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Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspecification and the use…

Machine Learning · Computer Science 2018-07-03 Volodymyr Kuleshov , Nathan Fenner , Stefano Ermon

Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates…

Quantum error correction allows for faulty quantum systems to behave in an effectively error free manner. One important class of techniques for quantum error correction is the class of quantum subsystem codes, which are relevant both to…

Quantum Physics · Physics 2013-05-29 Gregory M. Crosswhite , Dave Bacon

Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce…

Computer Vision and Pattern Recognition · Computer Science 2018-01-17 Huaijin Chen , Jinwei Gu , Orazio Gallo , Ming-Yu Liu , Ashok Veeraraghavan , Jan Kautz

Introducing quantum sensors as solution to real-world problem demands reliability and controllability outside laboratory conditions. Producers and operators ought to be assumed to have limited resources ready available for calibration, and…

Image processing is one of the most promising applications for quantum machine learning (QML). Quanvolutional Neural Networks with non-trainable parameters are the preferred solution to run on current and near future quantum devices. The…

Quantum Physics · Physics 2024-10-10 Daniele Lizzio Bosco , Beatrice Portelli , Giuseppe Serra

Efficient and accurate decoding of quantum error-correcting codes is essential for fault-tolerant quantum computation, however, it is challenging due to the degeneracy of errors, the complex code topology, and the large space for logical…

Quantum Physics · Physics 2025-03-28 Hanyan Cao , Feng Pan , Dongyang Feng , Yijia Wang , Pan Zhang

Scalable quantum information processing requires the ability to tune multi-qubit interactions. This makes the precise manipulation of quantum states particularly difficult for multi-qubit interactions because tunability unavoidably…

For a simple model of mutually interacting qubits it is shown how the errors induced by mutual interactions can be eliminated using concatenated coding. The model is solved exactly for arbitrary interaction strength, for two well-known…

Quantum Physics · Physics 2009-11-06 Julio Gea-Banacloche

Characterising the time over which quantum coherence survives is critical for any implementation of quantum bits, memories and sensors. The usual method for determining a quantum system's decoherence rate involves a suite of experiments…

Although Gaussian processes (GPs) with deep kernels have been successfully used for meta-learning in regression tasks, its uncertainty estimation performance can be poor. We propose a meta-learning method for calibrating deep kernel GPs for…

Machine Learning · Statistics 2023-12-14 Tomoharu Iwata , Atsutoshi Kumagai

Decoherence is the main problem to be solved before quantum computers can be built. To control decoherence, it is possible to use error correction methods, but these methods are themselves noisy quantum computation processes. In this work…

Quantum Physics · Physics 2009-11-07 Pedro J. Salas , Angel L. Sanz

Quantum nondemolition (QND) measurements are a precious resource for quantum information processing. Repetitive QND measurements can boost the fidelity of qubit preparation and measurement, even when the underlying single-shot measurements…

Calibration$\unicode{x2014}$the problem of ensuring that predicted probabilities align with observed class frequencies$\unicode{x2014}$is a basic desideratum for reliable prediction with machine learning systems. Calibration error is…

Machine Learning · Statistics 2026-03-02 Eugène Berta , Sacha Braun , David Holzmüller , Francis Bach , Michael I. Jordan

Quantum error correction (QEC) is an essential element of physical quantum information processing systems. Most QEC efforts focus on extending classical error correction schemes to the quantum regime. The input to a noisy system is embedded…

Quantum Physics · Physics 2009-11-13 Andrew S. Fletcher , Peter W. Shor , Moe Z. Win

Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different character than simulated data, and most MLP training…

With the rapid developments in quantum hardware comes a push towards the first practical applications on these devices. While fully fault-tolerant quantum computers may still be years away, one may ask if there exist intermediate forms of…

Quantum Physics · Physics 2020-02-19 Jarrod R. McClean , Zhang Jiang , Nicholas C. Rubin , Ryan Babbush , Hartmut Neven

Two recent developments in quantum control, concatenation and optimization of pulse intervals, are combined to yield a strategy to suppress unwanted couplings in quantum systems to high order. Longitudinal relaxation and transverse…

Quantum Physics · Physics 2009-04-09 Götz S. Uhrig

Dynamical decoupling (DD) is a widely-used quantum control technique that takes advantage of temporal symmetries in order to partially suppress quantum errors without the need resource-intensive error detection and correction protocols.…

The present thesis deals with various methods of quantum error correction. It is divided into two parts. In the first part, dynamical decoupling methods are considered which have the task of suppressing the influence of residual…

Quantum Physics · Physics 2009-09-30 Oliver Kern