Simulating the transverse-field Ising model on the kagome lattice using a programmable quantum annealer

Narasimhan, Pratyankara; Humeniuk, Stephan; Roy, Ananda; Drouin-Touchette, Victor

doi:10.1103/physrevb.110.054432

Phys. Rev. B

2024

DOI: 10.1103/physrevb.110.054432

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Simulating the transverse-field Ising model on the kagome lattice using a programmable quantum annealer

Pratyankara Narasimhan,

Stephan Humeniuk,

Ananda Roy

et al.

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2024

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Cited by 2 publications

References 74 publications

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Quantum quench dynamics of geometrically frustrated Ising models

Ali,

Xu,

Bernoudy

et al. 2024

Nat Commun

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Geometric frustration in two-dimensional Ising models allows for a wealth of exotic universal behavior, both Ising and non-Ising, in the presence of quantum fluctuations. In particular, the triangular antiferromagnet and Villain model in a transverse field can be understood through distinct XY pseudospins, but have qualitatively similar phase diagrams including a quantum phase transition in the (2+1)-dimensional XY universality class. While the quantum dynamics of modestly-sized systems can be simulated classically using tensor-based methods, these methods become infeasible for larger lattices. Here we perform both classical and quantum simulations of these dynamics, where our quantum simulator is a superconducting quantum annealer. Our observations on the triangular lattice suggest that the dominant quench dynamics are not described by the quantum Kibble-Zurek scaling of the quantum phase transition, but rather a faster coarsening dynamics in an effective two-dimensional XY model in the ordered phase. Similarly, on the Villain model, the scaling exponent does not match the Kibble-Zurek expectation. These results demonstrate the ability of quantum annealers to perform coherent quantum dynamics simulations that are hard to classically scale beyond small systems, and open the avenue to predictive simulations of the dynamics of Ising magnetic materials on quantum simulators.

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Quantum quench dynamics of geometrically frustrated Ising models

Ali,

Xu,

Bernoudy

et al. 2024

Nat Commun

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Synergy between noisy quantum computers and scalable classical deep learning for quantum error mitigation

Cantori,

Mari,

Vitali

et al. 2024

EPJ Quantum Technol.

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We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurately predict exact expectation values of parameterized quantum circuits representing the Trotter-decomposed dynamics of quantum Ising models. By incorporating (simulated) noisy expectation values alongside circuit structure information, our CNNs effectively capture the underlying relationships between circuit architecture and output behaviour, enabling, via transfer learning, also predictions for circuits with more qubits than those included in the training set. Notably, thanks to the quantum information, our CNNs succeed even when supervised learning based only on classical descriptors fails. Furthermore, they outperform a popular error mitigation scheme, namely, zero-noise extrapolation, demonstrating that the synergy between quantum and classical computational tools leads to higher accuracy compared with quantum-only or classical-only approaches. By tuning the noise strength, we explore the crossover from a computationally powerful classical CNN assisted by quantum noisy data, towards rather precise quantum computations, further error-mitigated via classical deep learning.

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Simulating the transverse-field Ising model on the kagome lattice using a programmable quantum annealer

Cited by 2 publications

References 74 publications

Quantum quench dynamics of geometrically frustrated Ising models

Quantum quench dynamics of geometrically frustrated Ising models

Synergy between noisy quantum computers and scalable classical deep learning for quantum error mitigation

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