chandan Kumar , Kevin Shen , Elvira Shishenina , Christian B. Mendl

We introduce a framework for the calculation of ground and excited state energies of bosonic systems suitable for near-term quantum devices and apply it to molecular vibrational anharmonic Hamiltonians. Our method supports generic reference…

Quantum Physics · Physics 2020-06-24 Pauline J. Ollitrault , Alberto Baiardi , Markus Reiher , Ivano Tavernelli

Electronic structure calculations of molecular systems are among the most promising applications for fault-tolerant quantum computing (FTQC) in quantum chemistry and drug design. However, while recent algorithmic advancements such as…

We introduce a variational quantum computing approach for quantum state reconstruction within a discretized logical framework, using experimental measurement data as input. By mapping the reconstruction cost function onto an Ising model,…

Quantum Physics · Physics 2026-04-03 Mwezi Koni , Shawal Kassim , Paola C. Obando , Neelan Gounden , Isaac Nape

In electronic structure theory, variational methods offer a valuable paradigm for approximating electronic ground states. However, for historical reasons, this principle is mostly restricted to model chemistries in pre-defined fixed basis…

Quantum Physics · Physics 2025-11-17 Fabian Langkabel , Stefan Knecht , Jakob S. Kottmann

Band structure is a cornerstone to understand electronic properties of materials. Accurate band structure calculations using a high-level quantum-chemistry theory can be computationally very expensive. It is promising to speed up such…

Quantum Physics · Physics 2021-09-13 Yi Fan , Jie Liu , Zhenyu Li , Jinlong Yang

Variational quantum eigensolvers (VQEs) are successful algorithms for studying physical systems on quantum computers. Recently, they were extended to the measurement-based model of quantum computing, bringing resource graph states and their…

Quantum Physics · Physics 2024-06-27 Albie Chan , Zheng Shi , Luca Dellantonio , Wolfgang Dür , Christine A. Muschik

We propose a variational quantum eigensolver (VQE) for the simulation of strongly-correlated quantum matter based on a multi-scale entanglement renormalization ansatz (MERA) and gradient-based optimization. This MERA quantum eigensolver can…

Quantum Physics · Physics 2023-09-04 Qiang Miao , Thomas Barthel

To obtain estimates of electronic energies, the Variational Quantum Eigensolver (VQE) technique performs separate measurements for multiple parts of the system Hamiltonian. Current quantum hardware is restricted to projective single-qubit…

Quantum Physics · Physics 2019-10-22 Artur F. Izmaylov , Tzu-Ching Yen , Robert A. Lang , Vladyslav Verteletskyi

Determining the properties of molecules and materials is one of the premier applications of quantum computing. A major question in the field is how to use imperfect near-term quantum computers to solve problems of practical value. Inspired…

The Variational Quantum Eigensolver (VQE) is a promising hybrid algorithm, utilizing both quantum and classical computers to obtain the ground state energy of molecules. In this context, this study applies VQE to investigate the ground…

Calculating excited states in chemistry is crucial to provide insight into photoinduced molecular behavior beyond the ground state, enabling innovations in spectroscopy, material sciences, and drug design. While several approaches have been…

Quantum Physics · Physics 2025-10-14 Akilan Rajamani , Martin Beseda , Benjamin Lasorne , Bruno Senjean