Quantum Brain

Papers

Live trends in quantum computing research, updated daily from arXiv.

Total Papers

27,749

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1,196

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Research Volume

13,047 papers in 12 months (-2% vs prior quarter)

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Papers by research theme (12 months). Hover for details.

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2,470 papers found

Equivalence Checking of Parameterized Quantum Circuits: Verifying the Compilation of Variational Quantum Algorithms

Tom Peham, Lukas Burgholzer, R. Wille·Oct 21, 2022

Variational quantum algorithms have been introduced as a promising class of quantum-classical hybrid algorithms that can already be used with the noisy quantum computing hardware available today by employing parameterized quantum circuits. Considerin...

Computer SciencePhysics

Coherence requirements for quantum communication from hybrid circuit dynamics

S. Kelly, U. Poschinger, F. Schmidt-Kaler +2 more·Oct 20, 2022

The coherent superposition of quantum states is an important resource for quantum information processing which distinguishes quantum dynamics and information from their classical counterparts. In this article we determine the coherence requirements t...

Physics

Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning

Giuseppe Scriva, Emanuele Costa, B. McNaughton +1 more·Oct 20, 2022

Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems. In this article, we show how to exploit them to accelerate equilibrium Markov chain Mo...

Physics

Quantum Machine Learning using the ZXW-Calculus

Mark Koch·Oct 18, 2022

The field of quantum machine learning (QML) explores how quantum computers can be used to more efficiently solve machine learning problems. As an application of hybrid quantum-classical algorithms, it promises a potential quantum advantages in the ne...

Computer SciencePhysics

Ancilla-driven blind quantum computation for clients with different quantum capabilities

Qunfeng Dai, Junyu Quan, X. Lou +1 more·Oct 18, 2022

Blind quantum computation (BQC) allows a client with limited quantum power to delegate his quantum computational task to a powerful server and still keep his input, output, and algorithm private. There are mainly two kinds of models about BQC, namely...

Physics

A modular quantum-classical framework for simulating chemical reaction pathways accurately

R. NirmalM., Shampa Sarkar, M. Nambiar +1 more·Oct 17, 2022

A lot of progress has been made in recent times for simulating accurately the ground state energy of small molecules and their potential energy surface, using quantum-classical hybrid computing architecture. While these single point energy calculatio...

Physics

Fully and partially distributed Quantum Generalized Benders Decomposition for Unit Commitment Problems

Fang Gao, Dejian Huang, Ziwei Zhao +3 more·Oct 13, 2022

A series of hybrid quantum-classical generalized Benders decomposition (GBD) algorithms are proposed to address unit commitment (UC) problems under centralized, distributed, and partially distributed frameworks. In the centralized approach, the quant...

Physics

Quantum-classical tradeoffs and multi-controlled quantum gate decompositions in variational algorithms

T. Tomesh, Nicholas Allen, Zain Saleem·Oct 10, 2022

The computational capabilities of near-term quantum computers are limited by the noisy execution of gate operations and a limited number of physical qubits. Hybrid variational algorithms are well-suited to near-term quantum devices because they allow...

Computer SciencePhysics

Time evolution of uniform sequential circuits

Nikita Astrakhantsev, Sheng-Hsuan Lin, F. Pollmann +1 more·Oct 7, 2022

Simulating time evolution of generic quantum many-body systems using classical numerical approaches has an exponentially growing cost either with evolution time or with the system size. In this work, we present a polynomially scaling hybrid quantum-c...

Physics

Bayesian autotuning of Hubbard model quantum simulators

L. Szulakowska, J. Dai·Oct 6, 2022

Spins in gated semiconductor quantum dots (QDs) are a promising platform for Hubbard model simulation inaccessible to computation. Precise control of the tunnel couplings by tuning voltages on metallic gates is vital for a successful QD-based simulat...

Physics

Quantum simulation of preferred tautomeric state prediction

Yu Shee, Tzu-Lan Yeh, Jeng-Yueh Hsiao +3 more·Oct 6, 2022

Prediction of tautomers plays an essential role in computer-aided drug discovery. However, it remains a challenging task nowadays to accurately predict the canonical tautomeric form of a given drug-like molecule. Lack of extensive tautomer databases,...

Physics

Hybrid Quantum Classical Simulations

D. Willsch, M. Jattana, M. Willsch +4 more·Oct 6, 2022

We report on two major hybrid applications of quantum computing, namely, the quantum approximate optimisation algorithm (QAOA) and the variational quantum eigensolver (VQE). Both are hybrid quantum classical algorithms as they require incremental com...

Physics

An Interface for Variational Quantum Eigensolver based Energy (VQE-E) and Force (VQE-F) Calculator to Atomic Simulation Environment (ASE)

R. NirmalM., Shampa Sarkar, M. Nambiar·Sep 28, 2022

The development of quantum algorithms to solve quantum chemistry problems has offered a promising new paradigm of performing computer simulations at the scale of atoms and molecules. Although majority of the research so far has focused on designing q...

Computer SciencePhysics

Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach

Daniel T. Chang·Sep 28, 2022

Quantum machine learning (QML) is the use of quantum computing for the computation of machine learning algorithms. With the prevalence and importance of classical data, a hybrid quantum-classical approach to QML is called for. Parameterized Quantum C...

Computer SciencePhysics

quEEGNet: Quantum AI for Biosignal Processing

T. Koike-Akino, Ye Wang·Sep 27, 2022

In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates...

Computer SciencePhysicsEngineering

Evaluating the impact of noise on the performance of the Variational Quantum Eigensolver

Marita Oliv, A. Matic, Thomas Messerer +1 more·Sep 26, 2022

Quantum computers are expected to be highly beneficial for chemistry simulations, promising significant improvements in accuracy and speed. The most prominent algorithm for chemistry simulations on NISQ devices is the Variational Quantum Eigensolver (V...

Physics

Magnetic phases of spatially modulated spin-1 chains in Rydberg excitons: Classical and quantum simulations.

M. Sajjan, H. Alaeian, S. Kais·Sep 23, 2022

In this work, we study the magnetic phases of a spatially modulated chain of spin-1 Rydberg excitons. Using the Density Matrix Renormalization Group (DMRG) technique, we study various magnetic and topologically nontrivial phases using both single-par...

MedicinePhysics

Robust and Secure Hybrid Quantum-Classical Computation on Untrusted Cloud-Based Quantum Hardware

S. Upadhyay, Swaroop Ghosh·Sep 23, 2022

Quantum computers are currently accessible through a cloud-based platform that allows users to run their programs on a suite of quantum hardware. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect more comp...

Computer SciencePhysics

Iterative Qubits Management for Quantum Index Searching in a Hybrid System

W. Mu, Y. Mao, Long Cheng +3 more·Sep 22, 2022

Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to dep...

Computer SciencePhysics

Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines

M. Schenk, E. Combarro, M. Grossi +4 more·Sep 22, 2022

Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-learning with the restriction, however, to discrete state-action space en...

Physics
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