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Quantum computing (QC) seems to show potential for application in machine learning (ML). In particular quantum kernel methods (QKM) exhibit promising properties for use in supervised ML tasks. However, a major disadvantage of kernel methods…

Quantum Physics · Physics 2025-01-14 Kilian Tscharke , Sebastian Issel , Pascal Debus

As quantum computing advances toward fault-tolerant architectures, quantum error detection (QED) has emerged as a practical and scalable intermediate strategy in the transition from error mitigation to full error correction. By identifying…

Remarkable advances have been achieved in localization techniques in past decades, rendering it one of the most important technologies indispensable to our daily lives. In this paper, we investigate a novel localization approach for future…

Emerging Technologies · Computer Science 2024-05-07 Entong He , Yuxiang Yang , Chenshu Wu

Leakage of quantum information out of computational states into higher energy states represents a major challenge in the pursuit of quantum error correction (QEC). In a QEC circuit, leakage builds over time and spreads through multi-qubit…

Quantum Physics · Physics 2024-05-01 Kevin C. Miao , Matt McEwen , Juan Atalaya , Dvir Kafri , Leonid P. Pryadko , Andreas Bengtsson , Alex Opremcak , Kevin J. Satzinger , Zijun Chen , Paul V. Klimov , Chris Quintana , Rajeev Acharya , Kyle Anderson , Markus Ansmann , Frank Arute , Kunal Arya , Abraham Asfaw , Joseph C. Bardin , Alexandre Bourassa , Jenna Bovaird , Leon Brill , Bob B. Buckley , David A. Buell , Tim Burger , Brian Burkett , Nicholas Bushnell , Juan Campero , Ben Chiaro , Roberto Collins , Paul Conner , Alexander L. Crook , Ben Curtin , Dripto M. Debroy , Sean Demura , Andrew Dunsworth , Catherine Erickson , Reza Fatemi , Vinicius S. Ferreira , Leslie Flores Burgos , Ebrahim Forati , Austin G. Fowler , Brooks Foxen , Gonzalo Garcia , William Giang , Craig Gidney , Marissa Giustina , Raja Gosula , Alejandro Grajales Dau , Jonathan A. Gross , Michael C. Hamilton , Sean D. Harrington , Paula Heu , Jeremy Hilton , Markus R. Hoffmann , Sabrina Hong , Trent Huang , Ashley Huff , Justin Iveland , Evan Jeffrey , Zhang Jiang , Cody Jones , Julian Kelly , Seon Kim , Fedor Kostritsa , John Mark Kreikebaum , David Landhuis , Pavel Laptev , Lily Laws , Kenny Lee , Brian J. Lester , Alexander T. Lill , Wayne Liu , Aditya Locharla , Erik Lucero , Steven Martin , Anthony Megrant , Xiao Mi , Shirin Montazeri , Alexis Morvan , Ofer Naaman , Matthew Neeley , Charles Neill , Ani Nersisyan , Michael Newman , Jiun How Ng , Anthony Nguyen , Murray Nguyen , Rebecca Potter , Charles Rocque , Pedram Roushan , Kannan Sankaragomathi , Christopher Schuster , Michael J. Shearn , Aaron Shorter , Noah Shutty , Vladimir Shvarts , Jindra Skruzny , W. Clarke Smith , George Sterling , Marco Szalay , Douglas Thor , Alfredo Torres , Theodore White , Bryan W. K. Woo , Z. Jamie Yao , Ping Yeh , Juhwan Yoo , Grayson Young , Adam Zalcman , Ningfeng Zhu , Nicholas Zobrist , Hartmut Neven , Vadim Smelyanskiy , Andre Petukhov , Alexander N. Korotkov , Daniel Sank , Yu Chen

Quantum computing is poised to solve practically useful problems which are computationally intractable for classical supercomputers. However, the current generation of quantum computers are limited by errors that may only partially be…

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivity, and coherence times, a quantum circuit optimization is essential to make the best use of near-term quantum devices. We…

Owing to the computational complexity of electronic structure algorithms running on classical digital computers, the range of molecular systems amenable to simulation remains tightly circumscribed even after many decades of work. Quantum…

Quantum Physics · Physics 2022-05-18 Alexis Ralli , Michael I. Williams , Peter V. Coveney

A new approximate Quantum State Preparation (QSP) method is introduced, called the Walsh Series Loader (WSL). The WSL approximates quantum states defined by real-valued functions of single real variables with a depth independent of the…

Quantum Physics · Physics 2023-11-13 Julien Zylberman , Fabrice Debbasch

Quantum repeaters have promised efficient scaling of quantum networks for over two decades. Despite numerous platforms proclaiming functional repeaters, the realization of large-scale networks remains elusive, indicating that the resources…

Quantum Physics · Physics 2026-02-27 Manik Dawar , Ralf Riedinger , Nilesh Vyas , Paulo Mendes

Quantum Random Access Memory (QRAM) is a critical component for loading classical data into quantum computers. While constructing a practical QRAM presents several challenges, including the impracticality of an infinitely large QRAM size…

Demonstrating quantum advantage has been a pressing challenge in the field. Most claimed quantum speedups rely on a subroutine in which classical information can be accessed in a coherent quantum manner, which imposes a crucial constraint…

Quantum Physics · Physics 2025-11-04 Nhat A. Nghiem

Variational Quantum Algorithms (VQAs) have emerged as promising methods for tackling complex problems on near-term quantum devices. Among these algorithms, the Variational Quantum Linear Solver (VQLS) addresses linear systems of the form…

Quantum Physics · Physics 2024-09-11 Gloria Turati , Alessia Marruzzo , Maurizio Ferrari Dacrema , Paolo Cremonesi

In recent years, quantum computing has started to demonstrate superior efficiency to classical computing. In quantum computing, quantum circuits that implement specific quantum algorithms are usually not directly executable on quantum…

Quantum Physics · Physics 2025-08-19 Yuntao Liu , Jayden John , Qian Wang

Quantum computing hardware has grown sufficiently complex that it often can no longer be simulated by classical computers, but its computational power remains limited by errors. These errors corrupt the results of quantum algorithms, and it…

Quantum machine learning consists in taking advantage of quantum computations to generate classical data. A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data, a…

The aircraft loading optimization problem is a computationally hard problem with the best known classical algorithm scaling exponentially with the number of objects. We propose a quantum approach based on a multi-angle variant of the QAOA…

In the quest to reboot computing, quantum annealing (QA) is an interesting candidate for a new capability. While it has not demonstrated an advantage over classical computing on a real-world application, many important regions of the QA…

Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world…

Quantum Physics · Physics 2024-09-13 Hasan Abbas Al-Mohammed , Saif Al-Kuwari , Hashir Kuniyil , Ahmed Farouk

The potential of quantum computers to outperform classical ones in practically useful tasks remains challenging in the near term due to scaling limitations and high error rates of current quantum hardware. While quantum error correction…