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  1. Article ; Online: Finite-size criticality in fully connected spin models on superconducting quantum hardware.

    Grossi, Michele / Kiss, Oriel / De Luca, Francesco / Zollo, Carlo / Gremese, Ian / Mandarino, Antonio

    Physical review. E

    2023  Volume 107, Issue 2-1, Page(s) 24113

    Abstract: The emergence of a collective behavior in a many-body system is responsible for the quantum criticality separating different phases of matter. Interacting spin systems in a magnetic field offer a tantalizing opportunity to test different approaches to ... ...

    Abstract The emergence of a collective behavior in a many-body system is responsible for the quantum criticality separating different phases of matter. Interacting spin systems in a magnetic field offer a tantalizing opportunity to test different approaches to study quantum phase transitions. In this work, we exploit the new resources offered by quantum algorithms to detect the quantum critical behavior of fully connected spin-1/2 models. We define a suitable Hamiltonian depending on an internal anisotropy parameter γ that allows us to examine three paradigmatic examples of spin models, whose lattice is a fully connected graph. We propose a method based on variational algorithms run on superconducting transmon qubits to detect the critical behavior for systems of finite size. We evaluate the energy gap between the first excited state and the ground state, the magnetization along the easy axis of the system, and the spin-spin correlations. We finally report a discussion about the feasibility of scaling such approach on a real quantum device for a system having a dimension such that classical simulations start requiring significant resources.
    Language English
    Publishing date 2023-03-17
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.107.024113
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Importance sampling for stochastic quantum simulations

    Kiss, Oriel / Grossi, Michele / Roggero, Alessandro

    2022  

    Abstract: Simulating complex quantum systems is a promising task for digital quantum computers. However, the depth of popular product formulas scales with the number of summands in the Hamiltonian, which can therefore be challenging to implement on near-term as ... ...

    Abstract Simulating complex quantum systems is a promising task for digital quantum computers. However, the depth of popular product formulas scales with the number of summands in the Hamiltonian, which can therefore be challenging to implement on near-term as well as fault-tolerant devices. An efficient solution is given by the stochastic compilation protocol known as qDrift, which builds random product formulas by sampling from the Hamiltonian according to the magnitude of their coefficients. In this work, we unify the qDrift protocol with importance sampling, allowing us to sample from arbitrary distributions while controlling both the bias as well as the statistical fluctuations. We show that the simulation cost can be reduced while achieving the same accuracy by considering the individual simulation cost during the sampling stage. Moreover, we incorporate recent work on composite channel and compute rigorous bounds on the bias and variance showing how to choose the number of samples, experiments, and time steps for a given target accuracy. These results lead to a more efficient implementation of the qDrift protocol, both with and without the use of composite channels. Theoretical results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.

    Comment: 15 pages, 10 pages supplemental material
    Keywords Quantum Physics
    Subject code 612
    Publishing date 2022-12-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Quantum neural networks force fields generation

    Kiss, Oriel / Tacchino, Francesco / Vallecorsa, Sofia / Tavernelli, Ivano

    2022  

    Abstract: Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in predicting accurate ... ...

    Abstract Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in predicting accurate values for energy and forces when trained on finite size ensembles generated with ab initio techniques. At the same time, quantum computers have recently started to offer new viable computational paradigms to tackle such problems. On the one hand, quantum algorithms may notably be used to extend the reach of electronic structure calculations. On the other hand, quantum machine learning is also emerging as an alternative and promising path to quantum advantage. Here we follow this second route and establish a direct connection between classical and quantum solutions for learning neural network potentials. To this end, we design a quantum neural network architecture and apply it successfully to different molecules of growing complexity. The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances, thus pointing towards potential quantum advantages in natural science applications via quantum machine learning.

    Comment: 12 pages, 7 figures
    Keywords Quantum Physics ; Computer Science - Machine Learning ; Physics - Chemical Physics ; Physics - Computational Physics
    Subject code 541
    Publishing date 2022-03-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Conditional Born machine for Monte Carlo event generation

    Kiss, Oriel / Grossi, Michele / Kajomovitz, Enrique / Vallecorsa, Sofia

    2022  

    Abstract: Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as a random source. So called Born machines are purely quantum models and promise to generate probability distributions in ... ...

    Abstract Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as a random source. So called Born machines are purely quantum models and promise to generate probability distributions in a quantum way, inaccessible to classical computers. This paper presents an application of Born machines to Monte Carlo simulations and extends their reach to multivariate and conditional distributions. Models are run on (noisy) simulators and IBM Quantum superconducting quantum hardware. More specifically, Born machines are used to generate muonic force carrier (MFC) events resulting from scattering processes between muons and the detector material in high-energy physics colliders experiments. MFCs are bosons appearing in beyond-the-standard-model theoretical frameworks, which are candidates for dark matter. Empirical evidence suggests that Born machines can reproduce the marginal distributions and correlations of data sets from Monte Carlo simulations.

    Comment: 12 pages, 9 figures, 6 tables
    Keywords Quantum Physics ; Computer Science - Machine Learning ; High Energy Physics - Experiment
    Subject code 541
    Publishing date 2022-05-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Hybrid Ground-State Quantum Algorithms based on Neural Schr\"odinger Forging

    de Schoulepnikoff, Paulin / Kiss, Oriel / Vallecorsa, Sofia / Carleo, Giuseppe / Grossi, Michele

    2023  

    Abstract: Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis ... ...

    Abstract Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis states, or bitstrings, when performing the Schmidt decomposition of the whole system. To overcome this challenge, we propose a new method for entanglement forging employing generative neural networks to identify the most pertinent bitstrings, eliminating the need for the exponential sum. Through empirical demonstrations on systems of increasing complexity, we show that the proposed algorithm achieves comparable or superior performance compared to the existing standard implementation of entanglement forging. Moreover, by controlling the amount of required resources, this scheme can be applied to larger, as well as non permutation invariant systems, where the latter constraint is associated with the Heisenberg forging procedure. We substantiate our findings through numerical simulations conducted on spins models exhibiting one-dimensional ring, two-dimensional triangular lattice topologies, and nuclear shell model configurations.

    Comment: 12 pages, 9 figures, 5 pages supplemental material
    Keywords Quantum Physics ; Condensed Matter - Statistical Mechanics ; Computer Science - Machine Learning
    Subject code 000
    Publishing date 2023-07-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Trainability barriers and opportunities in quantum generative modeling

    Rudolph, Manuel S. / Lerch, Sacha / Thanasilp, Supanut / Kiss, Oriel / Vallecorsa, Sofia / Grossi, Michele / Holmes, Zoë

    2023  

    Abstract: Quantum generative models, in providing inherently efficient sampling strategies, show promise for achieving a near-term advantage on quantum hardware. Nonetheless, important questions remain regarding their scalability. In this work, we investigate the ... ...

    Abstract Quantum generative models, in providing inherently efficient sampling strategies, show promise for achieving a near-term advantage on quantum hardware. Nonetheless, important questions remain regarding their scalability. In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration. We explore the interplay between explicit and implicit models and losses, and show that using implicit generative models (such as quantum circuit-based models) with explicit losses (such as the KL divergence) leads to a new flavour of barren plateau. In contrast, the Maximum Mean Discrepancy (MMD), which is a popular example of an implicit loss, can be viewed as the expectation value of an observable that is either low-bodied and trainable, or global and untrainable depending on the choice of kernel. However, in parallel, we highlight that the low-bodied losses required for trainability cannot in general distinguish high-order correlations, leading to a fundamental tension between exponential concentration and the emergence of spurious minima. We further propose a new local quantum fidelity-type loss which, by leveraging quantum circuits to estimate the quality of the encoded distribution, is both faithful and enjoys trainability guarantees. Finally, we compare the performance of different loss functions for modelling real-world data from the High-Energy-Physics domain and confirm the trends predicted by our theoretical results.

    Comment: 20+32 pages, 9+2 figures
    Keywords Quantum Physics ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Statistics - Machine Learning
    Subject code 190
    Publishing date 2023-05-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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