Papers
Live trends in quantum computing research, updated daily from arXiv.
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12,931 papers in 12 months (-5% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale +8 more·Nov 30, 2023
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structu...
Optimizing ZX-diagrams with deep reinforcement learning
Maximilian Nägele, Florian Marquardt·Nov 30, 2023
ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more. The utility of ZX-diagrams relies on a ...
Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers
Hanrui Wang, Pengyu Liu, K. Shao +5 more·Nov 27, 2023
Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable limits for useful algorithm execution. Quantum Error Correction (QEC) mi...
Artificial neural network syndrome decoding on IBM quantum processors
Brhyeton Hall, S. Gicev, Muhammad Usman·Nov 26, 2023
Syndrome decoding is an integral but computationally demanding step in the implementation of quantum error correction for fault-tolerant quantum computing. Here, we report the development and benchmarking of Artificial Neural Network (ANN) decoding o...
Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis
Erik Terres Escudero, Danel Arias Alamo, Oier Mentxaka Gómez +1 more·Nov 23, 2023
In the race towards quantum computing, the potential benefits of quantum neural networks (QNNs) have become increasingly apparent. However, Noisy Intermediate-Scale Quantum (NISQ) processors are prone to errors, which poses a significant challenge fo...
Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation
Mohammad Junayed Hasan, M.R.C. Mahdy·Nov 23, 2023
Quantum neural networks (QNNs), harnessing superposition and entanglement, have shown potential to surpass classical methods in complex learning tasks but remain limited by hardware constraints and noisy conditions. In this work, we present a novel f...
Alleviating Barren Plateaus in Parameterized Quantum Machine Learning Circuits: Investigating Advanced Parameter Initialization Strategies
Muhammad Kashif, Muhammad Rashid, S. Al-kuwari +1 more·Nov 22, 2023
Parameterized quantum circuits (PQCs) have emerged as a foundational element in the development and applications of quantum algorithms. However, when initialized with random parameter values, PQCs often exhibit barren plateaus (BP). These plateaus, c...
Benchmarking Machine Learning Models for Quantum Error Correction
Tim Fu, Yue Zhao·Nov 18, 2023
Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer systems, which aims to detect and correct errors in the data qubits within quantum computers. Due to the presence of unreliable data qubits in existing quantum comp...
NISQ-Compatible Error Correction of Quantum Data Using Modified Dissipative Quantum Neural Networks
Armin Ahmadkhaniha, M. Bathaee·Nov 17, 2023
Using a dissipative quantum neural network (DQNN) accompanied by conjugate layers, we upgrade the performance of the existing quantum auto-encoder (QAE) network as a quantum denoiser of a noisy m-qubit GHZ state. Our new denoising architecture requir...
sQUlearn - A Python Library for Quantum Machine Learning
D. Kreplin, Moritz Willmann, Jan Schnabel +2 more·Nov 15, 2023
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML resear...
Hybrid GRU-CNN bilinear parameters initialization for quantum approximate optimization algorithm
Zuyu Xu, Pengnian Cai, Kang Shen +7 more·Nov 14, 2023
The Quantum Approximate Optimization Algorithm (QAOA), a pivotal paradigm in the realm of variational quantum algorithms (VQAs), offers promising computational advantages for tackling combinatorial optimization problems. Well-defined initial circuit ...
Hybrid synaptic structure for spiking neural network realization
S. Razmkhah, M. A. Karamuftuoglu, A. Bozbey·Nov 13, 2023
Neural networks and neuromorphic computing represent fundamental paradigms as alternative approaches to Von-Neumann-based implementations, advancing in the applications of deep learning and machine vision. Nonetheless, conventional semiconductor circ...
Multimodal deep representation learning for quantum cross-platform verification
Yan Qian, Yuxuan Du, Zhenliang He +2 more·Nov 7, 2023
Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms, utilizing minimal measurements. While the ran...
From conceptual spaces to quantum concepts: formalising and learning structured conceptual models
Sean Tull, R. A. Shaikh, Sara Sabrina Zemljič +1 more·Nov 6, 2023
In this article we present a new modelling framework for structured concepts using a category-theoretic generalisation of conceptual spaces, and show how the conceptual representations can be learned automatically from data, using two very different ...
Noise-agnostic quantum error mitigation with data augmented neural models
Manwen Liao, Yan Zhu, G. Chiribella +1 more·Nov 3, 2023
Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model o...
Echo-evolution data generation for quantum error mitigation via neural networks
Danila Babukhin·Nov 1, 2023
Neural networks provide a prospective tool for error mitigation in quantum simulation of physical systems. However, we need both noisy and noise-free data to train neural networks to mitigate errors in quantum computing results. Here, we propose a ph...
Enhancing Graph Neural Networks with Quantum Computed Encodings
Slimane Thabet, Romain Fouilland, Mehdi Djellabi +4 more·Oct 31, 2023
Transformers are increasingly employed for graph data, demonstrating competitive performance in diverse tasks. To incorporate graph information into these models, it is essential to enhance node and edge features with positional encodings. In this wo...
Practical Trainable Temporal Postprocessor for Multistate Quantum Measurement
Saeed A. Khan, Ryan Kaufman, Boris Mesits +2 more·Oct 27, 2023
We develop and demonstrate a trainable temporal postprocessor (TPP) harnessing a simple but versatile machine learning algorithm to provide optimal processing of quantum measurement data subject to arbitrary noise processes for the readout of an arbi...
Hybrid Quantum-Classical Graph Neural Networks for Tumor Classification in Digital Pathology
Anupama Ray, Dhiraj Madan, Srushti Patil +2 more·Oct 17, 2023
Advances in classical machine learning and single-cell technologies have paved the way for understanding interactions between disease cells and tumor microenvironments towards accelerating therapeutic discovery. However, challenges in these machine l...
Calibrating the role of entanglement in variational quantum circuits
Azar C. Nakhl, T. Quella, Muhammad Usman·Oct 16, 2023
Entanglement is a key property of quantum computing that separates it from its classical counterpart, however, its exact role in the performance of quantum algorithms, especially variational quantum algorithms, is not well understood. In this work, w...