Quantifying Qubit Magic Resource with Gottesman-Kitaev-Preskill Encoding

Hahn, Oliver; Ferraro, Alessandro; Hultquist, Lina; Ferrini, Giulia; García-Álvarez, Laura

doi:10.1103/physrevlett.128.210502

Cited by 15 publications

(2 citation statements)

References 52 publications

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“…These measures are, however, computationally expensive to evaluate and their calculation requires exact knowledge of the wave-function combined with complex optimization [10], which excludes the study of large quantum circuits. More recently introduced magic monotonotes such as the Gottesman-Kitaev-Preskill magic measure and the stablizer Renyi entropy, [18,19], offer simplified scaling which enables exact calculation of magic for a few qubits using conventional computers.…”

Section: Introductionmentioning

confidence: 99%

Quantifying non-stabilizerness via information scrambling

Ahmadi,

Greplova

2024

SciPost Phys.

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The advent of quantum technologies brought forward much attention to the theoretical characterization of the computational resources they provide. A method to quantify quantum resources is to use a class of functions called magic monotones and stabilizer entropies, which are, however, notoriously hard and impractical to evaluate for large system sizes. In recent studies, a fundamental connection between information scrambling, the magic monotone mana and 2-Renyi stabilizer entropy was established. This connection simplified magic monotone calculation, but this class of methods still suffers from exponential scaling with respect to the number of qubits. In this work, we establish a way to sample an out-of-time-order correlator that approximates magic monotones and 2-Renyi stabilizer entropy. We numerically show the relation of these sampled correlators to different non-stabilizerness measures for both qubit and qutrit systems and provide an analytical relation to 2-Renyi stabilizer entropy. Furthermore, we put forward and simulate a protocol to measure the monotonic behaviour of magic for the time evolution of local Hamiltonians.

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Section: Introductionmentioning

confidence: 99%

Quantifying non-stabilizerness via information scrambling

Ahmadi,

Greplova

2024

SciPost Phys.

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“…This means that the power of quantum advance requires resources beyond the Clifford group, like the Phase π/8 gate (T gate) or the Toffoli gate and non-Gaussian states for the MCGs 14,15 . The precious resource that makes quantum computers special is colloquially dubbed as 'magic' and a resource theory of magic has been developed in recent years [2][3][4][16][17][18][19][20][21][22][23][24][25][26][27] .…”

Section: Introductionmentioning

confidence: 99%

Measuring magic on a quantum processor

et al. 2022

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Magic states are the resource that allows quantum computers to attain an advantage over classical computers. This resource consists in the deviation from a property called stabilizerness which in turn implies that stabilizer circuits can be efficiently simulated on a classical computer. Without magic, no quantum computer can do anything that a classical computer cannot do. Given the importance of magic for quantum computation, it would be useful to have a method for measuring the amount of magic in a quantum state. In this work, we propose and experimentally demonstrate a protocol for measuring magic based on randomized measurements. Our experiments are carried out on two IBM Quantum Falcon processors. This protocol can provide a characterization of the effectiveness of a quantum hardware in producing states that cannot be effectively simulated on a classical computer. We show how from these measurements one can construct realistic noise models affecting the hardware.

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Connecting Continuous and Discrete Wigner Functions Via GKP Encoding

Feng,

Luo

2024

Int J Theor Phys

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Quantifying Qubit Magic Resource with Gottesman-Kitaev-Preskill Encoding

Cited by 15 publications

References 52 publications

Quantifying non-stabilizerness via information scrambling

Quantifying non-stabilizerness via information scrambling

Measuring magic on a quantum processor

Connecting Continuous and Discrete Wigner Functions Via GKP Encoding

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