Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Distributed Quantum Computing with Photons and Atomic Memories
E. Oh, Xuanying Lai, J. Wen +1 more·Jul 5, 2022
The promise of universal quantum computing requires scalable single‐ and inter‐qubit control interactions. Currently, three of the leading candidate platforms for quantum computing are based on superconducting circuits, trapped ions, and neutral atom...
Knowledge Distillation in Quantum Neural Network using Approximate Synthesis
M. Alam, Satwik Kundu, Swaroop Ghosh·Jul 5, 2022
Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major building b...
Quantum Neural Network Compression
Zhirui Hu, Peiyan Dong, Zhepeng Wang +3 more·Jul 4, 2022
Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), that is, a type of neural netw...
Measurements of Floquet code plaquette stabilizers
James R. Wootton·Jul 1, 2022
The recently introduced Floquet codes have already inspired several follow up works in terms of theory and simulation. Here we report the first preliminary results on their experimental implementation, using IBM Quantum hardware. Specifically, we imp...
Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks
Jonathon Brown, M. Paternostro, A. Ferraro·Jun 29, 2022
We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits—encoded in the states of artificial atoms with no direc...
QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
Daniel Silver, Tirthak Patel, Devesh Tiwari·Jun 28, 2022
Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. QUILT is a framework for performing multi-cl...
Robust optimization for quantum reinforcement learning control using partial observations
Chen Jiang, Yu Pan, Zhengguang Wu +2 more·Jun 24, 2022
The current quantum reinforcement learning control models often assume that the quantum states are known a priori for control optimization. However, full observation of quantum state is experimentally infeasible due to the exponential scaling of the ...
Exploring ab initio machine synthesis of quantum circuits
Richard Meister, Cica Gustiani, S. Benjamin·Jun 22, 2022
Gate-level quantum circuits are often derived manually from higher level algorithms. While this suffices for small implementations and demonstrations, ultimately automatic circuit design will be required to realise complex algorithms using hardware-s...
Quantum self-consistent equation-of-motion method for computing molecular excitation energies, ionization potentials, and electron affinities on a quantum computer
Ayush Asthana, Ashutosh Kumar, Vibin Abraham +8 more·Jun 21, 2022
Near-term quantum computers are expected to facilitate material and chemical research through accurate molecular simulations. Several developments have already shown that accurate ground-state energies for small molecules can be evaluated on present-...
Stabilizing and Improving Qubit Coherence by Engineering the Noise Spectrum of Two-Level Systems
Xinyuan You, Ziwen Huang, Uğur Alyanak +3 more·Jun 21, 2022
Superconducting circuits are a leading platform for quantum computing. However, their coherence times are still limited and exhibit temporal fluctuations. Those phenomena are often attributed to the coupling between qubits and material defects that c...
Classical splitting of parametrized quantum circuits
Cenk Tüysüz, Giuseppe Clemente, Arianna Crippa +3 more·Jun 20, 2022
Barren plateaus appear to be a major obstacle for using variational quantum algorithms to simulate large-scale quantum systems or to replace traditional machine learning algorithms. They can be caused by multiple factors such as the expressivity of t...
Quantum machine learning channel discrimination
A. Kardashin, A. Vlasova, A. Pervishko +2 more·Jun 20, 2022
In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measuring the output state. This work studies applications of variational qu...
Inference-Based Quantum Sensing
C. H. Alderete, Max Hunter Gordon, F. Sauvage +4 more·Jun 20, 2022
In a standard quantum sensing (QS) task one aims at estimating an unknown parameter θ, encoded into an n-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to change...
Laziness, barren plateau, and noises in machine learning
Junyu Liu, Zexi Lin, Liang Jiang·Jun 19, 2022
We define laziness to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is exponential in the number of qubits for randomized variational quantum circuits. We...
Spectral analysis for noise diagnostics and filter-based digital error mitigation
Enrico Fontana, I. Rungger, Ross Duncan +1 more·Jun 17, 2022
We investigate the effects of noise on parameterised quantum circuits using spectral analysis and classical signal processing tools. For different noise models, we quantify the additional, higher frequency modes in the output signal caused by device ...
Open source variational quantum eigensolver extension of the quantum learning machine for quantum chemistry
Mohammad Haidar, M. Rančić, T. Ayral +2 more·Jun 17, 2022
Quantum chemistry (QC) is one of the most promising applications of quantum computing. However, present quantum processing units (QPUs) are still subject to large errors. Therefore, noisy intermediate‐scale quantum (NISQ) hardware is limited in terms...
Mapping renormalized coupled cluster methods to quantum computers through a compact unitary representation of nonunitary operators
Bo Peng, K. Kowalski·Jun 17, 2022
Non-unitary theories are commonly seen in the classical simulations of quantum systems. Among these theories, the method of moments of coupled-cluster equations (MMCCs) and the ensuing classes of the renormalized coupled-cluster (CC) approaches have ...
Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm
Fanzhe Qu, S. Erfani, M. Usman·Jun 16, 2022
Quantum computing is anticipated to offer immense computational capabilities which could provide efficient solutions to many data science problems. However, the current generation of quantum devices are small and noisy, which makes it difficult to pr...
Comparative analysis of error mitigation techniques for variational quantum eigensolver implementations on IBM quantum system
Shaobo Zhang, C. Hill, M. Usman·Jun 16, 2022
Quantum computers are anticipated to transcend classical supercomputers for computationally intensive tasks by exploiting the principles of quantum mechanics. However, the capabilities of the current generation of quantum devices are limited due to n...
Linear cross-entropy benchmarking with Clifford circuits
Jianxin Chen, D. Ding, Cupjin Huang +1 more·Jun 16, 2022
With the advent of quantum processors exceeding $100$ qubits and the high engineering complexities involved, there is a need for holistically benchmarking the processor to have quality assurance. Linear cross-entropy benchmarking (XEB) has been used ...