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High fidelity quantum state transfer is an essential part of quantum information processing. In this regard, we address the problem of maximizing the fidelity in a quantum state transformation process satisfying the Liouville-von Neumann…

Quantum Physics · Physics 2022-03-11 Nahid Binandeh Dehaghani , Fernando Lobo Pereira

Verifying the proper preparation of quantum states is essential in modern quantum information science. Various protocols have been developed to estimate the fidelity of quantum states produced by different parties. Direct fidelity…

Quantum Physics · Physics 2024-12-11 Christopher Vairogs , Bin Yan

Quantum key distribution (QKD) based on coherent states is well known for its implementation simplicity, but it suffers from loss-dependent attacks based on optimal unambiguous state discrimination. Crucially, previous research has…

Quantum Physics · Physics 2020-06-05 Li Liu , Yukun Wang , Emilien Lavie , Arno Ricou , Chao Wang , Fen Zhuo Guo , Charles Ci Wen Lim

Fidelity is a figure of merit widely employed in quantum technology in order to quantify similarity between quantum states and, in turn, to assess quantum resources or reconstruction techniques. Fidelities higher than, say, 0.9 or 0.99, are…

Quantum Physics · Physics 2015-06-17 Matteo Bina , Antonio Mandarino , Stefano Olivares , Matteo G. A. Paris

We employ quantum-volume random-circuit sampling to benchmark the two-QPU entanglement-assisted distributed quantum computing (DQC) and compare it with single-QPU quantum computing. We first specify a single-qubit depolarizing noise model…

Quantum Physics · Physics 2024-06-12 Shao-Hua Hu , George Biswas , Jun-Yi Wu

The quantum threshold theorem shows that a noisy quantum computer can accurately and efficiently simulate any ideal quantum computation provided that noise is weakly correlated and its strength is below a critical value known as the quantum…

Quantum Physics · Physics 2011-07-13 Panos Aliferis

We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking…

We present a simple proof of the approximate Eastin-Knill theorem, which connects the quality of a quantum error-correcting code (QECC) with its ability to achieve a universal set of transversal logical gates. Our derivation employs…

Quantum Physics · Physics 2021-04-21 Aleksander Kubica , Rafal Demkowicz-Dobrzanski

Determining the optimal fidelity for the transmission of quantum information over noisy quantum channels is one of the central problems in quantum information theory. Recently, [Berta-Borderi-Fawzi-Scholz, Mathematical Programming, 2021]…

Quantum Physics · Physics 2026-04-21 Yeow Meng Chee , Hoang Ta , Van Khu Vu

As quantum devices become more complex and the requirements on these devices become more demanding, it is crucial to be able to verify the performance of such devices in a scalable and reliable fashion. A cornerstone task in this challenge…

Recent progress in machine learning (ML) has made high-accuracy quantum chemistry (QC) calculations more accessible. Of particular interest are multifidelity machine learning (MFML) methods where training data from differing accuracies or…

Chemical Physics · Physics 2025-03-26 Vivin Vinod , Peter Zaspel

For a given ensemble of input and target states, the classical fidelity threshold (CFT) is the maximum valve of the averaged fidelity, and it can be achieved with a measure-and-prepare operation. This quantity can be employed to verify…

Quantum Physics · Physics 2018-07-17 Long Huang , Xiaohua Wu , Tao Zhou

To perform reliable quantum computation, quantum error correction is indispensable. In certain cases, continuous covariance symmetry of the physical system can make exact error correction impossible. In this work we study the approximate…

Quantum Physics · Physics 2023-08-25 Hao Dai

We present a data-driven approach to the quantitative verification of probabilistic programs and stochastic dynamical models. Our approach leverages neural networks to compute tight and sound bounds for the probability that a stochastic…

Logic in Computer Science · Computer Science 2026-04-22 Alessandro Abate , Alec Edwards , Mirco Giacobbe , Hashan Punchihewa , Diptarko Roy

Similar to the classical Internet, the quantum Internet will require knowledge regarding link qualities used for purposes such as optimal route selection. This is commonly accomplished by performing link-level tomography with or without…

Quantum Physics · Physics 2019-08-30 Takaaki Matsuo

Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing setting, a shared backbone is combined with task-specific…

Machine Learning · Computer Science 2026-04-16 Hevish Cowlessur , Chandra Thapa , Tansu Alpcan , Seyit Camtepe

Quantum-like modeling (QLM) - quantum theory applications outside of physics - are intensively developed with applications in biology, cognition, psychology, and decision-making. For cognition, QLM should be distinguished from quantum…

Neurons and Cognition · Quantitative Biology 2025-12-16 Andrei Khrennikov , Makiko Yamada

Quantum computers require high-fidelity measurement of many qubits to achieve a quantum advantage. Traditional approaches suffer from readout crosstalk for a neutral-atom quantum processor with a tightly spaced array. Although classical…

Quantum repeater networks have attracted attention for the implementation of long-distance and large-scale sharing of quantum states. Recently, researchers extended classical network coding, which is a technique for throughput enhancement,…

Quantum Physics · Physics 2016-03-09 Takahiko Satoh , Kaori Ishizaki , Shota Nagayama , Rodney Van Meter

Inference for continuous-time Markov chains (CTMCs) becomes challenging when the process is only observed at discrete time points. The exact likelihood is intractable, and existing methods often struggle even in medium-dimensional…

Methodology · Statistics 2025-07-23 Tao Tang , Lachlan Astfalck , David Dunson