Application

@article{nokey,

title = {Engineered dissipation to mitigate barren plateaus},

author = { Sannia, A. and Tacchino, F. and Tavernelli, I. Giorgi, G.L. and Zambrini, R.},

url = {https://www.nature.com/articles/s41534-024-00875-0#citeas},

doi = {doi.org/10.1038/s41534-024-00875-0},

year  = {2024},

date = {2024-09-04},

journal = {NPJ/Quantum Information},

volume = {10},

issue = {81},

abstract = {Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practical implementations crucially depend on the effectiveness of quantum circuit training, which can be severely limited by phenomena such as barren plateaus. While, in general, dissipation is detrimental for quantum algorithms, and noise itself can actually induce barren plateaus, here we describe how the inclusion of properly engineered Markovian losses after each unitary quantum circuit layer allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.},

keywords = {csic1},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Retrieving past quantum features with deep hybrid classical-quantum reservoir computing},

author = {Nokkala, J. and Giorgi, G.L. and Zambrini, R. },

url = {https://iopscience.iop.org/article/10.1088/2632-2153/ad5f12},

doi = {10.1088/2632-2153/ad5f12},

year  = {2024},

date = {2024-07-19},

journal = {Machine Learning: Science and Technology},

volume = {5},

abstract = {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.},

keywords = {csic1},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum fidelity kernel with a trapped-ion simulation platform},

author = {Martínez-Peña, R. and Soriano, M.C. and Zambrini, R. 

},

url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.109.042612},

doi = {10.1103/PhysRevA.109.042612},

issn = {2469-9926},

year  = {2024},

date = {2024-04-08},

urldate = {2024-04-08},

journal = {Physical Review A},

volume = {109},

issue = {4},

abstract = {Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder generalization capability, and the scalability of quantum kernels becomes an issue. To overcome these challenges, various strategies under the concept of inductive bias have been proposed. Bandwidth optimization is a promising approach that can be implemented using quantum simulation platforms. We propose trapped-ion simulation platforms as a means to compute quantum kernels, filling a gap in the previous literature and demonstrating their effectiveness for binary classification tasks. We compare the performance of the proposed method with an optimized classical kernel and evaluate the robustness of the quantum kernel against noise. The results show that ion trap platforms are well-suited for quantum kernel computation and can achieve high accuracy with only a few qubits.},

keywords = {csic1},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum memories for squeezed and coherent superpositions in a driven-dissipative nonlinear oscillator},

author = {Labay-Mora, A. and Zambrini, R. and Giorgi, G.L. 



},

url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.109.032407},

doi = {doi.org/10.1103/PhysRevA.109.032407},

issn = {2469-9926},

year  = {2024},

date = {2024-03-08},

journal = {Physical Review A},

volume = {109},

issue = {3},

abstract = {Quantum oscillators with nonlinear driving and dissipative terms have gained significant attention due to their ability to stabilize cat states for universal quantum computation. Recently, superconducting circuits have been employed to realize such long-lived qubits stored in coherent states. We present a generalization of these oscillators, which are not limited to coherent states. The key ingredient lies in the presence of different nonlinearities in driving and dissipation, beyond the quadratic one. Through an extensive analysis of the asymptotic dynamical features for different nonlinearities, we identify the conditions for the storage and retrieval of quantum states, such as squeezed states, in both coherent and incoherent superpositions. We explore their applications in quantum computing, where squeezing prolongs the lifetime of memory storage for qubits encoded in the superposition of two symmetric squeezed states, and in quantum associative memory, which has so far been limited to the storage of classical patterns.},

keywords = {csic1},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Squeezing as a resource for time series processing in quantum reservoir computing},

author = {García-Beni, J. and Giorgi, G.L. and Soriano, M.C. and Zambrini, R.},

url = {https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-4-6733&id=546462},

doi = {doi.org/10.1364/OE.507684},

year  = {2024},

date = {2024-02-09},

journal = {Optics Express},

volume = {32},

issue = {4},

pages = { 6733-6747},

abstract = {Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.},

keywords = {csic1},

pubstate = {published},

tppubtype = {article}

}