Papers
Live trends in quantum computing research, updated daily from arXiv.
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13,008 papers in 12 months (-3% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Quantum random power method for ground state computation
Taehee Ko, Hyowon Park, Sangkook Choi·Aug 16, 2024
We present a quantum-classical hybrid random power method that approximates a ground state of a Hamiltonian. The quantum part of our method computes a fixed number of elements of a Hamiltonian-matrix polynomial via quantum polynomial filtering techni...
A novel quantum algorithm for efficient attractor search in gene regulatory networks
M. Rossini, Felix M. Weidner, J. Ankerhold +1 more·Aug 16, 2024
Summary Describing gene interactions in cells is challenging due to their complexity and the limited microscopic detail available. Boolean networks offer a powerful, coarse-grained approach to modeling these dynamics using binary agents and their int...
Quantum enhanced Markov chains require fine-tuned quenches
Alev Orfi, Dries Sels·Aug 15, 2024
Quantum-enhanced Markov chain Monte Carlo, an algorithm in which configurations are proposed through a measured quantum quench and accepted or rejected by a classical algorithm, has been proposed as a possible method for robust quantum speedup on imp...
From Entanglement Purification Scheduling to Fidelity-constrained Multi-Flow Routing
Ziyue Jia, Lin Chen·Aug 15, 2024
Recently emerged as a disruptive networking paradigm, quantum networks rely on the mysterious quantum entanglement to teleport qubits without physically transferring quantum particles. However, the state of quantum systems is extremely fragile due to...
Quantum Rational Transformation Using Linear Combinations of Hamiltonian Simulations
Yizhi Shen, Niel Van Buggenhout, Daan Camps +2 more·Aug 14, 2024
Rational functions are exceptionally powerful tools in scientific computing, yet their abilities to advance quantum algorithms remain largely untapped. In this paper, we introduce effective implementations of rational transformations of a target oper...
A Multilevel Approach for Solving Large-Scale QUBO Problems with Noisy Hybrid Quantum Approximate Optimization
Filip B. Maciejewski, Bao Gia Bach, Maxime Dupont +5 more·Aug 14, 2024
Quantum approximate optimization is one of the promising candidates for useful quantum computation, particularly in the context of finding approximate solutions to Quadratic Unconstrained Binary Optimization (QUBO) problems. However, the existing qua...
Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements
Feng Zhang, Cai-Zhuang Wang, T. Iadecola +2 more·Aug 13, 2024
The adaptive variational quantum dynamics simulation (AVQDS) method performs real-time evolution of quantum states using automatically generated parameterized quantum circuits that often contain substantially fewer gates than Trotter circuits. Here w...
Robustness of optimal quantum annealing protocols
Niklas Funcke, Julian Berberich·Aug 13, 2024
Noise in quantum computing devices poses a key challenge in their realization. In this paper, we study the robustness of optimal quantum annealing (QA) protocols against coherent control errors, which are multiplicative Hamiltonian errors causing det...
Biased-Noise Thresholds of Zero-Rate Holographic Codes with Tensor-Network Decoding
Junyu Fan, Matthew Steinberg, Alexander Jahn +2 more·Aug 12, 2024
A crucial insight for practical quantum error correction is that different types of errors, such as single-qubit Pauli operators, typically occur with different probabilities. Finding an optimal quantum code under such biased noise is a challenging p...
From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
Andrea Ceschini, Francesco Mauro, Francesca De Falco +7 more·Aug 12, 2024
Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing da...
Stabilizer Entanglement Distillation and Efficient Fault-Tolerant Encoders
Yu Shi, A. Patil, Saikat Guha·Aug 12, 2024
Entanglement is essential for quantum information processing, but is limited by noise. We address this by developing high-yield entanglement distillation protocols with several advancements. (1) We extend the 2-to-1 recurrence entanglement distillati...
Optimal Overlapping Tomography
Kiara Hansenne, Rui Qu, Lisa T. Weinbrenner +7 more·Aug 11, 2024
Characterising large-scale quantum systems is central to fundamental physics and essential for applications of quantum technologies. While a full characterisation requires exponentially increasing resources, focusing on application-relevant informati...
Dynamic Resource Allocation with Quantum Error Detection
Joshua Gao, Ji Liu, A. Gonzales +3 more·Aug 10, 2024
Quantum processing units (QPUs) are highly heterogeneous in terms of physical qubit performance. To add even more complexity, drift in quantum noise landscapes has been well-documented. This makes resource allocation a challenging problem whenever a ...
Fault-tolerant quantum input/output
Matthias Christandl, Omar Fawzi, Ashutosh Goswami·Aug 9, 2024
Usual scenarios of fault-tolerant computation are concerned with the fault-tolerant realization of quantum algorithms that compute classical functions, such as Shor's algorithm for factoring. In particular, this means that input and output to the qua...
Concept learning of parameterized quantum models from limited measurements
Beng Yee Gan, Po-Wei Huang, Elies Gil-Fuster +1 more·Aug 9, 2024
Classical learning of the expectation values of observables for quantum states is a natural variant of learning quantum states or channels. While learning-theoretic frameworks establish the sample complexity and the number of measurement shots per sa...
Tensor-based quantum phase difference estimation for large-scale demonstration
Shutaroh Kanno, Kenji Sugisaki, Hajime Nakamura +5 more·Aug 9, 2024
Significance Quantum phase estimation (QPE) is a foundational algorithm for quantum chemistry, cryptanalysis, and solving linear equations, due to the potential of exponential acceleration for classical algorithms. Despite its importance, QPE has bee...
Distributed Quantum Computing for Chemical Applications
G. Jones, Hans-Arno Jacobsen·Aug 9, 2024
In recent years, interest in quantum computing has increased due to technological advances in quantum hardware and algorithms. Despite the promises of quantum advantage, the applicability of quantum devices has been limited to few qubits on hardware ...
A quantum GAN for entanglement detection and image classification
J. Steck, E. Behrman·Aug 9, 2024
Machine learning can be used as a systematic method to non-algorithmically program quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate building blocks, eliminating that diffi...
Quantum Neural Network Training of a Repeater Node
Diego Fuentealba, Jack Dahn, J. Steck +1 more·Aug 8, 2024
The construction of robust and scalable quantum gates is a uniquely hard problem in the field of quantum computing. Real-world quantum computers suffer from many forms of noise, characterized by the decoherence and relaxation times of a quantum circu...
Entanglement-enhanced learning of quantum processes at scale
A. Seif, Senrui Chen, Swarnadeep Majumder +6 more·Aug 6, 2024
Learning unknown processes affecting a quantum system reveals underlying physical mechanisms and enables suppression, mitigation, and correction of unwanted effects. Describing a general quantum process requires an exponentially large number of param...