E. van den Berg
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View article: Measuring central charge on a universal quantum processor
Measuring central charge on a universal quantum processor Open
This repository contains the error-mitigated experimental data obtained from IBM quantum processors and the post-processing code for extracting the central charge, as well as numerical simulations, which are used in the paper Measuring cen…
View article: Efficient Lindblad synthesis for noise model construction
Efficient Lindblad synthesis for noise model construction Open
Effective noise models are essential for analyzing and understanding the dynamics of quantum systems, particularly in applications like quantum error mitigation and correction. However, even when noise processes are well-characterized in i…
View article: Techniques for learning sparse Pauli-Lindblad noise models
Techniques for learning sparse Pauli-Lindblad noise models Open
Error-mitigation techniques such as probabilistic error cancellation and zero-noise extrapolation benefit from accurate noise models. The sparse Pauli-Lindblad noise model is one of the most successful models for those applications. In exi…
View article: Error mitigation with stabilized noise in superconducting quantum processors
Error mitigation with stabilized noise in superconducting quantum processors Open
Pre-fault tolerant quantum computers have already demonstrated the ability to estimate observable values accurately, at a scale beyond brute-force classical computation. This has been enabled by error mitigation techniques that often rely …
View article: Techniques for learning sparse Pauli-Lindblad noise models
Techniques for learning sparse Pauli-Lindblad noise models Open
Error-mitigation techniques such as probabilistic error cancellation and zero-noise extrapolation benefit from accurate noise models. The sparse Pauli-Lindblad noise model is one of the most successful models for those applications. In exi…
View article: Probabilistic error cancellation for dynamic quantum circuits
Probabilistic error cancellation for dynamic quantum circuits Open
Probabilistic error cancellation (PEC) is a technique that generates error-mitigated estimates of expectation values from ensembles of quantum circuits. In this work we extend the application of PEC from unitary-only circuits to dynamic ci…
View article: Single-shot error mitigation by coherent Pauli checks
Single-shot error mitigation by coherent Pauli checks Open
Generating samples from the output distribution of a quantum circuit is a ubiquitous task used as a building block of many quantum algorithms. Here we show how to accomplish this task on a noisy quantum processor lacking full-blown error c…
View article: Evidence for the utility of quantum computing before fault tolerance
Evidence for the utility of quantum computing before fault tolerance Open
Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely accepted…
View article: Single-shot error mitigation by coherent Pauli checks
Single-shot error mitigation by coherent Pauli checks Open
Generating samples from the output distribution of a quantum circuit is a ubiquitous task used as a building block of many quantum algorithms. Here we show how to accomplish this task on a noisy quantum processor lacking full-blown error c…
View article: Model-free readout-error mitigation for quantum expectation values
Model-free readout-error mitigation for quantum expectation values Open
Measurements on current quantum processors are subject to hardware\nimperfections that lead to readout errors. These errors manifest themselves as\na bias in quantum expectation values. Here, we propose a very simple method\nthat forces th…
View article: Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors
Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors Open
Noise in pre-fault-tolerant quantum computers can result in biased estimates of physical observables. Accurate bias-free estimates can be obtained using probabilistic error cancellation (PEC), which is an error-mitigation technique that ef…
View article: A simple method for sampling random Clifford operators
A simple method for sampling random Clifford operators Open
We describe a simple algorithm for sampling $n$-qubit Clifford operators uniformly at random. The algorithm outputs the Clifford operators in the form of quantum circuits with at most $5n + 2n^2$ elementary gates and a maximum depth of $\m…
View article: How high schools teach quantum physics – a cross-national analysis of curricula in secondary education
How high schools teach quantum physics – a cross-national analysis of curricula in secondary education Open
Quantum physics (QP) changed our worldview, it brought us modern electronic devices, and its almost mythical image fascinates. Although QP is relatively new in secondary education, it is now part of the national curricula of many countries…
View article: Model-free readout-error mitigation for quantum expectation values
Model-free readout-error mitigation for quantum expectation values Open
Measurements on current quantum processors are subject to hardware imperfections that lead to readout errors. These errors manifest themselves as a bias in quantum expectation values. Here, we propose a very simple method that forces the b…
View article: Iterative quantum phase estimation with optimized sample complexity
Iterative quantum phase estimation with optimized sample complexity Open
In this work we consider practical implementations of Kitaev's algorithm for\nquantum phase estimation. We analyze the use of phase shifts that simplify the\nestimation of successive bits in the estimation of unknown phase $\\varphi$. By\n…
View article: A simple method for sampling random Clifford operators
A simple method for sampling random Clifford operators Open
We describe a simple algorithm for sampling $n$-qubit Clifford operators uniformly at random. The algorithm outputs the Clifford operators in the form of quantum circuits with at most $5n + 2n^2$ elementary gates and a maximum depth of $\m…
View article: On sets of commuting and anticommuting Paulis
On sets of commuting and anticommuting Paulis Open
In this work we study the structure and cardinality of maximal sets of commuting and anticommuting Paulis in the setting of the abelian Pauli group. We provide necessary and sufficient conditions for anticommuting sets to be maximal, and p…
View article: The Ocean Tensor Package
The Ocean Tensor Package Open
The Ocean Tensor Package is an open-source package for matrix and tensor operations on CPU and GPU. The package aims to serve as a foundational layer for applications that require dense tensor operations on a variety of device types. All o…
View article: A hybrid quasi-Newton projected-gradient method with application to Lasso and basis-pursuit denoising
A hybrid quasi-Newton projected-gradient method with application to Lasso and basis-pursuit denoising Open
We propose a new algorithm for the optimization of convex functions over a polyhedral set in $${\mathbb {R}}^n$$. The algorithm extends the spectral projected-gradient method with limited-memory BFGS iterates restricted to the present face…
View article: Analysis of secondary school quantum physics curricula of 15 different countries: Different perspectives on a challenging topic
Analysis of secondary school quantum physics curricula of 15 different countries: Different perspectives on a challenging topic Open
Secondary school level quantum physics (QP) courses have recently been implemented in the national curricula of many countries. QP gives opportunities to acquaint students with more recent physics and its applications and to discuss aspect…
View article: The Ocean Tensor Package
The Ocean Tensor Package Open
Matrix and tensor operations form the basis of a wide range of fields and applications, and in many cases constitute a substantial part of the overall computational complexity. The ability of general-purpose GPUs to speed up many of these …
View article: Estimating Information Flow in Deep Neural Networks
Estimating Information Flow in Deep Neural Networks Open
We study the flow of information and the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory. The theory suggests that DNN trainin…
View article: Training variance and performance evaluation of neural networks in speech
Training variance and performance evaluation of neural networks in speech Open
In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition. Although this variance itself is well known, this is, to the best of our knowledge, the first pap…
View article: A Hybrid Quasi-Newton Projected-Gradient Method with Application to Lasso and Basis-Pursuit Denoise
A Hybrid Quasi-Newton Projected-Gradient Method with Application to Lasso and Basis-Pursuit Denoise Open
We propose a new algorithm for the optimization of convex functions over a polyhedral set in Rn. The algorithm extends the spectral projected-gradient method with limited-memory BFGS iterates restricted to the present face whenever possibl…
View article: Training variance and performance evaluation of neural networks in speech
Training variance and performance evaluation of neural networks in speech Open
In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition. Although this variance itself is well known, this is, to the best of our knowledge, the first pap…
View article: Some Insights into the Geometry and Training of Neural Networks
Some Insights into the Geometry and Training of Neural Networks Open
Neural networks have been successfully used for classification tasks in a rapidly growing number of practical applications. Despite their popularity and widespread use, there are still many aspects of training and classification that are n…
View article: SLOPE—Adaptive variable selection via convex optimization
SLOPE—Adaptive variable selection via convex optimization Open
We introduce a new estimator for the vector of coefficients β in the linear model y = Xβ + z, where X has dimensions n × p with p possibly larger than n. SLOPE, short for Sorted L-One Penalized Estimation, is the solution to [Formula: see …