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Quantum mechanical systems lose coherence through interactions with external environments---a process known as decoherence. Although decoherence is detrimental for most of the tasks in quantum information processing, a substantial degree of…

Quantum Physics · Physics 2017-09-28 Chao Lei , Shijie Peng , Chenyong Ju , Man-Hong Yung , Jiangfeng Du

Quantifying the resources available to a quantum computer appears to be necessary to separate quantum from classical computation. Among them, entanglement, nonstabilizerness and coherence are arguably of great significance. We introduce…

Probabilistic quantum filtering is proposed to properly adapt sequential independent quantum channels in order to stop sudden death of entanglement. In the adaptation, the quantum filtering does not distill or purify more entanglement, it…

Quantum Physics · Physics 2009-05-07 Miroslav Gavenda , Radim Filip

We revisit the problem of switching off unwanted phase evolution and decoherence in a single two-state quantum system in the light of recent results on random dynamical decoupling methods [L. Viola and E. Knill, Phys. Rev. Lett. {\bf 94},…

Quantum Physics · Physics 2009-11-11 Lea F. Santos , Lorenza Viola

The decoherence of quantum states defines the transition between the quantum world and classical physics. Decoherence or, analogously, quantum mechanical collapse events pose fundamental questions regarding the interpretation of quantum…

Mesoscale and Nanoscale Physics · Physics 2021-09-24 P. Bredol , H. Boschker , D. Braak , J. Mannhart

We develop dynamical non-Markovian description of quantum computing in weak coupling limit, in lowest order approximation. We show that long range memory of quantum reservoir produces strong interrelation between structure of noise and…

Quantum Physics · Physics 2009-11-07 Robert Alicki , Michal Horodecki , Pawel Horodecki , Ryszard Horodecki

Quantum computers use the quantum interference of different computational paths to enhance correct outcomes and suppress erroneous outcomes of computations. A common pattern underpinning quantum algorithms can be identified when quantum…

Quantum Physics · Physics 2009-10-30 Richard Cleve , Artur Ekert , Chiara Macchiavello , Michele Mosca

Simulating open quantum systems on quantum computers presents a fundamental challenge: open quantum dynamics are intrinsically nonunitary, whereas quantum computers operate through unitary evolution. Conventional approaches overcome this…

Quantum Physics · Physics 2025-10-27 Sameer Dambal , Akira Sone , Yu Zhang

Many problems intractable on classical devices could be solved by algorithms explicitly based on quantum mechanical laws, i.e. exploiting quantum information processing. As a result, increasing efforts from different fields are nowadays…

Quantum Physics · Physics 2024-08-23 Alessandro Chiesa , Emilio Macaluso , Stefano Carretta

Dissipation is commonly regarded as an obstacle to quantum control, as it induces decoherence and irreversibility. Here we demonstrate that dissipation can instead be exploited as a resource to reshape the dynamics of interacting quantum…

Quantum Physics · Physics 2026-02-20 Debabrata Mondal , Lea F. Santos , S. Sinha

We present a nonintrusive method for reliably estimating the noise level during quantum computation and quantum communication protected by quantum error-correcting codes. As preprocessing of quantum error correction, our scheme estimates…

Quantum Physics · Physics 2014-05-27 Yuichiro Fujiwara

When modeling the effects of noise on quantum circuits, one often makes the assumption that these effects can be accounted for by individual decoherence events following an otherwise noise-free gate. In this work, we address the validity of…

Quantum Physics · Physics 2023-12-19 Keith R. Fratus , Juha Leppäkangas , Michael Marthaler , Jan-Michael Reiner

Emerging reinforcement learning techniques using deep neural networks have shown great promise in control optimization. They harness non-local regularities of noisy control trajectories and facilitate transfer learning between tasks. To…

Quantum Physics · Physics 2018-04-17 Murphy Yuezhen Niu , Sergio Boixo , Vadim Smelyanskiy , Hartmut Neven

Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in systems and control…

Systems and Control · Electrical Eng. & Systems 2025-12-23 Jan Schneider , Julian Berberich

Recurrent neural networks play an important role in both research and industry. With the advent of quantum machine learning, the quantisation of recurrent neural networks has become recently relevant. We propose fully quantum recurrent…

Quantum Physics · Physics 2023-01-20 Dmytro Bondarenko , Robert Salzmann , Viktoria-S. Schmiesing

The selection of random subspaces plays a role in quantum information theory analogous to the role of random strings in classical information theory. Recent applications have included protocols achieving the quantum channel capacity and…

Quantum Physics · Physics 2009-11-10 Patrick Hayden

Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention the relevant dynamical control. Here we introduce proof of…

Disordered Systems and Neural Networks · Physics 2022-03-01 Tang-You Huang , Yue Ban , E. Ya. Sherman , Xi Chen

Randomness is a valuable resource in science, cryptography, engineering, and information technology. Quantum-mechanical sources of randomness are attractive because of the indeterminism of individual quantum processes. Here we consider the…

Quantum computing gates are proposed to apply on trapped ions in decoherence-free states. As phase changes due to time evolution of components with different eigenenergies of quantum superposition are completely frozen, quantum computing…

Quantum Physics · Physics 2009-11-07 Mang Feng , Xiaoguang Wang

Quantum machine learning deals with leveraging quantum theory with classic machine learning algorithms. Current research efforts study the advantages of using quantum mechanics or quantum information theory to accelerate learning time or…

Quantum Physics · Physics 2025-09-03 Javier Orduz , Pablo Rivas , Erich Baker