Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
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...
Erasing Doppler dephasing error in Rydberg quantum gates
Rui Li, Jing Qian, Weiping Zhang·Jul 9, 2024
The Doppler dephasing error due to residual thermal motion of atoms is a major cause of infidelity in neutral-atom quantum gates. Besides cooling and trapping advancements, few effective methods exist to mitigate this error. In the present work, we f...
Low-cost noise reduction for Clifford circuits
Nicolas Delfosse, Edwin Tham·Jul 9, 2024
We propose a Clifford noise reduction (CliNR) scheme that provides a reduction of the logical error rate of Clifford circuit with lower overhead than error correction and without the exponential sampling overhead of error mitigation. CliNR implements...
Advantages of multistage quantum walks over QAOA
Lasse Gerblich, Tamanna Dasanjh, Horatio Q. X. Wong +4 more·Jul 9, 2024
Methods to find the solution state for optimization problems encoded into Ising Hamiltonians are a very active area of current research. In this work we compare the quantum approximate optimization algorithm (QAOA) with multi-stage quantum walks (MSQ...
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...
A Randomized Method for Simulating Lindblad Equations and Thermal State Preparation
Hongrui Chen, Bowen Li, Jianfeng Lu +1 more·Jul 9, 2024
<jats:p> We study a qDRIFT-type randomized method to simulate Lindblad dynamics by decomposing its generator into an ensemble of Lindbladians, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> ...
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...
Cyclic solid-state quantum battery: thermodynamic characterization and quantum hardware simulation
Luca Razzoli, G. Gemme, Ilia Khomchenko +4 more·Jul 9, 2024
We introduce a cyclic quantum battery QB model, based on an interacting bipartite system, weakly coupled to a thermal bath. The working cycle of the battery consists of four strokes: system thermalization, disconnection of subsystems, ergotropy extra...
Realizing Scalable Conditional Operations through Auxiliary Energy Levels
Sheng Zhang, Peng Duan, Yun-Jie Wang +19 more·Jul 9, 2024
In the noisy intermediate-scale quantum (NISQ) era, flexible quantum operations are essential for advancing large-scale quantum computing, as they enable shorter circuits that mitigate decoherence and reduce gate errors. However, the complex control ...
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...
The Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Models
Archisman Ghosh, Swaroop Ghosh·Jul 9, 2024
Quantum Machine Learning (QML) is an amalgamation of quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Inte...
Protein Design by Integrating Machine Learning with Quantum Annealing and Quantum-inspired Optimization
Veronica Panizza, Philipp Hauke, Cristian Micheletti +1 more·Jul 9, 2024
The protein design problem involves finding polypeptide sequences folding into a given threedimensional structure. Its rigorous algorithmic solution is computationally demanding, involving a nested search in sequence and structure spaces. Structure s...
Colloquium: Synthetic quantum matter in non-standard geometries
Tobias Grass, Dario Bercioux, Utso Bhattacharya +3 more·Jul 8, 2024
Quantum simulation is making a significant impact on scientific research. The prevailing tendency of the field is to build quantum simulators that get closer to real-world systems of interest, in particular electronic materials. However, progress in ...
Heisenberg-limited Bayesian phase estimation with low-depth digital quantum circuits
Su Direkci, Ran Finkelstein, Manuel Endres +1 more·Jul 8, 2024
Optimal phase estimation protocols require complex state preparation and readout schemes, generally unavailable or unscalable in many quantum platforms. We develop and analyze a scheme that achieves near-optimal precision up to a constant overhead fo...
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...
Circuit Partitioning and Transmission Cost Optimization in Distributed Quantum Circuits
Xinyu Chen, Zilu Chen, P. Zhu +2 more·Jul 8, 2024
Given the limitations on the number of qubits in current noisy intermediate-scale quantum (NISQ) devices, the implementation of large-scale quantum algorithms on such devices is challenging, prompting research into distributed quantum computing. This...
Packing fraction related transport in disordered quantum dot arrays
K. Eshraghi, Shreyam Natani, P. Bandaru·Jul 8, 2024
Models to describe electrical conduction in quantum dot (QD) constituted films often overlook the effects of geometric disorder. We address related issues by examining the influence of the QD packing fraction (PF) on the charge transport and transmis...
Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches
Y. Cordero, S. Biswas, F. Vilariño +1 more·Jul 8, 2024
Quantum machine learning (QML) investigates how quantum phenomena can be exploited in order to learn data in an alternative way, \textit{e.g.} by means of a quantum computer. While recent results evidence that QML models can potentially surpass their...
Noise-tolerant public-key quantum money from a classical oracle
P. Yuen·Jul 8, 2024
Quantum money is the task of verifying the validity of banknotes while ensuring that they cannot be counterfeited. Public-key quantum money allows anyone to perform verification, while the private-key setting restricts the ability to verify to banks,...
Tight Quantum Depth Lower Bound for Solving Systems of Linear Equations
Qisheng Wang, Zhicheng Zhang·Jul 8, 2024
Since Harrow [A. W. Harrow, A. Hassidim, and S. Lloyd, ] showed that a system of linear equations with N variables and condition number κ can be solved on a quantum computer in poly[log(N),κ] time, exponentially faster than any classical algorithms, ...