Finite-size criticality in fully connected spin models on superconducting quantum hardware

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

doi:10.1103/physreve.107.024113

Cited by 11 publications

(2 citation statements)

References 85 publications

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“…as often used in the context of the variational quantum eigensolver [45][46][47][48]. Moreover, it has been reported that adding physical inspired constraints results in more reliable quantum technology protocols [49][50][51].…”

Section: Extension For Spin System Applicationsmentioning

confidence: 99%

Counterdiabatic optimized driving in quantum phase sensitive models

Barone,

Kiss,

Grossi

et al. 2024

New J. Phys.

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State preparation plays a pivotal role in numerous quantum algorithms, including quantum phase estimation. This paper extends and benchmarks counterdiabatic driving protocols across three one-dimensional spin systems characterized by phase transitions: the axial next-nearest neighbor Ising, XXZ, and Haldane-Shastry models. We perform a shallow quantum optimal control over the counterdiabatic protocols by optimizing an energy cost function. Moreover, we provide a code package for computing symbolically various adiabatic gauge potentials. This protocol consistently surpasses standard annealing schedules, often achieving performance improvements of several orders of magnitude. The the axial next-nearest neighbor Ising model stands out as a notable example, where fidelities exceeding 0.5 are attainable in most cases. Furthermore, the optimized paths exhibit promising generalization capabilities to higher-dimensional systems, allowing for the extension of parameters from smaller models. Nevertheless, our investigations reveal limitations in the case of the XXZ and Haldane-Shastry models, particularly when transitioning away from the ferromagnetic phase. This suggests that finding optimal diabatic gauge potentials for specific systems remains an important research direction.

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Section: Extension For Spin System Applicationsmentioning

confidence: 99%

Counterdiabatic optimized driving in quantum phase sensitive models

Barone,

Kiss,

Grossi

et al. 2024

New J. Phys.

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“…On the other hand, the resource requirements for quantum simulations are only subject to polynomial growth in many practical circumstances, as in the simulation of local Hamiltonians [3], and more particularly in spin chains [4]. Hence, quantum computers offer a natural paradigm for Hamilto-Oriel Kiss: oriel.kiss@cern.ch nian simulations, with numerous applications in nuclear [5][6][7] and condensed matter physics [8][9][10][11][12], quantum field theory [13][14][15] and quantum chemistry [16][17][18][19][20]. For instance, quantum simulations have been applied to the computation of energy levels via quantum phase estimation [21], chemical reaction rates predictions [22], correlation functions [8,9,23,24], neutrino oscillations [25,26] and scattering experiments [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Importance sampling for stochastic quantum simulations

Kiss

Grossi

Roggero

2023

Quantum

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Simulating many-body quantum systems is a promising task for quantum computers. However, the depth of most algorithms, such as product formulas, scales with the number of terms in the Hamiltonian, and can therefore be challenging to implement on near-term, as well as early fault-tolerant quantum 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 coefficients. In this work, we unify the qDrift protocol with importance sampling, allowing us to sample from arbitrary probability 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.

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