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Related papers: Randomized Benchmarking in the Analogue Setting

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In this work we extend a multi-qubit benchmarking technique known as the Binned Output Generation (BOG) in order to discriminate between coherent and incoherent noise sources in the multi-qubit regime. While methods exist to discriminate…

Quantum Physics · Physics 2021-08-25 Jin-Sung Kim , Lev S. Bishop , Antonio D. Corcoles , Seth Merkel , John A. Smolin , Sarah Sheldon

As quantum circuits increase in size, it is critical to establish scalable multiqubit fidelity metrics. Here we investigate three-qubit randomized benchmarking (RB) with fixed-frequency transmon qubits coupled to a common bus with pairwise…

Quantum Physics · Physics 2019-05-30 David C. McKay , Sarah Sheldon , John A. Smolin , Jerry M. Chow , Jay M. Gambetta

Quantum systems are inherently open and susceptible to environmental noise, which can have both detrimental and beneficial effects on their dynamics. This phenomenon has been observed in bio-molecular systems, where noise enables novel…

Quantum annealers like those from D-Wave Systems implement adiabatic quantum computing to solve optimization problems, but their analog nature and limited control functionalities present challenges to correcting or mitigating errors. As…

Quantum Physics · Physics 2024-04-11 Hristo N. Djidjev

Noise and imperfections are among the prevalent challenges in quantum software engineering for current NISQ systems. They will remain important in the post-NISQ area, as logical, error-corrected qubits will be based on software mechanisms.…

Quantum Physics · Physics 2025-09-17 Stefan Raimund Maschek , Jürgen Schwitalla , Maja Franz , Wolfgang Mauerer

We discuss guidelines for evaluating the performance of parameterized stochastic solvers for optimization problems, with particular attention to systems that employ novel hardware, such as digital quantum processors running variational…

The even distribution and optimization of tasks across resources and workstations is a critical process in manufacturing aimed at maximizing efficiency, productivity, and profitability, known as Robotic Assembly Line Balancing (RALB). With…

Quantum Physics · Physics 2024-12-13 Moritz Willmann , Marcel Albus , Jan Schnabel , Marco Roth

A precise measurement of dephasing over a range of timescales is critical for improving quantum gates beyond the error correction threshold. We present a metrological tool, based on randomized benchmarking, capable of greatly increasing the…

Randomized Benchmarking allows to efficiently and scalably characterize the average error of an unitary 2-design such as the Clifford group $\mathcal{C}$ on a physical candidate for quantum computation, as long as there are no…

Quantum Physics · Physics 2015-11-04 T. Chasseur , F. K. Wilhelm

The effects of noise are one of the most important factors to consider when it comes to quantum computing in the noisy intermediate-scale quantum computing (NISQ) era that we are currently in. Therefore, it is important not only to gain…

Quantum Physics · Physics 2025-08-07 T. Piskor , M. Schöndorf , M. Bauer , D. Smith , T. Ayral , S. Pogorzalek , A. Auer , M. Papič

Bayesian inference is a widely used technique for real-time characterization of quantum systems. It excels in experimental characterization in the low data regime, and when the measurements have degrees of freedom. A decisive factor for its…

Quantum Physics · Physics 2025-07-10 Alexandra Ramôa , Raffaele Santagati , Nathan Wiebe

Quantum learning tasks often leverage randomly sampled quantum circuits to characterize unknown systems. An efficient approach known as "circuit reusing," where each circuit is executed multiple times, reduces the cost compared to…

Quantum Physics · Physics 2025-01-29 Zhuo Chen , Guoding Liu , Xiongfeng Ma

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors…

As experimental platforms for quantum information processing continue to mature, characterization of the quality of unitary gates that can be applied to their quantum bits (qubits) becomes essential. Eventually, the quality must be…

Controlled experimentation, also called A/B testing, is widely adopted to accelerate product innovations in the online world. However, how fast we innovate can be limited by how we run experiments. Most experiments go through a "ramp up"…

Applications · Statistics 2018-01-26 Ya Xu , Weitao Duan , Shaochen Huang

We provide a comprehensive analysis of the differences between two important standards for randomized benchmarking (RB): the Clifford-group RB protocol proposed originally in Emerson et al (2005) and Dankert et al (2006), and a variant of…

Quantum Physics · Physics 2019-03-27 Kristine Boone , Arnaud Carignan-Dugas , Joel J. Wallman , Joseph Emerson

Ubiquitous noises in quantum systems remain a key obstacle to building quantum computers, necessitating the use of quantum error correction codes. Recently, error-correcting codes tailored for noise-biased systems have been shown to offer…

Quantum annealing is a powerful alternative model for quantum computing, which can succeed in the presence of environmental noise even without error correction. However, despite great effort, no conclusive proof of a quantum speedup…

Quantum Physics · Physics 2023-04-03 Eliot Kapit , Vadim Oganesyan

Randomized benchmarking is a technique for estimating the average fidelity of a set of quantum gates. For general gatesets, however, it is difficult to draw robust conclusions from the resulting data. Here we propose a new method based on…

Quantum Physics · Physics 2019-07-31 Jonas Helsen , Xiao Xue , Lieven M. K. Vandersypen , Stephanie Wehner

Undesired coupling to the surrounding environment destroys long-range correlations on quantum processors and hinders the coherent evolution in the nominally available computational space. This incoherent noise is an outstanding challenge to…

Quantum Physics · Physics 2024-11-07 A. Morvan , B. Villalonga , X. Mi , S. Mandrà , A. Bengtsson , P. V. Klimov , Z. Chen , S. Hong , C. Erickson , I. K. Drozdov , J. Chau , G. Laun , R. Movassagh , A. Asfaw , L. T. A. N. Brandão , R. Peralta , D. Abanin , R. Acharya , R. Allen , T. I. Andersen , K. Anderson , M. Ansmann , F. Arute , K. Arya , J. Atalaya , J. C. Bardin , A. Bilmes , G. Bortoli , A. Bourassa , J. Bovaird , L. Brill , M. Broughton , B. B. Buckley , D. A. Buell , T. Burger , B. Burkett , N. Bushnell , J. Campero , H. S. Chang , B. Chiaro , D. Chik , C. Chou , J. Cogan , R. Collins , P. Conner , W. Courtney , A. L. Crook , B. Curtin , D. M. Debroy , A. Del Toro Barba , S. Demura , A. Di Paolo , A. Dunsworth , L. Faoro , E. Farhi , R. Fatemi , V. S. Ferreira , L. Flores Burgos , E. Forati , A. G. Fowler , B. Foxen , G. Garcia , E. Genois , W. Giang , C. Gidney , D. Gilboa , M. Giustina , R. Gosula , A. Grajales Dau , J. A. Gross , S. Habegger , M. C. Hamilton , M. Hansen , M. P. Harrigan , S. D. Harrington , P. Heu , M. R. Hoffmann , T. Huang , A. Huff , W. J. Huggins , L. B. Ioffe , S. V. Isakov , J. Iveland , E. Jeffrey , Z. Jiang , C. Jones , P. Juhas , D. Kafri , T. Khattar , M. Khezri , M. Kieferová , S. Kim , A. Kitaev , A. R. Klots , A. N. Korotkov , F. Kostritsa , J. M. Kreikebaum , D. Landhuis , P. Laptev , K. -M. Lau , L. Laws , J. Lee , K. W. Lee , Y. D. Lensky , B. J. Lester , A. T. Lill , W. Liu , W. P. Livingston , A. Locharla , F. D. Malone , O. Martin , S. Martin , J. R. McClean , M. McEwen , K. C. Miao , A. Mieszala , S. Montazeri , W. Mruczkiewicz , O. Naaman , M. Neeley , C. Neill , A. Nersisyan , M. Newman , J. H. Ng , A. Nguyen , M. Nguyen , M. Yuezhen Niu , T. E. O'Brien , S. Omonije , A. Opremcak , A. Petukhov , R. Potter , L. P. Pryadko , C. Quintana , D. M. Rhodes , E. Rosenberg , C. Rocque , P. Roushan , N. C. Rubin , N. Saei , D. Sank , K. Sankaragomathi , K. J. Satzinger , H. F. Schurkus , C. Schuster , M. J. Shearn , A. Shorter , N. Shutty , V. Shvarts , V. Sivak , J. Skruzny , W. C. Smith , R. D. Somma , G. Sterling , D. Strain , M. Szalay , D. Thor , A. Torres , G. Vidal , C. Vollgraff Heidweiller , T. White , B. W. K. Woo , C. Xing , Z. J. Yao , P. Yeh , J. Yoo , G. Young , A. Zalcman , Y. Zhang , N. Zhu , N. Zobrist , E. G. Rieffel , R. Biswas , R. Babbush , D. Bacon , J. Hilton , E. Lucero , H. Neven , A. Megrant , J. Kelly , I. Aleiner , V. Smelyanskiy , K. Kechedzhi , Y. Chen , S. Boixo
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