Papers
Live trends in quantum computing research, updated daily from arXiv.
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15,683 papers in 12 months (-39% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Quantum Advantage with Shallow Circuits under Arbitrary Corruption
Atsuya Hasegawa, Franccois Le Gall·May 3, 2021
Recent works by Bravyi, Gosset and K\"onig (Science 2018), Bene Watts et al. (STOC 2019), Coudron, Stark and Vidick (QIP 2019) and Le Gall (CCC 2019) have shown unconditional separations between the computational powers of shallow (i.e., small-depth)...
Progress toward favorable landscapes in quantum combinatorial optimization
Juneseo Lee, Alicia B. Magann, H. Rabitz +1 more·May 3, 2021
The performance of variational quantum algorithms relies on the success of using quantum and classical computing resources in tandem. Here, we study how these quantum and classical components interrelate. In particular, we focus on algorithms for sol...
Subexponential rate versus distance with time-multiplexed quantum repeaters
Prajit Dhara, A. Patil, H. Krovi +1 more·May 3, 2021
Quantum communications capacity using direct transmission over length-$L$ optical fiber scales as $R \sim e^{-\alpha L}$, where $\alpha$ is the fiber's loss coefficient. The rate achieved using a linear chain of quantum repeaters equipped with quantu...
Cost vector analysis&multi-path entanglement routing in quantum networks
Hudson Leone, N. Miller, Deepesh Singh +2 more·May 2, 2021
We present a static framework for analysing quantum routing protocols that we call the \textit{cost-vector formalism}. Here, quantum networks are recast as multi-graphs where edges represent two-qubit entanglement resources that \textit{could} exist ...
Comparison between a quantum annealer and a classical approximation algorithm for computing the ground state of an Ising spin glass.
Ran Yaacoby, Nathan Schaar, Leon Kellerhals +3 more·May 2, 2021
Finding the ground state of an Ising spin glass on general graphs belongs to the class of NP-hard problems, widely believed to have no efficient polynomial-time algorithms to solve them. An approach developed in computer science for dealing with such...
Fock state-enhanced expressivity of quantum machine learning models
Beng Yee Gan, D. Leykam, D. Angelakis·May 1, 2021
The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategies are necessary. We propose a photonic-based bosonic data-encoding sc...
Tensor Renormalization Group for interacting quantum fields
M. Campos, G. Sierra, E. Lopez·Apr 30, 2021
We present a new tensor network algorithm for calculating the partition function of interacting quantum field theories in 2 dimensions. It is based on the Tensor Renormalization Group (TRG) protocol, adapted to operate entirely at the level of fields...
Hot-Start Optimization for Variational Quantum Eigensolver
Belozerova Polina, S. Arthur, Zotov Yuriy +2 more·Apr 30, 2021
The Variational Quantum Eigensolver (VQE) is one the most perspective algorithms for simulation of quantum many body physics that have recently attached a lot of attention and believed would be practical for implementation on the near term quantum de...
Optimizing Parameterized Quantum Circuits With Free-Axis Single-Qubit Gates
Hiroshi C. Watanabe, Rudy Raymond, Yuko Ohnishi +2 more·Apr 30, 2021
Variational quantum algorithms, which utilize parameterized quantum circuits (PQCs), are promising tools to achieve quantum advantage for optimization problems on near-term quantum devices. Their PQCs have been conventionally constructed from paramet...
Automatically Differentiable Quantum Circuit for Many-qubit State Preparation
Peng Zhou, Rui Hong, Shi-Ju Ran·Apr 30, 2021
Constructing quantum circuits for efficient state preparation belongs to the central topics in the field of quantum information and computation. As the number of qubits grows fast, methods to derive large-scale quantum circuits are strongly desired. ...
Entangling Quantum Generative Adversarial Networks.
M. Niu, Alexander Zlokapa, M. Broughton +4 more·Apr 30, 2021
Generative adversarial networks (GANs) are one of the most widely adopted machine learning methods for data generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (an entangling quantum GAN, EQ-GAN...
Quantum Machine Learning with SQUID
A. Roggero, Jakub Filipek, Shih-Chieh Hsu +1 more·Apr 30, 2021
In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized desi...
Verification of Distributed Quantum Programs
Yuan Feng, Sanjiang Li, M. Ying·Apr 30, 2021
Distributed quantum systems and especially the Quantum Internet have the ever-increasing potential to fully demonstrate the power of quantum computation. This is particularly true given that developing a general-purpose quantum computer is much more ...
Continuous black-box optimization with quantum annealing and random subspace coding
Syun Izawa, Koki Kitai, Shu Tanaka +2 more·Apr 30, 2021
A black-box optimization algorithm such as Bayesian optimization finds extremum of an unknown function by alternating inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perfo...
Universal set of quantum gates for the flip-flop qubit in the presence of 1/f noise
E. Ferraro, Davide Rei, M. Paris +1 more·Apr 29, 2021
Impurities hosted in semiconducting solid matrices represent an extensively studied platform for quantum computing applications. In this scenario, the so-called flip-flop qubit emerges as a convenient choice for scalable implementations in silicon. F...
Quantum computational quantitative trading: high-frequency statistical arbitrage algorithm
Xi-Ning Zhuang, Zhao-Yun Chen, Yuchun Wu +1 more·Apr 29, 2021
Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage tradi...
Optimal training of variational quantum algorithms without barren plateaus
T. Haug, M. Kim·Apr 29, 2021
Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers. However, training VQAs often requires an extensive amount of time and suffers from the barren plateau problem where the magnitude of the gradients vanishes wi...
Relaxation of stationary states on a quantum computer yields a unique spectroscopic fingerprint of the computer’s noise
Scott E. Smart, Zixuan Hu, S. Kais +1 more·Apr 29, 2021
Quantum computing has the potential to revolutionize computing, but its significant sensitivity to noise requires sophisticated error correction and mitigation. Traditionally, noise on the quantum device is characterized directly through qubit and ga...
Quantum speedups for dynamic programming on n-dimensional lattice graphs
A. Glos, R. Mori, J. Vihrovs·Apr 29, 2021
Motivated by the quantum speedup for dynamic programming on the Boolean hypercube by Ambainis et al. (2019), we investigate which graphs admit a similar quantum advantage. In this paper, we examine a generalization of the Boolean hypercube graph, the...
Optimal Control of the Operating Regime of a Single-Electron Double Quantum Dot
Vincent Reiher, Y. B'erub'e-Lauziere·Apr 28, 2021
The double-quantum-dot device benefits from the advantages of both the spin and charge qubits, while offering ways to mitigate their drawbacks. Careful gate voltage modulation can grant greater spinlike or chargelike dynamics to the device, yielding ...