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Non-native quantum Generative Optimization with Adversarial Autoencoders

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arXiv 2024年

作者： Wilson, Blake A. Wurtz, Jonathan Mkhitaryan, Vahagn Bezick, Michael Wang, Sheng-Tao Kais, Sabre Shalaev, Vladimir M. Boltasseva, Alexandra Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Quantum Science Center Oak Ridge National Laboratory Oak RidgeTN37831 United States QuEra Computing Inc. 1284 Soldiers Field Road BostonMA02135 United States School of Computer Science Purdue University West LafayetteIN47907 United States Department of Chemistry Purdue University West LafayetteIN47907 United States

Large-scale optimization problems are prevalent in several fields, including engineering, finance, and logistics. However, most optimization problems cannot be efficiently encoded onto a physical system because the existing quantum samplers have too few qubits. Another typical limiting factor is that the optimization constraints are not compatible with the native cost Hamiltonian. This work presents a new approach to address these challenges. We introduce the adversarial quantum autoencoder model (AQAM) that can be used to map large-scale optimization problems onto existing quantum samplers while simultaneously optimizing the problem through latent quantum-enhanced Boltzmann sampling. We demonstrate the AQAM on a neutral atom sampler, and showcase the model by optimizing 64px×64px unit cells that represent a broad-angle filter metasurface applicable to improving the coherence of neutral atom devices. Using 12-atom simulations, we demonstrate that the AQAM achieves a lower Renyi divergence and a larger spectral gap when compared to classical Markov Chain Monte Carlo samplers. Our work paves the way to more efficient mapping of conventional optimization problems into existing quantum samplers. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization

Unpolarized transverse momentum dependent parton distributions of the nucleon from lattice QCD

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Physical Review D 2024年第11期109卷 114513-114513页

作者： Jin-Chen He Min-Huan Chu Jun Hua Xiangdong Ji Andreas Schäfer Yushan Su Wei Wang Yi-Bo Yang Jian-Hui Zhang Qi-An Zhang Yang Yuanqing Scientific Computering Center Tsung-Dao Lee Institute Shanghai Key Laboratory for Particle Physics and Cosmology Key Laboratory for Particle Astrophysics and Cosmology (MOE) School of Physics and Astronomy Department of Physics Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center Institut für Theoretische Physik Southern Center for Nuclear-Science Theory (SCNT) CAS Key Laboratory of Theoretical Physics School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China Beijing/Hangzhou China School of Physical Sciences School of Science and Engineering Center of Advanced Quantum Studies Department of Physics School of Physics

We present a first lattice QCD calculation of the unpolarized nucleon’s isovector transverse-momentum-dependent parton distribution functions (TMDPDFs), which are essential to predict observables of multiscale, semi-inclusive processes in the standard model. We use a Nf=2+1+1 MILC ensemble with valence clover fermions on a highly improved staggered quark (HISQ) sea to compute the quark momentum distributions in a large-momentum nucleon on the lattice. The state-of-the-art techniques in renormalization and extrapolation in the correlation distance on the lattice are adopted. The perturbative kernel up to next-to-next-to-leading order is taken into account, and the dependence on the pion mass and the hadron momentum is explored. Our results are qualitatively comparable with phenomenological TMDPDFs, which provide an opportunity to predict high energy scatterings from first principles.

关键词： Lattice QCD Nucleon distribution Parton distribution functions

Repairing Deep Neural Networks Based on Behavior Imitation

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arXiv 2023年

作者： Liang, Zhen Wu, Taoran Zhao, Changyuan Liu, Wanwei Xue, Bai Yang, Wenjing Wang, Ji Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China

The increasing use of deep neural networks (DNNs) in safety-critical systems has raised concerns about their potential for exhibiting ill-behaviors. While DNN verification and testing provide post hoc conclusions regarding unexpected behaviors, they do not prevent the erroneous behaviors from occurring. To address this issue, DNN repair/patch aims to eliminate unexpected predictions generated by defective DNNs. Two typical DNN repair paradigms are retraining and fine-tuning. However, existing methods focus on the high-level abstract interpretation or inference of state spaces, ignoring the underlying neurons' outputs. This renders patch processes computationally prohibitive and limited to piecewise linear (PWL) activation functions to great extent. To address these shortcomings, we propose a behavior-imitation based repair framework, BIRDNN, which integrates the two repair paradigms for the first time. BIRDNN corrects incorrect predictions of negative samples by imitating the closest expected behaviors of positive samples during the retraining repair procedure. For the fine-tuning repair process, BIRDNN analyzes the behavior differences of neurons on positive and negative samples to identify the most responsible neurons for the erroneous behaviors. To tackle more challenging domain-wise repair problems (DRPs), we synthesize BIRDNN with a domain behavior characterization technique to repair buggy DNNs in a probably approximated correct style. We also implement a prototype tool based on BIRDNN and evaluate it on ACAS Xu DNNs. Our experimental results show that BIRDNN can successfully repair buggy DNNs with significantly higher efficiency than state-of-the-art repair tools. Additionally, BIRDNN is highly compatible with different activation *** Codes 68N99, , 68T99 Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

THE SUBFIELD METRIC AND ITS APPLICATION TO quantum ERROR CORRECTION

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arXiv 2022年

作者： Grassl, Markus Horlemann, Anna-Lena Weger, Violetta International Centre for Theory of Quantum Technologies University of Gdansk Poland School and Institute of Computer Science University of St.Gallen Switzerland Institute for Communications Engineering Technical University of Munich Germany

We introduce a new weight and corresponding metric over finite extension fields for asymmetric error correction. The weight distinguishes between elements from the base field and the ones outside of it, which is motivated by asymmetric quantum codes. We set up the theoretic framework for this weight and metric, including upper and lower bounds, asymptotic behavior of random codes, and we show the existence of an optimal family of codes achieving the Singleton-type upper bound. Copyright © 2022, The Authors. All rights reserved.

关键词： Error correction

Synthesis and Arithmetic of Single Qutrit Circuits

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arXiv 2023年

作者： Kalra, Amolak Ratan Mosca, Michele Valluri, Dinesh Institute for Quantum Computing University of Waterloo WaterlooON Canada David R. Cheriton School of Computer Science University of Waterloo WaterlooON Canada Perimeter Institute for Theoretical Physics WaterlooON Canada Dept. of Combinatorics & Optimization University of Waterloo WaterlooON Canada

In this paper we study single qutrit circuits consisting of words over the Clifford+D cyclotomic gate set, where D = diag(±ξa, ±ξb, ±ξc), ξ is a primitive 9-th root of unity and a, b, c are integers. We characterize classes of qutrit unit vectors z with entries in [ξ, χ1 ] based on the possibility of reducing their smallest denominator exponent (sde) with respect to χ := 1 − ξ, by acting an appropriate gate in Clifford+D. We do this by studying the notion of ‘derivatives mod 3’ of an arbitrary element of [ξ] and using it to study the smallest denominator exponent of HDz where H is the qutrit Hadamard gate and D ∈ D. In addition, we reduce the problem of finding all unit vectors of a given sde to that of finding integral solutions of a positive definite quadratic form along with some additional constraints. As a consequence we prove that the Clifford+D gates naturally arise as gates with sde 0 and 3 in the group U(3, [ξ, χ1 ]) of 3 × 3 unitaries with entries in [ξ, χ1 ]. We illustrate the general applicability of these methods to obtain an exact synthesis algorithm for Clifford+R and recover the previous exact synthesis algorithm in [14]. The framework developed to formulate qutrit gate synthesis for Clifford+D extends to qudits of arbitrary prime power. © 2023, CC BY.

关键词： Digital arithmetic

Finite-size analysis of prepare-and-measure and decoy-state QKD via entropy accumulation

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arXiv 2024年

作者： Kamin, Lars Arqand, Amir George, Ian Lütkenhaus, Norbert Tan, Ernest Y.-Z. Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States

An important goal in quantum key distribution (QKD) is the task of providing a finite-size security proof without the assumption of collective attacks. For prepare-and-measure QKD, one approach for obtaining such proofs is the generalized entropy accumulation theorem (GEAT), but thus far it has only been applied to study a small selection of protocols. In this work, we present techniques for applying the GEAT in finite-size analysis of generic prepare-and-measure protocols, with a focus on decoy-state protocols. In particular, we present an improved approach for computing entropy bounds for decoy-state protocols, which has the dual benefits of providing tighter bounds than previous approaches (even asymptotically) and being compatible with methods for computing min-tradeoff functions in the GEAT. Furthermore, we develop methods to incorporate some improvements to the finite-size terms in the GEAT, and implement techniques to automatically optimize the min-tradeoff function. Our approach also addresses some numerical stability challenges specific to prepare-and-measure protocols, which were not addressed in previous works. © 2024, CC BY.

关键词： quantum cryptography

Matrix manipulations via unitary transformations and ancilla-state measurements

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arXiv 2023年

作者： Zenchuk, Alexander I. Qi, Wentao Kumar, Asutosh Wu, Junde Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry RAS Moscow reg. Chernogolovka142432 Russia Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Physics Gaya College Magadh University Rampur Gaya823001 India School of Mathematical Sciences Zhejiang University Hangzhou310027 China

We propose protocols for calculating inner product, matrix addition and matrix multiplication based on multiqubit Toffoli-type and the simplest one-qubit operations and employ ancilla-measurements to remove all garbage of calculations. The depth (runtime) of addition protocol is O(1) and that of other protocols logarithmically increases with the dimensionality of the considered matrices. © 2023, CC BY-NC-ND.

关键词： Matrix algebra

quantum dense coding network using multimode squeezed states of light

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Physical Review A 2022年第5期106卷 052607-052607页

作者： Ayan Patra Rivu Gupta Saptarshi Roy Tamoghna Das Aditi Sen(De) Harish-Chandra Research Institute A CI of Homi Bhabha National Institute Chhatnag Road Jhunsi Prayagraj 211019 India Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong International Centre for Theory of Quantum Technologies University of Gdańsk 80-952 Gdańsk Poland

We present a framework of a multimode dense coding network with multiple senders and a single receiver using continuous variable systems. The protocol is scalable to arbitrary numbers of modes with the encoding being displacements while the decoding involves homodyne measurements of the modes after they are combined in a pairwise manner by a sequence of beam splitters, thereby exhibiting its potentiality to implement in laboratories with currently available resources. We compute the closed form expression of the dense coding capacity for the cases of two and three senders that involve sharing of three- and four-mode states, respectively. The dense coding capacity is computed with the constraint of fixed average energy transmission when the modes of the sender are transferred to the receiver after the encoding operation. In both the cases, we demonstrate the quantum advantage of the protocol using paradigmatic classes of three- and four-mode states. The quantum advantage increases with the increase in the amount of energy that is allowed to be transmitted from the senders to the receiver.

关键词： quantum channels quantum communication quantum information processing with continuous variables quantum networks

A Geometrical Characterization on Feature Density of Image Datasets

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A Geometrical Characterization on Feature Density of Image D...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhen Liang Changyuan Zhao Wanwei Liu Bai Xue Wenjing Yang Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China

Recently, the interpretability and verification of deep learning have attracted enormous attention from both academic and industrial communities, aiming to gain users’ trust and ease their concerns. To guide learning procedures or data operations carried out in a more interpretable way, in this paper, we put a similar perspective on image datasets, the inputs of deep learning. Based on manifold learning, we work out an interpretable geometrical characterization on the curvity of manifolds to depict the feature density of datasets, which is represented with the ratio of the Euclidean distance and the geodesic distance. It is a noteworthy characteristic of image datasets and we take the dataset compression and enhancement problems as application instances via sample credit assignment with the geometrical information. Experiments on typical image datasets have justified the effectiveness and enormous prospect of the presented geometrical characteristic.

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