Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Controlling NMR spin systems for quantum computation.
Jonathan A. Jones·Feb 1, 2024
Nuclear magnetic resonance is arguably both the best available quantum technology for implementing simple quantum computing experiments and the worst technology for building large scale quantum computers that has ever been seriously put forward. Afte...
Quantum-Assisted Hilbert-Space Gaussian Process Regression
Ahmad Farooq, Cristian A. Galvis-Florez, Simo Särkkä·Feb 1, 2024
Gaussian processes are probabilistic models that are commonly used as functional priors in machine learning. Due to their probabilistic nature, they can be used to capture the prior information on the statistics of noise, smoothness of the functions,...
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning
A. Pastor, Pau Escofet, Sahar Ben Rached +3 more·Jan 31, 2024
Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of quantum mechanics. The scalability of quantum architectures remains a significant challenge. Multi-core quantum architecture...
Correlated optical convolutional neural network with “quantum speedup”
Yifan Sun, Qian Li, Ling‐Jun Kong +1 more·Jan 31, 2024
Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs ha...
Enhancing Grover’s search algorithm: a modified approach to increase the probability of good states
Ismael Abdulrahman·Jan 31, 2024
This article introduces an enhancement to the Grover search algorithm to speed up computing the probability of finding good states. It suggests incorporating a rotation phase angle determined mathematically from the derivative of the model during the...
Intrinsic correlations for statistical ensembles of Dirac-like structures
Caio Fernando e Silva, A. Bernardini·Jan 31, 2024
The Weyl-Wigner formalism for evaluating the intrinsic information of Dirac bispinors as correlated qubits (localized) in a magnetic field is investigated in the extension to statistical ensembles. The confining external field quantizes the quantum c...
Decoherence-Free Subspaces Cannot Prevent the Collapse of Wave Functions
Alfred Li, H. Rabitz, Benjamin Lienhard·Jan 31, 2024
Efficacious quantum information processing relies on extended coherence and precise control. Investigating the limitations surrounding quantum processors is vital for their advancement. In their operation, one challenge is inadvertent wave function c...
Quantum error correction realized by the renormalization group in scalar field theories
Takaaki Kuwahara, Ryota Nasu, Gota Tanaka +1 more·Jan 31, 2024
We demonstrate that quantum error correction is realized by the renormalization group in scalar field theories. We construct $q$-level states by using coherent states in the IR region. By acting on them the inverse of the unitary operator $U$ that de...
Enhanced observable estimation through classical optimization of informationally overcomplete measurement data: Beyond classical shadows
Joonas Malmi, Keijo Korhonen, Daniel Cavalcanti +1 more·Jan 31, 2024
In recent years, informationally complete measurements have attracted considerable attention, especially in the context of classical shadows. In the particular case of informationally over-complete measurements, for which the number of possible outco...
Towards enhancing quantum expectation estimation of matrices through partial Pauli decomposition techniques and post-processing
Dingjie Lu, Yangfan Li, Dax Enshan Koh +3 more·Jan 31, 2024
We introduce an approach for estimating the expectation values of arbitrary $n$-qubit matrices $M \in \mathbb{C}^{2^n\times 2^n}$ on a quantum computer. In contrast to conventional methods like the Pauli decomposition that utilize $4^n$ distinct quan...
Power Characterization of Noisy Quantum Kernels
Yabo Wang, Bo Qi, Xin Wang +2 more·Jan 31, 2024
Quantum kernel methods have been widely recognized as one of the promising quantum machine learning (QML) algorithms that have the potential to achieve quantum advantages. However, their capabilities may be severely degraded by inevitable noises in t...
Unsupervised learning of topological non-Abelian braiding in non-Hermitian bands
Yang Long, Haoran Xue, Baile Zhang·Jan 31, 2024
The topological classification of energy bands has laid the foundation for the discovery of various topological phases of matter in recent decades. While previous work focused on real-energy bands in Hermitian systems, recent studies have shifted att...
Randomly Monitored Quantum Codes
Dongjin Lee, Beni Yoshida·Jan 31, 2024
Quantum measurement has conventionally been regarded as the final step in quantum information processing, which is essential for reading out the processed information but collapses the quantum state into a classical state. However, recent studies hav...
In Situ‐Tunable Spin–Spin Interactions in a Penning Trap with In‐Bore Optomechanics
Joseph H. Pham, Julian Y. Z. Jee, A. Rischka +2 more·Jan 31, 2024
Experimental implementations of quantum simulation must balance control‐field‐induced decoherence with the controllability of the quantum system. The ratio of coherent interaction strength to decoherence induced by stimulated emission in atomic syste...
Realization of fractional quantum Hall state with interacting photons
Can Wang, Feng-Ming Liu, Ming-Cheng Chen +10 more·Jan 30, 2024
Fractional quantum Hall (FQH) states are known for their robust topological order and possess properties that are appealing for applications in fault-tolerant quantum computing. An engineered quantum platform would provide opportunities to operate FQ...
Quantum Time Dynamics Mediated by the Yang-Baxter Equation and Artificial Neural Networks.
Sahil Gulania, Yuri Alexeev, Stephen K. Gray +2 more·Jan 30, 2024
Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANNs) and the Yang-Baxter equation (YBE). Unlike traditional error mi...
QPlacer: Frequency-Aware Component Placement for Superconducting Quantum Computers
Junyao Zhang, Hanrui Wang, Qi Ding +7 more·Jan 30, 2024
Quantum Computers face a critical limitation in qubit numbers, hindering their progression towards large-scale and fault-tolerant quantum computing. A significant challenge impeding scaling is crosstalk, characterized by unwanted interactions among n...
Tapestry of dualities in decohered quantum error correction codes
Kaixiang Su, Zhou Yang, Chao-Ming Jian·Jan 30, 2024
Quantum error correction (QEC) codes protect quantum information from errors due to decoherence. Many of them also serve as prototypical models for exotic topological quantum matters. Investigating the behavior of the QEC codes under decoherence shed...
Mixed State Variational Quantum Eigensolver for the Estimation of Expectation Values at Finite Temperature
Giuseppe Clemente·Jan 30, 2024
We introduce a novel hybrid quantum-classical algorithm for the near-term computation of expectation values in quantum systems at finite temperatures. This is based on two stages: on the first one, a mixed state approximating a fiducial truncated den...
Quantum Circuit Mapping for Universal and Scalable Computing in MZI-based Integrated Photonics
Y. Kwon, Alessio Baldazzi, Lorenzo Pavesi +1 more·Jan 30, 2024
Linear optical quantum computing (LOQC) offers a quantum computation paradigm based on well-established and robust technology and flexible environmental conditions following DiVincenzo's criteria. Within this framework, integrated photonics can be ut...