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20182026
most citedEfficient Learning for Deep Quantum Neural Networks

712 citations · 771 across the 19 of their papers we have counts for

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20 papers · 1 filter

quant-ph2026

Hypercontractivity and entropy decay for infinite-dimensional quantum Gibbs samplers

Simon Becker, Cambyse Rouzé, Robert Salzmann

We derive rates of exponential relative-entropy decay for a broad class of infinite-dimensional quantum Gibbs samplers. A key technical ingredient is a perturbative stability theor…

quant-ph2026

Discriminating Lindbladian Dynamics

Robert Salzmann, Ludovico Lami, Martin B. Plenio +1

The fundamental task of quantum hypothesis testing, which consists in discriminating quantum objects, has been extensively studied for quantum states and channels. Here, we investi…

quant-ph2026

Transmitting algebras through quantum channels

Robert Salzmann, Satvik Singh

We develop a theory of exact transmission of finite-dimensional -algebras through quantum channels. These algebras describe hybrid classical-quantum information, allowing the…

quant-ph2026

Computing the free energy of quantum Coulomb gases and molecules via quantum Gibbs sampling

Simon Becker, Cambyse Rouzé, Robert Salzmann

We develop a quantum Gibbs-sampling approach to free-energy estimation for trapped systems of distinguishable particles with Coulomb interactions in dimensions . The s…

quant-ph2026

Simulating Thermal Properties of Bose-Hubbard Models on a Quantum Computer

Simon Becker, Cambyse Rouzé, Robert Salzmann

While recent advances have established efficient quantum algorithms for preparing Gibbs states of finite-dimensional systems, comparable complexity results for bosonic and other in…

quant-ph2026

Quantum Gibbs Sampling in Infinite Dimensions: Generation, Mixing Times and Circuit Implementation

Simon Becker, Cambyse Rouzé, Robert Salzmann

We develop a rigorous and implementable framework for Gibbs sampling of infinite-dimensional quantum systems governed by unbounded Hamiltonians. Extending dissipative Gibbs sampler…

quant-ph2024

Quantitative Quantum Zeno and Strong Damping Limits in Strong Topology

Robert Salzmann

Frequent applications of a mixing quantum operation to a quantum system slow down its time evolution and eventually drive it into the invariant subspace of the named operation. We…

quant-ph2024★ 1 cited

Optimal Fidelity Estimation from Binary Measurements for Discrete and Continuous Variable Systems

Omar Fawzi, Aadil Oufkir, Robert Salzmann

Estimating the fidelity between a desired target quantum state and an actual prepared state is essential for assessing the success of experiments. For pure target states, we use fu…

quant-ph2024

Robustness of Fixed Points of Quantum Channels and Application to Approximate Quantum Markov Chains

Robert Salzmann, Bjarne Bergh, Nilanjana Datta

Given a quantum channel and a state which satisfy a fixed point equation approximately (say, up to an error ), can one find a new channel and a state, which are respec…

quant-ph2024★ 7 cited

An invitation to the sample complexity of quantum hypothesis testing

Hao-Chung Cheng, Nilanjana Datta, Nana Liu +3

Quantum hypothesis testing (QHT) has been traditionally studied from the information-theoretic perspective, wherein one is interested in the optimal decay rate of error probabiliti…

quant-ph2023

Infinite Dimensional Asymmetric Quantum Channel Discrimination

Bjarne Bergh, Jan Kochanowski, Robert Salzmann +1

We study asymmetric binary channel discrimination, for qantum channels acting on separable Hilbert spaces. We establish quantum Stein's lemma for channels for both adaptive and par…

quant-ph2023★ 3 cited

Composite Classical and Quantum Channel Discrimination

Bjarne Bergh, Nilanjana Datta, Robert Salzmann

We study the problem of binary composite channel discrimination in the asymmetric setting, where the hypotheses are given by fairly arbitrary sets of channels, and samples do not h…

quant-ph2023★ 3 cited

Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks

Dmytro Bondarenko, Robert Salzmann, Viktoria-S. Schmiesing

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 becom…

quant-ph2022★ 11 cited

Parallelization of Adaptive Quantum Channel Discrimination in the Non-Asymptotic Regime

Bjarne Bergh, Nilanjana Datta, Robert Salzmann +1

We investigate the performance of parallel and adaptive quantum channel discrimination strategies for a finite number of channel uses. It has recently been shown that, in the asymm…

quant-ph2022★ 1 cited

Total insecurity of communication via strong converse for quantum privacy amplification

Robert Salzmann, Nilanjana Datta

Quantum privacy amplification is a central task in quantum cryptography. Given shared randomness, which is initially correlated with a quantum system held by an eavesdropper, the g…

quant-ph2021★ 1 cited

Interpolating between symmetric and asymmetric hypothesis testing

Robert Salzmann, Nilanjana Datta

The task of binary quantum hypothesis testing is to determine the state of a quantum system via measurements on it, given the side information that it is in one of two possible sta…

quant-ph2021★ 3 cited

The Limit of the Sharp Quantum Rényi Divergence

Bjarne Bergh, Robert Salzmann, Nilanjana Datta

Fawzi and Fawzi recently defined the sharp Rényi divergence, , for , as an additional quantum Rényi divergence with nice mathematical properties and appli…

quant-ph2021★ 11 cited

Symmetric distinguishability as a quantum resource

Robert Salzmann, Nilanjana Datta, Gilad Gour +2

We develop a resource theory of symmetric distinguishability, the fundamental objects of which are elementary quantum information sources, i.e., sources that emit one of two possib…

quant-ph2020★ 16 cited

Quantum Zeno effect for open quantum systems

Simon Becker, Nilanjana Datta, Robert Salzmann

We prove the quantum Zeno effect in open quantum systems whose evolution, governed by quantum dynamical semigroups, is repeatedly and frequently interrupted by the action of a quan…

quant-ph2019★ 712 cited

Efficient Learning for Deep Quantum Neural Networks

Kerstin Beer, Dmytro Bondarenko, Terry Farrelly +3

Neural networks enjoy widespread success in both research and industry and, with the imminent advent of quantum technology, it is now a crucial challenge to design quantum neural n…