Generating Haar-Uniform Randomness Using Stochastic Quantum Walks on a Photonic Chip

Tang, Hao; Banchi, Leonardo; Wang, Tianyu; Shang, Xiao-Wen; Tan, Xi; Zhou, Wen‐Hao; Feng, Zhen; Pal, Anurag; Li, Hang; Hu, Cheng-Qiu; Kim, M S; Jin, Xian-Min

doi:10.1103/physrevlett.128.050503

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…We then turn to a more challenging scenario of open QW interacting with a simulated environment ( 19 , 54 ). Decoherence can be introduced by inserting an extra phase gate trueR̂ (β)=ei2normalβnormalσtruê z with a fast fluctuating phase β ∈ [ − δβ, δβ] into each step of coherent QW.…”

Section: Resultsmentioning

confidence: 99%

“…While as a prototypical dynamical process, the open QW can model the dissipative evolution of quantum neural networks ( 11 , 12 ), and this simulation enables better performance to process various issues such as pattern recognition ( 13 ). Adding controlled noise into the quantum evolution, QW can be dynamically initialized in any high-dimensional form ( 14 – 17 ) and generate the Haar random unitary operators ( 18 , 19 ) required for quantum computation. Implementing the open QW and validating these noise-assisted computational and simulated performances demand a complete density-matrix characterization.…”

Section: Introductionmentioning

confidence: 99%

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Efficient learning of mixed-state tomography for photonic quantum walk

Wang,

Dong,

et al. 2024

Sci. Adv.

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Noise-enhanced applications in open quantum walk (QW) has recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this challenge, we present a neural-network–based method for reconstructing mixed states with a high fidelity (∼97.5%) while costing only 50% of the number of measurements typically required for open discrete-time QW in one dimension. Our method uses a neural density operator that models the system and environment, followed by a generalized natural gradient descent procedure that significantly speeds up the training process. Moreover, we introduce a compact interferometric measurement device, improving the scalability of our photonic QW setup that enables experimental learning of mixed states. Our results demonstrate that highly expressive neural networks can serve as powerful alternatives to traditional state tomography.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient learning of mixed-state tomography for photonic quantum walk

Wang,

Dong,

et al. 2024

Sci. Adv.

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“…In fact, a true simulation of quantum systems is only feasible on another quantum system, called quantum simulator, which is normally less complex and more controllable than the system of interest [1]. Thanks to recent advancements in quantum technologies, quantum simulators are now emerging in various physical systems, including cold atoms [2][3][4][5], superconducting devices [6][7][8][9][10][11][12][13], ion-tarps [14][15][16], Rydberg atoms [17][18][19][20] and optical systems [21][22][23]. However, current noisy intermediate-scale quantum (NISQ) simulators are far from being perfect [24].…”

Section: Introductionmentioning

confidence: 99%

“…Digital quantum simulators, in NISQ era, come with different levels of qubit connectivity and thus limited interactions between their qubits. In some physical setups, such as superconducting systems [6][7][8][9][10][11][12][13] or cold atoms in optical lattices [21][22][23], the connectivity is determined by the geometry of the simulator and is normally restricted to nearest neighbors. In other platforms, such as ion-traps [14][15][16] and Rydberg atoms [17][18][19][20], the connectivity is more versatile and in principle interactions can be induced between any pair of qubits.…”

Section: Introductionmentioning

confidence: 99%

Variational quantum simulation of long-range interacting systems

Lyu

Tang

et al. 2023

New J. Phys.

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Current quantum simulators suffer from multiple limitations such as short coherence time, noisy operations, faulty readout and restricted qubit connectivity in some platforms. Variational quantum algorithms are the most promising approach in near-term quantum simulation to achieve practical quantum advantage over classical computers. Here, we explore variational quantum algorithms, with different levels of qubit connectivity, for digital simulation of the ground state of long-range interacting systems as well as generation of spin squeezed states. We find that as the interaction becomes more long-ranged, the variational algorithms become less efficient, achieving lower fidelity and demanding more optimization iterations. In particular, when the system is near its criticality the efficiency is even lower. Increasing the connectivity between distant qubits improves the results, even with less quantum and classical resources. Our results show that by mixing circuit layers with different levels of connectivity one can sensibly improve the performance. Interestingly, the order of layers becomes very important and grouping the layers with long-distance connectivity at the beginning of the circuit outperforms other permutations. The same design of circuits can also be used to variationally produce spin squeezed states, as a resource for quantum metrology.

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“…It is experimentally simulated using an asymmetric three-dimensional (3D) photonic lattice, for the first time, to uncover how the excitation energy propagates between the seven BChla molecules, including both neighboring and non-neighboring interactions. The optical simulator is realized by a 3D waveguide array fabricated by the femtosecond laser direct writing (FLDW) − possessing true 3D micromachining capability. − By converting the temporal ( t ) energy transport into the spatial ( l ) evolution of light in waveguides through l = ct , − we can intuitively reveal the efficient CET dynamics by excitation of BChl-6, which paves the way for its application to more integrated artificial light-harvesting devices, even multiport light harvesters.…”

Section: Introductionmentioning

confidence: 99%

Ultrafast Coherent Energy Transport of Fenna–Matthews–Olson Complex in a 3D Photonic Lattice

Yan,

Li,

et al. 2023

J. Phys. Chem. C

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The coherent energy transport phenomenon inside the light-harvesting antenna like the Fenna−Matthews−Olson (FMO) complex has always been vital and attractive but elusive to investigate directly due to the complex organisms and the ultrafast transport process. Therefore, researchers have focused on the interaction of the FMO complex as a whole with the environment or the theoretical study of its dynamics. Here, we experimentally verify the theoretical system of ultrafast coherent energy transport among seven chlorophyll molecules within FMO with a threedimensional photonic lattice directly written by the femtosecond laser to intuitively uncover the dynamic process between chlorophyll molecules under both neighboring and non-neighboring interactions. The high similarities between the theoretical and experimental results demonstrate the precise positional layout control of the photonic lattices. These achievements will lead to a deep understanding of the mechanism and promising applications in the construction of more efficient integrated optical devices for artificial light energy transport.

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Generating Haar-Uniform Randomness Using Stochastic Quantum Walks on a Photonic Chip

Cited by 13 publications

References 32 publications

Efficient learning of mixed-state tomography for photonic quantum walk

Efficient learning of mixed-state tomography for photonic quantum walk

Variational quantum simulation of long-range interacting systems

Ultrafast Coherent Energy Transport of Fenna–Matthews–Olson Complex in a 3D Photonic Lattice

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