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We present a classical algorithm based on Pauli propagation for estimating expectation values of arbitrary observables on random unstructured quantum circuits across all circuit architectures and depths, including those with all-to-all…

Present-day, noisy, small or intermediate-scale quantum processors---although far from fault-tolerant---support the execution of heuristic quantum algorithms, which might enable a quantum advantage, for example, when applied to…

Noise in existing quantum processors only enables an approximation to ideal quantum computation. However, these approximations can be vastly improved by error mitigation, for the computation of expectation values, as shown by small-scale…

Quantum computers have now surpassed classical simulation limits, yet noise continues to limit their practical utility. As the field shifts from proof-of-principle demonstrations to early deployments, there is no standard method for…

Quantum Physics · Physics 2025-05-29 J. A. Montanez-Barrera , Kristel Michielsen , David E. Bernal Neira

Parameterized quantum circuits (PQCs) play an essential role in the application of variational quantum algorithms (VQAs) in noisy intermediate-scale quantum (NISQ) devices. The PQCs are a leading candidate to achieve a quantum advantage in…

Quantum Physics · Physics 2025-10-10 Joona V. Pankkonen , Matti Raasakka , Andrea Marchesin , Ilkka Tittonen

The rapid development of noisy intermediate-scale quantum (NISQ) devices has raised the question of whether or not these devices will find commercial use. Unfortunately, a major shortcoming of many proposed NISQ-amenable algorithms, such as…

Quantum Physics · Physics 2021-10-22 Amara Katabarwa , Alex Kunitsa , Borja Peropadre , Peter Johnson

Characterizing and mitigating errors in current noisy intermediate-scale devices is important to improve performance of next generations of quantum hardware. In order to investigate the importance of the different noise mechanisms affecting…

Quantum Physics · Physics 2023-02-14 Gabriele Cenedese , Giuliano Benenti , Maria Bondani

Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive reliance on entanglement. To address these limitations, we…

Quantum Physics · Physics 2025-03-04 Seongmin Kim , In-Saeng Suh

A general method to mitigate the effect of errors in quantum circuits is outlined. The method is developed in sight of characteristics that an ideal method should possess and to ameliorate an existing method which only mitigates state…

Quantum Physics · Physics 2020-11-24 Manpreet Singh Jattana , Fengping Jin , Hans De Raedt , Kristel Michielsen

The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for combinatorial optimisation. Several obstacles…

Quantum Physics · Physics 2026-02-26 Simon Thelen , Hila Safi , Wolfgang Mauerer

Variational quantum algorithms are the centerpiece of modern quantum programming. These algorithms involve training parameterized quantum circuits using a classical co-processor, an approach adapted partly from classical machine learning.…

Quantum Physics · Physics 2022-11-09 V. Akshay , H. Philathong , E. Campos , D. Rabinovich , I. Zacharov , Xiao-Ming Zhang , J. Biamonte

Extracting useful information from noisy near-term quantum simulations requires error mitigation strategies. A broad class of these strategies rely on precise characterization of the noise source. We study the robustness of probabilistic…

Quantum Physics · Physics 2025-07-28 Pradeep Niroula , Sarang Gopalakrishnan , Michael J. Gullans

Noise in quantum hardware remains the biggest roadblock for the implementation of quantum computers. To fight the noise in the practical application of near-term quantum computers, instead of relying on quantum error correction which…

Quantum Physics · Physics 2021-10-14 Zhenyu Cai

Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant…

Quantum Physics · Physics 2024-06-05 Zichang He , Bo Peng , Yuri Alexeev , Zheng Zhang

Classical verification of quantum learning allows classical clients to reliably leverage quantum computing advantages by interacting with untrusted quantum servers. Yet, current quantum devices available in practice suffers from a variety…

Quantum Physics · Physics 2024-11-15 Yinghao Ma , Jiaxi Su , Dong-Ling Deng

One of the crucial steps in building a scalable quantum computer is to identify the noise sources which lead to errors in the process of quantum evolution. Different implementations come with multiple hardware-dependent sources of noise and…

Quantum Physics · Physics 2021-06-02 Daniel Stilck França , Sergii Strelchuk , Michał Studziński

Quantum error mitigation is a critical technology for extracting reliable computations from noisy quantum processors, proving itself essential not only in the near term but also as a valuable supplement to fully fault-tolerant systems in…

Quantum Physics · Physics 2025-10-24 Dayue Qin , Ying Li , You Zhou

Variational quantum algorithms are tailored to perform within the constraints of current quantum devices, yet they are limited by performance-degrading errors. In this study, we consider a noise model that reflects realistic gate errors…

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…

We investigate (a) spontaneous Raman scattering of classical signals in long optical fiber links leading to significant Raman noise in quantum channels propagating in the same fiber and (b) impact of chromatic dispersion on the performance…

Quantum Physics · Physics 2017-12-19 Michal Mlejnek , Nikolay A. Kaliteevskiy , Dan A. Nolan
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