Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Quantum Resource Theories beyond Convexity
Roberto Salazar, Jakub Czartowski, Ricard Ravell Rodríguez +3 more·May 9, 2024
A class of quantum resource theories, based on non-convex star-shape sets, presented in this work captures the key quantum properties that cannot be studied by standard convex theories. We provide operational interpretations for a resource of this cl...
Sudden change of interferometric power for X shape states
D. Zhu, F. L. Zhang, J. L. Chen·May 9, 2024
Quantum interferometric power (IP) is a discordlike measure. We study the dynamics of IP for two-qubit X shape states under different noisy environments. Our study shows that IP exhibits sudden change, and one side quantum channel is enough for the o...
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Shamminuj Aktar, Andreas Bartschi, Diane Oyen +2 more·May 9, 2024
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full pot...
Quantum Secure Anonymous Communication Networks
Mohammad Saidur Rahman, Stephen Diadamo, Miralem Mehic +1 more·May 9, 2024
Anonymous communication networks (ACNs) enable Internet browsing in a way that prevents accessed content from being traced back to the user. This provides a high level of privacy, protecting individuals from being tracked by advertisers, governments,...
High-coherence fluxonium qubits manufactured with a wafer-scale-uniformity process
Fei Wang, Kannan Lu, Huijuan Zhan +32 more·May 9, 2024
Fluxonium qubits are recognized for their high coherence times and high operation fidelities. These are attributed to their unique design incorporating a superinductor, which is typically implemented using an array of over 100 Josephson junctions; ho...
Photonic quantum generative adversarial networks for classical data
Tigran Sedrakyan, Alexia Salavrakos·May 9, 2024
In generative learning, models are trained to produce new samples that follow the distribution of the target data. These models were historically difficult to train, until proposals such as Generative Adversarial Networks (GANs) emerged, where a gene...
A Genetic Approach to Minimising Gate and Qubit Teleportations for Multi-Processor Quantum Circuit Distribution
O. Crampton, Panagiotis Promponas, Richard Chen +3 more·May 9, 2024
Distributed Quantum Computing (DQC) provides a means for scaling available quantum computation by interconnecting multiple quantum processor units (QPUs). A key challenge in this domain is efficiently allocating logical qubits from quantum circuits t...
Estimating Non-Stabilizerness Dynamics Without Simulating It
Alessio Paviglianiti, Guglielmo Lami, M. Collura +1 more·May 9, 2024
We introduce the Iterative Clifford Circuit Renormalization (ICCR), a novel technique designed to efficiently handle the dynamics of non-stabilizerness (a.k.a. quantum magic) in generic quantum circuits. ICCR iteratively adjusts the starting circuit,...
Wave Function Collapse, Lorentz Invariance, and the Third Postulate of Relativity
Edward J. Gillis·May 8, 2024
The changes that quantum states undergo during measurement are both probabilistic and nonlocal. These two characteristics complement one another to insure compatibility with relativity and maintain conservation laws. The probabilistic nature of nonlo...
Optical single-shot readout of spin qubits in silicon
A. Gritsch, A. Ulanowski, Jakob Pforr +1 more·May 8, 2024
Small registers of spin qubits in silicon can exhibit hour-long coherence times and exceeded error-correction thresholds. However, their connection to larger quantum processors is an outstanding challenge. To this end, spin qubits with optical interf...
Optimizing Tensor Contraction Paths: A Greedy Algorithm Approach With Improved Cost Functions
Sheela Orgler, Mark Blacher·May 8, 2024
Finding efficient tensor contraction paths is essential for a wide range of problems, including model counting, quantum circuits, graph problems, and language models. There exist several approaches to find efficient paths, such as the greedy and rand...
Barren plateaus swamped with traps
Nikita A. Nemkov, E. Kiktenko, Aleksey K. Fedorov·May 8, 2024
Two main challenges preventing efficient training of variational quantum algorithms and quantum machine learning models are local minima and barren plateaus. Typically, barren plateaus are associated with deep circuits, while shallow circuits have be...
Quantum Circuit Ansatz: Patterns of Abstraction and Reuse of Quantum Algorithm Design
Xiaoyu Guo, Takahiro Muta, Jianjun Zhao·May 8, 2024
Quantum computing holds the potential to revolutionize various fields by efficiently tackling complex problems. At its core are quantum circuits, sequences of quantum gates manipulating quantum states. The selection of the right quantum circuit ansat...
Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer.
Javier Robledo-Moreno, Mario Motta, Holger Haas +14 more·May 8, 2024
A universal quantum computer can simulate diverse quantum systems, with electronic structure for chemistry offering challenging problems for practical use cases around the hundred-qubit mark. Although current quantum processors have reached this size...
Quantum-inspired genetic algorithm for designing planar multilayer photonic structure
Zhihao Xu, Wenjie Shang, Seongmin Kim +3 more·May 8, 2024
Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computing resources and the growing computing needs has become an urgent prob...
Hybrid Quantum Graph Neural Network for Molecular Property Prediction
Michael Vitz, Hamed Mohammadbagherpoor, S. Sandeep +3 more·May 8, 2024
To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have demonstrated abi...
Scalable Circuit Cutting and Scheduling in a Resource-Constrained and Distributed Quantum System
Shuwen Kan, Zefan Du, Miguel Palma +6 more·May 7, 2024
Despite rapid developments in quantum computing, current systems remain limited in practical applications due to their constrained qubit counts and quality. Technologies such as superconducting, trapped ions, and neutral atom quantum computing are pr...
Beyond the Buzz: Strategic Paths for Enabling Useful NISQ Applications
P. Hegde, O. Kyriienko, H. Heimonen +6 more·May 7, 2024
There is much debate on whether quantum computing on current NISQ devices, consisting of noisy hundred qubits and requiring a non-negligible usage of classical computing as part of the algorithms, has utility and will ever offer advantages for scient...
Quantum unital Otto heat engines: Using Kirkwood-Dirac quasi-probability for the engine’s coherence to stay alive
Abdelkader El Makouri, A. Slaoui, R. Laamara·May 7, 2024
In this work, we consider quantum unital Otto heat engines. The latter refers to the fact that both the unitaries of the adiabatic strokes and the source of the heat provided to the engine preserve the maximally mixed state. We show how to compute th...
Quantum Circuit Optimisation and MBQC Scheduling with a Pauli Tracking Library
Jannis Ruh, S. Devitt·May 7, 2024
We present a software library for the commutation of Pauli operators through quantum Clifford circuits, which is called Pauli tracking. Tracking Pauli operators allows one to reduce the number of Pauli gates that must be executed on quantum hardware....