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A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to…

Quantum Physics · Physics 2025-07-18 Jaden Nola , Uriah Sanchez , Anusha Krishna Murthy , Elizabeth Behrman , James Steck

The numerical version of the Hamilton-Jacobi quantization method, recently proposed, is applied to the one dimensional quartic oscillator. A suitable quantization condition is formulated and various energy levels and wave functions are…

Quantum Physics · Physics 2017-11-28 Mario Fusco Girard

Information-Theoretical restrictions on the information transfer are applied to Quantum Measurements. For the measurement of quantum object S by information system O this restrictions are calculated in Algebraic QM formalism as the…

Quantum Physics · Physics 2007-05-23 S. Mayburov

Scattering of electromagnetic waves by many small particles of arbitrary shapes is reduced rigorously to solving linear algebraic system of equations bypassing the usual usage of integral equations. The matrix elements of this linear…

Mathematical Physics · Physics 2009-11-11 A. G. Ramm

This paper introduces a robust and scalable framework for implementing nested affine transformations in quantum circuits. Utilizing Hadamard-supported conditional initialization and block encoding, the proposed method systematically applies…

Quantum Physics · Physics 2026-04-28 Anish Giri , David Hyde , Kalman Varga

We develop an approach for the treatment of one--dimensional bounded quantum--mechanical models by straightforward modification of a successful method for unbounded ones. We apply the new approach to a simple example and show that it…

Mathematical Physics · Physics 2009-11-13 Francisco M. Fernández

Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency,…

Computer Vision and Pattern Recognition · Computer Science 2020-08-14 Kuan Wang , Zhijian Liu , Yujun Lin , Ji Lin , Song Han

Deep Neural Networks reached state-of-the-art performance across numerous domains, but this progress has come at the cost of increasingly large and over-parameterized models, posing serious challenges for deployment on resource-constrained…

Machine Learning · Computer Science 2026-02-04 Dario Malchiodi , Mattia Ferraretto , Marco Frasca

Systems with constraints pose problems when they are quantized. Moreover, the Dirac procedure of quantization prior to reduction is preferred. The projection operator method of quantization, which can be most conveniently described by…

Quantum Physics · Physics 2007-05-23 John R. Klauder

This paper introduces a new notion of quantum recursion of which the control flow of the computation is quantum rather than classical as in the notions of recursion considered in the previous studies of quantum programming. A typical…

Quantum Physics · Physics 2014-08-07 Mingsheng Ying

This article has one single purpose: introduce a new and simple, yet highly insightful approach to capture, fully and quantitatively, the dynamics of the circular flow of income in economies. The proposed approach relies mostly on basic…

General Economics · Economics 2023-07-07 Aziz Guergachi , Javid Hakim

As quantum parallelism allows the effective co-representation of classical mutually exclusive states, the diagonalization method of classical recursion theory has to be modified. Quantum diagonalization involves unitary operators whose…

High Energy Physics - Theory · Physics 2010-04-14 Karl Svozil

In this paper, we study extended linear regression approaches for quantum state tomography based on regularization techniques. For unknown quantum states represented by density matrices, performing measurements under certain basis yields…

Quantum Physics · Physics 2019-04-29 Biqiang Mu , Hongsheng Qi , Ian R. Petersen , Guodong Shi

Bayesian inference of gravitational wave signals is subject to systematic error due to modelling uncertainty in waveform signal models, coined approximants. A growing collection of approximants are available which use different approaches…

General Relativity and Quantum Cosmology · Physics 2020-03-25 Gregory Ashton , Sebastian Khan

The controller design of the so-called "difference algebraic equation" (DAE) systems that are frequently shown in industrial processes, tend to be challenging because of the combination of algebraic equations and high state dimensions. In…

Systems and Control · Computer Science 2017-03-16 Fei Chen

An effective formalism for quantum constrained systems is presented which allows manageable derivations of solutions and observables, including a treatment of physical reality conditions without requiring full knowledge of the physical…

Mathematical Physics · Physics 2009-03-12 Martin Bojowald , Barbara Sandhoefer , Aureliano Skirzewski , Artur Tsobanjan

In this paper we present a simple and computationally efficient quantization scheme that enables us to reduce the resolution of the parameters of a neural network from 32-bit floating point values to 8-bit integer values. The proposed…

Machine Learning · Computer Science 2016-12-20 Raziel Alvarez , Rohit Prabhavalkar , Anton Bakhtin

An algebraic approach to the study of quantum mechanics on configuration spaces with a finite fundamental group is presented. It uses, in an essential way, the Gelfand-Naimark and Serre-Swan equivalences and thus allows one to represent…

Mathematical Physics · Physics 2011-12-30 A. F. Reyes-Lega

In this article we give an implementation of the standard algorithm to segment a real algebraic plane curve defined implicitly. Our implementation is efficient and simpler than previous. We use global information to count the number of…

Algebraic Geometry · Mathematics 2016-05-24 Cesar Massri , Manuel Dubinsky

Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regarded as machine learning models with remarkable expressive…

Quantum Physics · Physics 2019-11-15 Marcello Benedetti , Erika Lloyd , Stefan Sack , Mattia Fiorentini