Photonic quantum simulations of coupled 
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-symmetric Hamiltonians

Maraviglia, Nicola; Yard, Patrick; Wakefield, Ross; Carolan, Jacques; Sparrow, Chris; Chakhmakhchyan, Levon; Harrold, Christopher; Hashimoto, Toshikazu; Matsuda, Nobuyuki; Harter, Andrew K.; Joglekar, Yogesh N.; Laing, Anthony

doi:10.1103/physrevresearch.4.013051

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…Exploring correlated physics in open quantum systems attracts great interest mainly because of the systematic treatment of loss and gain in these systems, which quantitatively reproduces experimental observations [51][52][53][54][55]. In recent years, along with brute force studies of open quantum systems, understanding their effective descriptions based on non-Hermitian physics get momentum [56][57][58][59][60][61]. The studies of 4) and ( 5)).…”

Section: Introductionmentioning

confidence: 89%

Transfer learning from Hermitian to non-Hermitian quantum many-body physics

Sayyad,

Lado

2024

J. Phys.: Condens. Matter

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Identifying phase boundaries of interacting systems is one of the key steps to understanding quantum many-body models. The development of various numerical and analytical methods has allowed exploring the phase diagrams of many Hermitian interacting systems. However, numerical challenges and scarcity of analytical solutions hinder obtaining phase boundaries in non-Hermitian many-body models. Recent machine learning methods have emerged as a potential strategy to learn phase boundaries from various observables without having access to the full many-body wavefunction. Here, we show that a machine learning methodology trained solely on Hermitian correlation functions allows identifying phase boundaries of non-Hermitian interacting models. These results demonstrate that Hermitian machine learning algorithms can be redeployed to non-Hermitian models without requiring further training to reveal non-Hermitian phase diagrams. Our findings establish transfer learning as a versatile strategy to leverage Hermitian physics to machine learning non-Hermitian phenomena.

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Section: Introductionmentioning

confidence: 89%

Transfer learning from Hermitian to non-Hermitian quantum many-body physics

Sayyad,

Lado

2024

J. Phys.: Condens. Matter

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“…Thus, realizing PT-symmetric photonic systems in the quantum domain necessitates a shift to passive systems . Such passive PT systems recently enabled the very first observations of PT-symmetric quantum interference and the quantum simulation of coupled PT-symmetric Hamiltonians on a photonic platform, which opened up the exploration of quantum correlations in larger non-Hermitian networks.…”

Section: Introductionmentioning

confidence: 99%

Order-Invariant Two-Photon Quantum Correlations in PT-Symmetric Interferometers

Wolterink,

Heinrich,

Scheel

et al. 2023

ACS Photonics

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Multiphoton correlations in linear photonic quantum networks are governed by matrix permanents. Yet, surprisingly few systematic properties of these crucial algebraic objects are known. As such, predicting the overall multiphoton behavior of a network from its individual building blocks typically defies intuition. In this work, we identify sequences of concatenated two-mode linear optical transformations whose two-photon behavior is invariant under reversal of the order. We experimentally verify this systematic behavior in parity-time-symmetric complex interferometer arrangements of varying compositions. Our results underline new ways in which quantum correlations may be preserved in counterintuitive ways, even in small-scale non-Hermitian networks.

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“…Note that the above two-dimensional Hamiltonian has been used in different contexts ranging from the effective Hamiltonian in the open quantum systems to the simulation of  -symmetric systems with photonic devices [50][51][52][53]. Let us discuss how this Hamiltonian can represent an effective Hamiltonian describing the evolution of a system that interacts with an external environment.…”

mentioning

confidence: 99%

Necessary condition for information transfer under simulated parity-time-symmetric evolution

Lakkaraju,

Mal,

Sen (De)

2024

Phys. Scr.

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Parity-time $(\mathcal{PT})$ symmetric quantum theory can broaden the scope of quantum dynamics beyond unitary evolution which may lead to numerous counter-intuitive phenomena, including single-shot discrimination of non-orthogonal states, faster evolution of state than the standard quantum speed limit, and violation of no-signaling principle. On the other hand, $\mathcal{PT}$-symmetric evolution can be realized as reduced dynamics of a subsystem in real experiments within the scope of standard QT. In this experimental setup, if one side of a composite system is evolved according to a $\mathcal{PT}$-symmetric way, a non-trivial information transfer can happen, i.e., the operation performed at one side can be gathered by the other side. By considering an arbitrary shared state between two parties situated in two distant locations and arbitrary measurements, we show that the $\mathcal{PT}$-symmetric evolution of the reduced subsystem at one side is not sufficient for this information transfer to occur. Specifically, we prove that the information transfer can only happen when the density matrix and the corresponding measurements contain complex numbers. Moreover, we connect the entanglement content of the shared state with the efficacy of information transfer. We find evidence that the task becomes more efficient with the increase of dimension. 

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Photonic quantum simulations of coupled PT -symmetric Hamiltonians

Cited by 10 publications

References 45 publications

Transfer learning from Hermitian to non-Hermitian quantum many-body physics

Transfer learning from Hermitian to non-Hermitian quantum many-body physics

Order-Invariant Two-Photon Quantum Correlations in PT-Symmetric Interferometers

Necessary condition for information transfer under simulated parity-time-symmetric evolution

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