Experimental Optical Simulator of Reconfigurable and Complex Quantum Environment

Renault, P.; Nokkala, J.; Roeland, G.; Joly, N.Y.; Zambrini, R.; Maniscalco, S.; Piilo, J.; Treps, N.; Parigi, V.

doi:10.1103/prxquantum.4.040310

Cited by 5 publications

(2 citation statements)

References 68 publications

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“…where i = √ −1, a j and a † j are the annihilation and creation operators, respectively, indexed by mode and satisfying the commutator relation [a j , a † l ] = δ jl . The reservoir is a complex network [47,48] with (real) couplings g jl and h jl . Following [41] they are random numbers uniformly distributed as g jl ∈ [0.1, 0.3] and h jl ∈ [0.2, 0.4], scaled with the modes frequencies (ω j = 1); these parameters are consistent with squeezing levels of state-of-the-art experiments.…”

Section: Methodsmentioning

confidence: 99%

Retrieving past quantum features with deep hybrid classical-quantum reservoir computing

Nokkala,

Giorgi,

Zambrini

2024

Mach. Learn.: Sci. Technol.

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Machine learning techniques have achieved impressive results in recent years and the possibility of harnessing the power of quantum physics opens new promising avenues to speed up classical learning methods. Rather than viewing classical and quantum approaches as exclusive alternatives, their integration into hybrid designs has gathered increasing interest, as seen in variational quantum algorithms, quantum circuit learning, and kernel methods. Here we introduce deep hybrid classical-quantum reservoir computing for temporal processing of quantum states where information about, for instance, the entanglement or the purity of past input states can be extracted via a single-step measurement. We find that the hybrid setup cascading two reservoirs not only inherits the strengths of both of its constituents but is even more than just the sum of its parts, outperforming comparable non-hybrid alternatives. The quantum layer is within reach of state-of-the-art multimode quantum optical platforms while the classical layer can be implemented in silico.

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Section: Methodsmentioning

confidence: 99%

Retrieving past quantum features with deep hybrid classical-quantum reservoir computing

Nokkala,

Giorgi,

Zambrini

2024

Mach. Learn.: Sci. Technol.

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“…The problem of experimental realization of random quantum harmonic oscillator networks has been recently solved in a multimode quantum optics platform [192], where a shaped pulse train pumps an optical parametric process, creating squeezed modes, which can then be measured in a suitable basis to complete the mapping of the network dynamics to that of the optical modes. This has already been used to experimentally realize non-Markovian open system dynamics [193,194].…”

Section: Structured Environments and Probingmentioning

confidence: 99%

Complex quantum networks: a topical review

Nokkala,

Piilo,

Bianconi

2024

J. Phys. A: Math. Theor.

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These are exciting times for quantum physics as new quantum technologies are expected to soon transform computing at an unprecedented level. Simultaneously network science is ﬂourishing proving an ideal mathematical and computational framework to capture the complexity of large interacting systems. Here we provide a comprehensive and timely review of the rising ﬁeld of complex quantum networks. On one side, this subject is key to harness the potential of complex networks in order to provide design principles to boost and enhance quantum algorithms and quantum technologies. On the other side this subject can provide a new generation of quantum algorithms to infer signiﬁcant complex network properties. The ﬁeld features fundamental research questions as diverse as designing networks to shape Hamiltonians and their corresponding phase diagram, taming the complexity of many-body quantum systems with network theory, revealing how quantum physics and quantum algorithms can predict novel network properties and phase transitions, and studying the interplay between architecture, topology and performance in quantum communication networks. Our review covers all of these multifaceted aspects in a self-contained presentation aimed both at network-curious quantum physicists and at quantum-curious network theorists. We provide a framework that uniﬁes the ﬁeld of quantum complex networks along four main research lines: network-generalized, quantum-applied, quantum-generalized and quantum-enhanced. Finally we draw attention to the connections between these research lines, which can lead to new opportunities and new discoveries at the interface between quantum physics and network science.

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Simulating spin biology using a digital quantum computer: Prospects on a near-term quantum hardware emulator

Alvarez,

Chowdhury,

Smith

et al. 2024

APL Quantum

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Understanding the intricate quantum spin dynamics of radical pair reactions is crucial for unraveling the underlying nature of chemical processes across diverse scientific domains. In this work, we leverage Trotterization to map coherent radical pair spin dynamics onto a digital gate-based quantum simulation. Our results demonstrated an agreement between the idealized noiseless quantum circuit simulations and established master equation approaches for homogeneous radical pair recombination, identifying ∼15 Trotter steps to be sufficient for faithfully reproducing the coupled spin dynamics of a prototypical system. By utilizing this computational technique to study the dynamics of spin systems of biological relevance, our findings underscore the potential of digital quantum simulation (DQS) of complex radical pair reactions and builds the groundwork toward more utilitarian investigations into their intricate reaction dynamics. We further investigate the effect of realistic error models on our DQS approach and provide an upper limit for the number of Trotter steps that can currently be applied in the absence of error mitigation techniques before losing simulation accuracy to deleterious noise effects.

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Experimental Optical Simulator of Reconfigurable and Complex Quantum Environment

Cited by 5 publications

References 68 publications

Retrieving past quantum features with deep hybrid classical-quantum reservoir computing

Retrieving past quantum features with deep hybrid classical-quantum reservoir computing

Complex quantum networks: a topical review

Simulating spin biology using a digital quantum computer: Prospects on a near-term quantum hardware emulator

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