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quantum generative training using rényi divergences

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arXiv 2021年

作者： Kieferová, Mária Marrero, Carlos Ortiz Wiebe, Nathan Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney Data Sciences & Analytics Group Department of Electrical & Computer Engineering Pacific Northwest National Laboratory North Carolina State University Department of Computer Science Department of Physics High Performance Computing Group University of Toronto University of Washington Pacific Northwest National Laboratory

quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning. A major challenge in QNN development is a concentration of measure phenomenon known as a barren plateau that leads to exponentially small gradients for a range of QNNs models. In this work, we examine the assumptions that give rise to barren plateaus and show that an unbounded loss function can circumvent the existing no-go results. We propose a training algorithm that minimizes the maximal Rényi divergence of order two and present techniques for gradient computation. We compute the closed form of the gradients for Unitary QNNs and quantum Boltzmann Machines and provide sufficient conditions for the absence of barren plateaus in these models. We demonstrate our approach in two use cases: thermal state learning and Hamiltonian learning. In our numerical experiments, we observed rapid convergence of our training loss function and frequently archived a 99% average fidelity in fewer than 100 epochs. © 2021, CC BY.

关键词： quantum computers

Improved Weak Lensing Photometric Redshift Calibration via StratLearn and Hierarchical Modeling

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arXiv 2024年

作者： Autenrieth, Maximilian Wright, Angus H. Trotta, Roberto van Dyk, David A. Stenning, David C. Joachimi, Benjamin Department of Mathematics Imperial College London 180 Queen’s Gate LondonSW7 2AZ United Kingdom German Centre for Cosmological Lensing Bochum44780 Germany SISSA International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Department of Physics Imperial College London Blackett Laboratory Prince Consort Rd LondonSW72AZ United Kingdom Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy INFN – National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy Department of Statistics and Actuarial Science Simon Fraser University Canada Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful ΛCDM model to describe the nature of the Universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. In this paper, we improve photo-z calibration via Bayesian hierarchical modeling of full galaxy photo-z conditional densities, by employing StratLearn, a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic training/source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS+VIKING-450 dataset, we show that StratLearn-estimated conditional densities improve the galaxy tomographic bin assignment, and that our StratLearn-Bayesian framework leads to nearly unbiased estimates of the target population means. This leads to a factor of ∼ 2 improvement upon the previously best photo-z calibration method. Our approach delivers a maximum bias per tomographic bin of ∆〈z〉 = 0.0095 ± 0.0089, with an average absolute bias of 0.0052 ± 0.0067 across the five tomographic bins. © 2024, CC BY.

关键词： Galaxies

SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

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arXiv 2024年

作者： Karchev, Konstantin Grayling, Matthew Boyd, Benjamin M. Trotta, Roberto Mandel, Kaisey S. Weniger, Christoph Via Bonomea 265 TriesteI-34136 Italy Institute of Astronomy Kavli Institute for Cosmology Madingley Road CambridgeCB3 0HA United Kingdom Astrophysics Group Physics Department Blackett Lab Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom INFN - National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy Italian Research Center on High-Performance Computing Big Data and Quantum Computing Via Magnanelli 2 BO Casalecchio di RenoI-40033 Italy Statistical Laboratory DPMMS University of Cambridge Wilberforce Road CambridgeCB3 0WB United Kingdom University of Amsterdam Science Park 904 AmsterdamNL-1098 XH Netherlands

We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovæ (SNæ Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilizing truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalize over 4000 latent variables (for a set of ≈100 SNæ Ia) to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortization of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNæ Ia from the Carnegie Supernova Project, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model, e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-Ia contamination, without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples. © 2024, CC BY.

关键词： Supernovae

Heavy hybrid decays to quarkonia

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arXiv 2022年

作者： Brambilla, Nora Lai, Wai Kin Mohapatra, Abhishek Vairo, Antonio Physik-Department Technische Universität München James-Franck-Str. 1 Garching85748 Germany Institute for Advanced Study Technische Universität München Lichtenbergstrasse 2 a Garching85748 Germany Munich Data Science Institute Technische Universität München Walther-von-Dyck-Strasse 10 Garching85748 Germany Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Department of Physics and Astronomy University of California Los AngelesCA90095 United States

The decay rates of the XYZ exotics discovered in the heavy quarkonium sector are crucial observables for identifying the nature of these states. Based on the framework of nonrelativistic effective field theories, we calculate the rates of semi-inclusive decays of heavy quarkonium hybrids into conventional heavy quarkonia. We compute them at leading and subleading power in the inverse of the heavy-quark mass, extending and updating previous results. We compare our predictions with experimental data of inclusive decay rates for candidates of heavy quarkonium hybrids. © 2022, CC BY.

关键词： Hadrons

Strong quantum nonlocality without entanglement in multipartite quantum systems

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Physical Review A 2020年第4期102卷 042228-042228页

作者： Pei Yuan Guojing Tian Xiaoming Sun CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100049 China CAS Center of Excellence in Topological Quantum Computation Beijing 100049 China

In this paper, we generalize the concept of strong quantum nonlocality from two aspects. First, in a tripartite quantum system, we present a construction of strongly nonlocal quantum states containing 6(d−1)2 orthogonal product states in Cd⊗Cd⊗Cd and build 6d2−8d+4 product states in Cd⊗Cd⊗Cd+1, which have been proved to be strongly nonlocal. Obviously, both results turn out to be one order of magnitude smaller than the number of basis states d3 for d≥3. Second, we give explicit forms of strongly nonlocal orthogonal product basis in C3⊗C3⊗C3⊗C3 and C4⊗C4⊗C4⊗C4 quantum systems, where four is the largest known number of subsystems in which there exists strong quantum nonlocality without entanglement up to now. All the results positively answer the open problems raised by Halder et al. [Phys. Rev. Lett. 122, 040403 (2019)]; that is, there do exist a small number of quantum states that can demonstrate strong quantum nonlocality without entanglement.

关键词： Nonlocality

Flat band, tunable chiral anomaly, and pitchfork bifurcation in a honeycomb lattice

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Physical Review B 2024年第19期110卷 195134-195134页

作者： V. A. Saroka F. Kong L. Bogani C. A. Downing R. B. Payod F. R. Fischer Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway Department of Materials University of Oxford 16 Parks Road OX1 3PH Oxford United Kingdom Department of Physics and Astronomy University of Exeter Exeter EX4 4QL United Kingdom Institute of Physics University of the Philippines - Los Baños College Los Baños Laguna 4030 Philippines Department of Chemistry University of California Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Kavli Energy NanoSciences Institute at the University of California Berkeley and the Lawrence Berkeley National Laboratory Berkeley California 94720 USA and Bakar Institute of Digital Materials for the Planet Division of Computing Data Science and Society University of California Berkeley California 94720 USA Department of Electronic Engineering The Chinese University of Hong Kong Shatin New Territories Hong Kong SAR China

Chiral anomaly engineering has been established as one of the mechanisms for achieving energy efficient dissipationless transport. Here we show how to design tunable chiral anomalies in a honeycomb lattice free of any magnetic fields. By applying an external electric field to a flat band material with a broken inversion symmetry, we generate electronic modes exhibiting chiral anomalies capable of disorder resilient transport in the bulk of the material. As the electric field increases, the system exhibits an unusual cubic-like dispersion. By providing a performance comparable to other honeycomb lattice-based ballistic conductors such as armchair nanotube, zigzag nanoribbon and hypothetical cumulenic carbyne, this scheme opens a new research avenue for the design of energy efficient information processing and higher-order dispersion materials.

关键词： Dirac fermions Edge states Electronic structure Flat bands Valley degrees of freedom Valleytronics

Generating Approximate Ground States of Molecules Using quantum Machine Learning

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

Leveraging hardware-control imperfections for error mitigation via generalized quantum subspace

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arXiv 2023年

作者： Ohkura, Yasuhiro Endo, Suguru Satoh, Takahiko Van Meter, Rodney Yoshioka, Nobuyuki Graduate School of Media and Governance Keio University SFC Kanagawa Fujisawa252-0882 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan Keio University Quantum Computing Center Kanagawa Yokohama223-8522 Japan Graduate School of Science and Technology Keio University Kanagawa Yokohama223-8522 Japan Faculty of Environment and Information Studies Keio University SFC Kanagawa Fujisawa252-0882 Japan Department of Applied Physics University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Wako-shi Saitama351-0198 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan

In the era of quantum computing without full fault-tolerance, it is essential to suppress noise effects via the quantum error mitigation techniques to enhance the computational power of the quantum devices. One of the most effective noise-agnostic error mitigation schemes is the generalized quantum subspace expansion (GSE) method, which unifies various mitigation algorithms under the framework of the quantum subspace expansion. Specifically, the fault-subspace method, a subclass of GSE method, constructs an error-mitigated quantum state with copies of quantum states with different noise levels. However, from the experimental aspect, it is nontrivial to determine how to reliably amplify the noise so that the error in the simulation result is efficiently suppressed. In this work, we explore the potential of the fault-subspace method by leveraging the hardware-oriented noise: intentional amplification of the decoherence, noise boost by insertion of identity, making use of crosstalk, and probabilistic implementation of noise channel. We demonstrate the validity of our proposals via both numerical simulations with the noise parameters reflecting those in quantum devices available via IBM quantum, and also experiments performed therein. Copyright © 2023, The Authors. All rights reserved.

关键词： Errors

Quantifying fermionic nonlinearity of quantum circuits

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arXiv 2021年

作者： Hakkaku, Shigeo Tashima, Yuichiro Mitarai, Kosuke Mizukami, Wataru Fujii, Keisuke Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Osaka Toyonaka560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation 3-9-11 Midoricho Musashino180-8585 Japan Center for Quantum Information and Quantum Biology Osaka University 1-2 Machikaneyama Toyonaka560-0043 Japan JST PRESTO 4-1-8 Honcho Saitama Kawaguchi332-0012 Japan Hirosawa 2-1 Saitama Wako351-0198 Japan Fujitsu Quantum Computing Joint Research Division QIQB Osaka University 1-2 Machikaneyama Toyonaka560-0043 Japan

Variational quantum algorithms (VQAs) have been proposed as one of the most promising approaches to demonstrate quantum advantage on noisy intermediate-scale quantum (NISQ) devices. However, it has been unclear whether VQAs can maintain quantum advantage under the intrinsic noise of the NISQ devices, which deteriorates the quantumness. Here we propose a measure, called fermionic nonlinearity, to quantify the classical simulatability of quantum circuits designed for simulating fermionic Hamiltonians. Specifically, we construct a Monte Carlo type classical algorithm based on the classical simulatability of fermionic linear optics, whose sampling overhead is characterized by the fermionic nonlinearity. As a demonstration of these techniques, we calculate the upper bound of the fermionic nonlinearity of a rotation gate generated by four fermionic modes under the dephasing noise. Moreover, we estimate the sampling costs of the unitary coupled cluster singles and doubles quantum circuits for hydrogen chains subject to the dephasing noise. We find that, depending on the error probability and atomic spacing, there are regions where the fermionic nonlinearity becomes very small or unity, and hence the circuits are classically simulatable. We believe that our method and results help to design quantum circuits for fermionic systems with potential quantum advantages. Copyright © 2021, The Authors. All rights reserved.

关键词： Timing circuits