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The impressive progress in quantum hardware in the last years has raised the interest of the quantum computing community in harvesting the computational power of such devices. However, in the absence of error correction, these devices can…

Quantum Physics · Physics 2023-02-21 Giacomo De Palma , Milad Marvian , Cambyse Rouzé , Daniel Stilck França

Variational quantum algorithms are among the most promising approaches for simulating interacting quantum many-body systems on noisy intermediate-scale quantum (NISQ) devices. However, the practical success of variational quantum…

Quantum Physics · Physics 2026-01-01 Rudraksh Sharma

Finding the ground-state energy of molecules is an important and challenging computational problem for which quantum computing can potentially find efficient solutions. The variational quantum eigensolver (VQE) is a quantum algorithm that…

Quantum Physics · Physics 2023-02-15 Daniel Yoffe , Amir Natan , Adi Makmal

Variational quantum algorithms have found success in the NISQ era owing to their hybrid quantum-classical approach which mitigate the problems of noise in quantum computers. In our study we introduce the dynamic ansatz in the Variational…

Quantum Physics · Physics 2022-02-08 Hrushikesh Patil , Yulun Wang , Predrag Krstic

The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Despite a lot of empirical studies and recent progress in theoretical understanding…

Quantum Physics · Physics 2022-05-26 Xuchen You , Shouvanik Chakrabarti , Xiaodi Wu

State-of-the-art noisy intermediate-scale quantum devices (NISQ), although imperfect, enable computational tasks that are manifestly beyond the capabilities of modern classical supercomputers. However, present quantum computations are…

In 2017, John Preskill defined Noisy Intermediate Scale Quantum (NISQ) computers as an intermediate step on the road to large scale error corrected fault-tolerant quantum computers (FTQC). The NISQ regime corresponds to noisy qubit quantum…

Quantum Physics · Physics 2023-06-13 Olivier Ezratty

Adaptive variational quantum eigensolvers (ADAPT-VQEs) are promising candidates for simulations of strongly correlated systems on near-term quantum hardware. To further improve the noise resilience of these algorithms, recent efforts have…

The rapid progress of noisy intermediate-scale quantum (NISQ) computing underscores the need to test and evaluate new devices and applications. Quantum chemistry is a key application area for these devices, and therefore serves as an…

Noisy, intermediate-scale quantum computers come with intrinsic limitations in terms of the number of qubits (circuit "width") and decoherence time (circuit "depth") they can have. Here, for the first time, we demonstrate a recently…

Quantum Physics · Physics 2020-09-02 Thomas Ayral , François-Marie Le Régent , Zain Saleem , Yuri Alexeev , Martin Suchara

Quantum computers are devices, which allow more efficient solutions of problems as compared to their classical counterparts. As the timeline to developing a quantum-error corrected computer is unclear, the quantum computing community has…

Quantum Physics · Physics 2023-02-16 Marko J. Rančić

As a hybrid quantum-classical algorithm, the variational quantum eigensolver is widely applied in quantum chemistry simulations, especially in computing the electronic structure of complex molecular systems. However, on existing noisy…

Quantum Physics · Physics 2025-01-28 Wanqi Sun , Jungang Xu , Chenghua Duan

The Variational Quantum Eigensolver (VQE) is a promising algorithm for Noisy Intermediate Scale Quantum (NISQ) computation. Verification and validation of NISQ algorithms' performance on NISQ devices is an important task. We consider the…

Quantum Physics · Physics 2021-09-14 Kenneth Robbins , Peter J. Love

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse-level noise-adaptive compilation. However, limited research efforts…

Quantum Physics · Physics 2024-04-05 Hanrui Wang , Yongshan Ding , Jiaqi Gu , Zirui Li , Yujun Lin , David Z. Pan , Frederic T. Chong , Song Han

Recent studies to solve nuclear structure problems using quantum computers rely on a quantum algorithm known as Variational Quantum Eigensolver (VQE). In this study, we calculate the correlation energy in Helium-6 using VQE, with a…

Quantum Physics · Physics 2024-06-25 Ching-Hwa Wee , Meng-Hock Koh , Yung Szen Yap

Quantum machine learning has proven to be a fruitful area in which to search for potential applications of quantum computers. This is particularly true for those available in the near term, so called noisy intermediate-scale quantum (NISQ)…

Quantum Physics · Physics 2022-05-20 Brian Coyle

Resource-efficient, low-depth implementations of quantum circuits remain a promising strategy for achieving reliable and scalable computation on quantum hardware, as they reduce gate resources and limit the accumulation of noisy operations.…

The utility of effective model spaces in quantum simulations of non-relativistic quantum many-body systems is explored in the context of the Lipkin-Meshkov-Glick model of interacting fermions. We introduce an iterative…

Quantum Physics · Physics 2023-08-25 Caroline E. P. Robin , Martin J. Savage

Finding ground state energies on current quantum processing units (QPUs) using algorithms like the variational quantum eigensolver (VQE) continues to pose challenges. Hardware noise severely affects both the expressivity and trainability of…

Quantum Physics · Physics 2024-10-07 João C. Getelina , Prachi Sharma , Thomas Iadecola , Peter P. Orth , Yong-Xin Yao

The exact evaluation of the molecular ground state in quantum chemistry requires an exponentially increasing computational cost. Quantum computation is a promising way to overcome the exponential problem using polynomial-time quantum…

Quantum Physics · Physics 2023-05-30 Changsu Cao , Jiaqi Hu , Wengang Zhang , Xusheng Xu , Dechin Chen , Fan Yu , Jun Li , Hanshi Hu , Dingshun Lv , Man-Hong Yung