Papers
Live trends in quantum computing research, updated daily from arXiv.
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Quantum tangent kernel
Norihito Shirai, K. Kubo, K. Mitarai +1 more·Nov 4, 2021
The quantum kernel method is one of the key approaches to quantum machine learning, which has the advantage of not requiring optimization and its theoretical simplicity. By virtue of these properties, several experimental demonstrations and discussio...
QDCNN: Quantum Dilated Convolutional Neural Network
Yixiong Chen·Oct 29, 2021
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elemen...
Subtleties in the trainability of quantum machine learning models
Supanut Thanasilp, Samson Wang, Nhat A. Nghiem +2 more·Oct 27, 2021
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. Howev...
Variational quantum algorithm for Gaussian discrete solitons and their boson sampling
C. Conti·Oct 24, 2021
In the context of quantum information, highly nonlinear regimes, such as those supporting solitons, are marginally investigated. We miss general methods for quantum solitons, although they can act as entanglement generators or as self-organized quant...
QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
Hanrui Wang, Jiaqi Gu, Yongshan Ding +4 more·Oct 21, 2021
Parameterized Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQC models has a severe degradation on real quantum devices. Take Quantu...
Efficient Decoding of Surface Code Syndromes for Error Correction in Quantum Computing
Debasmita Bhoumik, Pinaki Sen, Ritajit Majumdar +3 more·Oct 21, 2021
Errors in surface code have typically been decoded by Minimum Weight Perfect Matching (MWPM) based method. Recently, neural-network-based Machine Learning (ML) techniques have been employed for this purpose. Here we propose a two-level (low and high)...
Machine learning for continuous quantum error correction on superconducting qubits
Ian Convy, Haoran Liao, Song Zhang +5 more·Oct 20, 2021
Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla ...
Classical-To-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks
Jun Qi, Javier Tejedor·Oct 17, 2021
This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and...
Development of Quantum Circuits for Perceptron Neural Network Training, Based on the Principles of Grover's Algorithm
C. B. Pronin, A. Ostroukh·Oct 15, 2021
This paper highlights a practical research of the possibility of forming quantum circuits for training neural networks. The demonstrated quantum circuits were based on the principles of Grover's Search Algorithm. The perceptron was chosen as the arch...
Unsupervised learning of correlated quantum dynamics on disordered lattices
Miri Kenig, Y. Lahini·Oct 13, 2021
Quantum particles co-propagating on disordered lattices develop complex non-classical correlations due to an interplay between quantum statistics, inter-particle interactions, and disorder. Here we present a deep learning algorithm based on Generativ...
A scalable and fast artificial neural network syndrome decoder for surface codes
S. Gicev, L. Hollenberg, M. Usman·Oct 12, 2021
Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing. When operated as stabilizer codes, surface code computations consist of a syndrome decoding step where measured stabilizer operators...
Does Preprocessing Help Training Over-parameterized Neural Networks?
Zhao Song, Shuo Yang, Ruizhe Zhang·Oct 9, 2021
Deep neural networks have achieved impressive performance in many areas. Designing a fast and provable method for training neural networks is a fundamental question in machine learning. The classical training method requires paying $\Omega(mnd)$ cost...
Supervised Learning Enhanced Quantum Circuit Transformation
Xiang-Yu Zhou, Yuan Feng, Sanjiang Li·Oct 6, 2021
A quantum circuit transformation (QCT) is required when executing a quantum program in a real quantum processing unit (QPU). By inserting auxiliary SWAP gates, a QCT algorithm transforms a quantum circuit to one that satisfies the connectivity constr...
Lossy compression of statistical data using quantum annealer
B. Yoon, Nga T. T. Nguyen, Chia-Cheng Chang +1 more·Oct 5, 2021
We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables. The algorithm finds a set of basis vectors and their binary coefficients that precisely reconstruct the original...
Machine learning statistical gravity from multi-region entanglement entropy
J. Lam, Yi-Zhuang You·Oct 3, 2021
The Ryu-Takayanagi formula directly connects quantum entanglement and geometry. Yet the assumption of static geometry lead to an exponentially small mutual information between far-separated disjoint regions, which does not hold in many systems such a...
Development and Training of Quantum Neural Networks, Based on the Principles of Grover's Algorithm
C. B. Pronin, A. Ostroukh·Oct 1, 2021
This paper highlights the possibility of creating quantum neural networks that are trained by Grover's Search Algorithm. The purpose of this work is to propose the concept of combining the training process of a neural network, which is performed on t...
Hybrid quantum classical graph neural networks for particle track reconstruction
Cenk Tüysüz, C. Rieger, Kristiane Novotny +6 more·Sep 26, 2021
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in lum...
Deep recurrent networks predicting the gap evolution in adiabatic quantum computing
Naeimeh Mohseni, C. Navarrete-Benlloch, T. Byrnes +1 more·Sep 17, 2021
In adiabatic quantum computing finding the dependence of the gap of the Hamiltonian as a function of the parameter varied during the adiabatic sweep is crucial in order to optimize the speed of the computation. Inspired by this challenge, in this wor...
Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases
J. Herrmann, Sergi Masot Llima, A. Remm +15 more·Sep 13, 2021
Quantum computing crucially relies on the ability to efficiently characterize the quantum states output by quantum hardware. Conventional methods which probe these states through direct measurements and classically computed correlations become comput...
Towards a variational Jordan–Lee–Preskill quantum algorithm
Junyu Liu, Zimu Li, Han Zheng +2 more·Sep 12, 2021
Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices. In this work, we formulate the theory of (time-dependent) variational quantum simulation of the 1+1 di...