Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Scaling of contraction costs for entanglement renormalization algorithms including tensor Trotterization and variational Monte Carlo
Thomas Barthel, Q. Miao·Jul 30, 2024
The multi-scale entanglement renormalization ansatz (MERA) is a hierarchical class of tensor network states motivated by the real-space renormalization group. It is used to simulate strongly correlated quantum many-body systems. For prominent MERA st...
Strong coherent ion-electron coupling using a wire data bus
Baiyi Yu, R. Betzholz, Jianming Cai·Jul 29, 2024
Ion-ion coupling over long distances represents a highly useful resource for quantum technologies, for example, to sympathetically cool or interconnect qubits in ion-based quantum-computing architectures. In this respect, the recently demonstrated wi...
Effects of quadratic optomechanical coupling on bipartite entanglements, mechanical ground-state cooling, and mechanical quadrature squeezing in an electro-optomechanical system
N. Ghorbani, A. Motazedifard, M. Naderi·Jul 27, 2024
We theoretically investigate the steady-state bipartite entanglements, mechanical ground-state cooling, and mechanical quadrature squeezing in a hybrid electro-optomechanical system where a moving membrane is linearly coupled to the microwave field m...
Integration of Quantum Accelerators into HPC: Toward a Unified Quantum Platform
Amr Elsharkawy, Xiaorang Guo, Martin Schulz·Jul 26, 2024
To harness the power of quantum computing (QC) in the near future, tight and efficient integration of QC with high performance computing (HPC) infrastructure (both on the software (SW) and the hardware (HW) level) is crucial. This paper addresses the...
A Scalable Quantum Non-local Neural Network for Image Classification
Sparsh Gupta, Debanjan Konar, Vaneet Aggarwal·Jul 26, 2024
Non-local operations play a crucial role in computer vision enabling the capture of long-range dependencies through weighted sums of features across the input, surpassing the constraints of traditional convolution operations that focus solely on loca...
Quantum-inspired evolutionary algorithms for feature subset selection: a comprehensive survey
Yelleti Vivek, V. Ravi, P. R. Krishna·Jul 25, 2024
The clever hybridization of quantum computing concepts and evolutionary algorithms (EAs) resulted in a new field called quantum-inspired evolutionary algorithms (QIEAs). Unlike traditional EAs, QIEAs employ quantum bits to adopt a probabilistic repre...
The Questionable Influence of Entanglement in Quantum Optimisation Algorithms
Tobias Rohe, Daniëlle Schuman, Jonas Nusslein +3 more·Jul 24, 2024
The performance of the Variational Quantum Eigen-solver (VQE) is promising compared to other quantum algorithms, but also depends significantly on the appropriate design of the underlying quantum circuit. Recent research by Bowles, Ahmend & Schuld, 2...
Orbital-free density functional theory with first-quantized quantum subroutines
Yusuke Nishiya, Hirofumi Nishi, Taichi Kosugi +1 more·Jul 23, 2024
In this study, we propose a quantum-classical hybrid scheme for performing orbital-free density functional theory (OFDFT) using probabilistic imaginary-time evolution (PITE), designed for the era of fault-tolerant quantum computers (FTQC), as a mater...
Backdoor Attacks against Hybrid Classical-Quantum Neural Networks
Ji Guo, Wenbo Jiang, Rui Zhang +3 more·Jul 23, 2024
Hybrid Quantum Neural Networks (HQNNs) represent a promising advancement in Quantum Machine Learning (QML), yet their security has been rarely explored. In this paper, we present the first systematic study of backdoor attacks on HQNNs. We begin by pr...
Subspace-Based Local Compilation of Variational Quantum Circuits for Large-Scale Quantum Many-Body Simulation
S. Kanasugi, Yuichiro Hidaka, Yuya O. Nakagawa +5 more·Jul 19, 2024
Simulation of quantum many-body systems is a promising application of quantum computers. However, implementing the time-evolution operator as a quantum circuit efficiently on near-term devices with limited resources is challenging. Standard approache...
Conditional diffusion-based parameter generation for quantum approximate optimization algorithm
Fanxu Meng, Xiang-Yu Zhou, Pengcheng Zhu +1 more·Jul 17, 2024
The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm that shows promise in efficiently solving the Max-Cut problem, a representative example of combinatorial optimization. However, its effectiveness heavily de...
Walking through Hilbert Space with Quantum Computers.
Tong Jiang, Jinghong Zhang, Moritz K A Baumgarten +6 more·Jul 16, 2024
Computations of chemical systems' equilibrium properties and nonequilibrium dynamics have been suspected of being a "killer app" for quantum computers. This review highlights the recent advancements of quantum algorithms tackling complex sampling tas...
Quantum annealer accelerates the variational quantum eigensolver in a triple-hybrid algorithm
M. Jattana·Jul 16, 2024
Hybrid algorithms that combine quantum and classical resources have become commonplace in quantum computing. The variational quantum eigensolver (VQE) is routinely used to solve prototype problems. Currently, hybrid algorithms use no more than one ki...
Hybrid Oscillator-Qubit Quantum Processors: Instruction Set Architectures, Abstract Machine Models, and Applications
Yuan Liu, Shraddha Singh, Kevin C. Smith +11 more·Jul 15, 2024
Quantum computing with discrete variable (DV, qubit) hardware is approaching the large scales necessary for computations beyond the reach of classical computers. However, important use cases such as quantum simulations of physical models containing b...
A novel quantum realization of jet clustering in high-energy physics experiments.
Yongfeng Zhu, Weifeng Zhuang, Chen Qian +4 more·Jul 12, 2024
Exploring the application of quantum technologies to fundamental sciences holds the key to fostering innovation for both sides. In high-energy particle collisions, quarks and gluons are produced and immediately form collimated particle sprays known a...
Characterization and generation of a SQL-beating catlike state through repetitive measurements
Mamiko Tatsuta, Yuichiro Matsuzaki, Hiroki Kuji +2 more·Jul 9, 2024
Sensitivity in metrology without entanglement is limited by the standard quantum limit (SQL). Recent studies have found that the Heisenberg-limited scaling, the ultimate sensitivity in quantum metrology, can be achieved by generalized cat states, whi...
Quantum Approximate Optimization: A Computational Intelligence Perspective
Christo Meriwether Keller, S. Misra, Andreas Bartschi +1 more·Jul 9, 2024
Quantum computing is an emerging field on the multidisciplinary interface between physics, engineering, and computer science with the potential to make a large impact on computational intelligence (CI). The aim of this paper is to introduce quantum a...
Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection
David Bucher, Daniel Porawski, Benedikt Wimmer +5 more·Jul 9, 2024
Power grid partitioning is an important requirement for resilient distribution grids. Since electricity production is progressively shifted to the distribution side, dynamic identification of self-reliant grid subsets becomes crucial for operation. T...
BHT-QAOA: Generalizing Quantum Approximate Optimization Algorithm to Solve Arbitrary Boolean Problems as Hamiltonians
A. Al-Bayaty, M. Perkowski·Jul 9, 2024
A new methodology is proposed to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). Our methodology successfully finds all optimized approximated solutions for Boolean problems, after conver...
Improving the trainability of VQE on NISQ computers for solving portfolio optimization using convex interpolation
Shengbin Wang, Guihui Li, Zhimin Wang +5 more·Jul 8, 2024
Solving combinatorial optimization problems using variational quantum algorithms (VQAs) might be a promise application in the NISQ era. However, the limited trainability of VQAs could hinder their scalability to large problem sizes. In this paper, we...