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Kohn-Sham Density Functional Theory (KS-DFT) provides the exact ground state energy and electron density of a molecule, contingent on the as-yet-unknown universal exchange-correlation (XC) functional. Recent research has demonstrated that…

Density Functional Theory (DFT) is widely used for atomistic simulations. However, its reach stays limited due to several limitations such as lack of accurate exchange-correlation functional, requirement of costly O(N 3) diagonalization…

Quantum Physics · Physics 2026-05-18 Namrata Manglani , Samrit Kumar Maity , Shashank Sharma , Soham Phulare , Sanjay Wandhekar

With the current rate of progress in quantum computing technologies, systems with more than 50 qubits will soon become reality. Computing ideal quantum state amplitudes for circuits of such and larger sizes is a fundamental step to assess…

An extension of the Variational Quantum Eigensolver (VQE) method is presented where a quantum computer generates an accurate exchange-correlation potential for a Density Functional Theory (DFT) simulation on classical hardware. The method…

Quantum Physics · Physics 2019-03-14 Ryan Hatcher , Jorge A. Kittl , Christopher Bowen

Simulating quantum circuits (QC) on high-performance computing (HPC) systems has become an essential method to benchmark algorithms and probe the potential of large-scale quantum computation despite the limitations of current quantum…

First-principles density functional theory (DFT) with plane wave (PW) basis set is the most widely used method in quantum mechanical material simulations due to its advantages in accuracy and universality. However, a perceived drawback of…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-04-08 Qingcai Jiang , Zhenwei Cao , Junshi Chen , Xinming Qin , Wei Hu , Hong An , Jinlong Yang

Dissipative collective effects are ubiquitous in quantum physics, and their relevance ranges from the study of entanglement in biological systems to noise mitigation in quantum computers. Here, we put forward the first fully quantum…

The implementation and practicality of quantum algorithms highly hinge on the quality of operations within a quantum processor. Therefore, including realistic error models in quantum computing simulation platforms is crucial for testing…

Quantum Physics · Physics 2021-04-12 Ahmed Abid Moueddene , Nader Khammassi , Koen Bertels , Carmen G. Almudever

It is one of the most fundamental objectives in quantum information science to understand the boundary between the computational power of classical and quantum computers. One possible avenue to explore this boundary is to identify classes…

Quantum Physics · Physics 2025-03-28 Christophe Piveteau

Near term quantum computers with a high quantity (around 50) and quality (around 0.995 fidelity for two-qubit gates) of qubits will approximately sample from certain probability distributions beyond the capabilities of known classical…

Quantum Physics · Physics 2018-01-23 Sergio Boixo , Sergei V. Isakov , Vadim N. Smelyanskiy , Hartmut Neven

Classical simulation of quantum computers is an irreplaceable step in the design of quantum algorithms. Exponential simulation costs demand the use of high-performance computing techniques, and in particular distribution, whereby the…

Quantum Physics · Physics 2023-11-06 Tyson Jones , Bálint Koczor , Simon C. Benjamin

We propose a new molecular simulation framework that combines the transferability, robustness and chemical flexibility of an ab initio method with the accuracy and efficiency of a machine learned force field. The key to achieve this mix is…

Computational Physics · Physics 2020-01-08 Sebastian Dick , Marivi Fernandez-Serra

Executing large quantum circuits is not feasible using the currently available NISQ (noisy intermediate-scale quantum) devices. The high costs of using real quantum devices make it further challenging to research and develop quantum…

Quantum Physics · Physics 2025-02-18 Kartikey Sarode , Daniel E. Huang , E. Wes Bethel

While quantum computers have shown significant promise for electronic structure calculations, their potential to accelerate density functional theory (DFT) calculations remains unclear. In this work, we present a qubit-efficient encoding…

Simulating the full dynamics of a quantum field theory over a wide range of energies requires exceptionally large quantum computing resources. Yet for many observables in particle physics, perturbative techniques are sufficient to…

High Energy Physics - Phenomenology · Physics 2021-12-01 Christian W. Bauer , Marat Freytsis , Benjamin Nachman

Quantum circuit simulators running on classical computers offer a vital platform for designing, testing, and optimizing quantum algorithms, driving innovation despite limited access to real quantum hardware. However, their scalability is…

Quantum Physics · Physics 2025-10-29 Gleb Kalachev , Pavel Mosharev , Zuoheng Zou , Pavel Panteleev , Man-Hong Yung

In this introductory article a brief description of Quantum Field Theories (QFT) is presented with emphasis on the distinction between strongly and weakly coupled theories. A case is made for using numerical simulations to solve QCD, the…

High Energy Physics - Lattice · Physics 2007-05-23 Rajan Gupta

In this paper, we propose a novel method using Deep Neural Networks (DNNs) to optimise the parameters of pulse waveforms used for manipulating qubit states, resulting in high fidelity implementation of qubit gates. High fidelity quantum…

Quantum Physics · Physics 2024-08-06 Zachary Fillingham , Hossein Nevisi , Shirin Dora

Density Functional Theory (DFT) accurately predicts the quantum chemical properties of molecules, but scales as $O(N_{\text{electrons}}^3)$. Sch\"utt et al. (2019) successfully approximate DFT 1000x faster with Neural Networks (NN).…

The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our…

Strongly Correlated Electrons · Physics 2021-09-29 Juan Carrasquilla , Di Luo , Felipe Pérez , Ashley Milsted , Bryan K. Clark , Maksims Volkovs , Leandro Aolita