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Clonació quàntica variacional en un processador fotònic quàntic

Nicolau Orell, Sebastià

Published Jul 6, 2021• 0 citations
OpenAlex

Abstract

The Variational Quantum Cloning algorithm uses machine learning to obtain through optimization a circuit capable of generating approximate clones from unknown input quantum states. We adapt this algorithm to a boson sampling photonic platform formed exclusively by phase-shifters and beam-splitters, which is not universal but it still allows us to achieve quantum advantage. We have been able to obtain results through simulations reaching optimal fidelities for 1 to 2 universal cloning and also 1 to 2 and 1 to 3 phase-covariant cloning. This project demonstrates the capabilities of this type of photonic platforms and it can be used as a reference to adapt other variational algorithms to them.

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arXiv:quant-ph/0609105•Physical Review A•Jul 26, 2007•21 citations
The Variational Quantum Cloning algorithm uses machine learning to obtain through optimization a circuit capable of generating approximate clones from unknown input quantum states. We adapt this algorithm to a boson sampling photonic platform formed exclusively by phase-shifters and beam-splitters, which is not universal but it still allows us to achieve quantum advantage. We have been able to obtain results through simulations reaching optimal fidelities for 1 to 2 universal cloning and also 1 to 2 and 1 to 3 phase-covariant cloning. This project demonstrates the capabilities of this type of photonic platforms and it can be used as a reference to adapt other variational algorithms to them.
A seminal task in quantum information theory is to realize a device able to produce copies of a generic input state with the highest possible output fidelity, thus realizing an optimal quantum cloning machine. Recently, the concept of variational quantum cloning was introduced: a quantum machine learning algorithm through which, by exploiting a classical feedback loop informed by the output of a quantum processing unit, the system can self-learn the programming required for an optimal quantum cloning strategy. In this work, we experimentally implement a 1 → 2 variational cloning machine of dual-rail encoded photonic qubits, both for phase-covariant and state-dependent cloning. We exploit a fully programmable six-mode universal integrated device and classical feedback to reach near-optimal cloning performances. Our results demonstrate the potential of programmable integrated photonic platforms for variational self-learning of quantum algorithms.
We propose a quantum cloning machine, which clones a qubit into two clones assuming known modulus of expectation value of Pauli ${\ensuremath{\sigma}}_{z}$ matrix. The process is referred to as the mirror phase-covariant cloning for which the input state is a priori less known than that for the standard phase-covariant cloning. Analytical expressions describing the cloning transformation and fidelity of the clones are found. Extremal equations for the optimal cloning are derived and analytically solved by generalizing a method of Fiur\'a\ifmmode \check{s}\else \v{s}\fi{}ek [Phys. Rev. A 64, 062310 (2001)]. Quantum circuits implementing the optimal cloning transformation and their physical realization in a quantum-dot system are described.
We present the results of a linear optics photonic implementation of a quantum circuit that simulates a phase covariant cloner. Input polarization qubit states are cloned into path qubit states of the same photon, which is identified as a potential eavesdropper in a quantum key distribution protocol. The device has the flexibility to simulate optimal and non-optimal cloning machines, as well as mirrored versions that optimally clone states on either the northern or southern hemispheres of the Bloch sphere. We experimentally simulate the effect of a single 1→21\rightarrow 21→2 cloner, as well as the action of a random sequence of the original cloner and its mirrored version. This procedure allows the eavesdropper to mitigate the amount of noise added to the original copy and therefore to mask its presence.
We study state-dependent quantum cloning that can outperform universal cloning (UC).This is possible by using some a priori information on a given quantum state to be cloned.Specifically, we propose a generalization and optical implementation of quantum optimal mirror phase-covariant cloning, which refers to optimal cloning of sets of qubits of known modulus of the expectation value of Pauli's Z operator.Our results can be applied to cloning of an arbitrary mirror-symmetric distribution of qubits on the Bloch sphere including in special cases UC and phase-covariant cloning.We show that the cloning is optimal by adapting our former optimality proof for axisymmetric cloning (Bartkiewicz and Miranowicz 2010 Phys.Rev. A 82 042330).Moreover, we propose an optical realization of the optimal mirror phase-covariant 1 → 2 cloning of a qubit, for which the mean probability of successful cloning varies from 1/6 to 1/3 depending on prior information on the set of qubits to be cloned.The qubits are represented by polarization states of photons generated by the type-I spontaneous parametric down-conversion.The scheme is based on the interference of two photons on an unbalanced polarization-dependent beam splitter with different splitting ratios for vertical and horizontal polarization components and the additional application of feedforward by means of Pockels cells.The experimental feasibility of the proposed setup is carefully studied including various kinds of imperfections and losses.Moreover, we briefly describe two possible cryptographic applications of the optimal mirror phase-covariant cloning corresponding to state discrimination (or estimation) and secure quantum teleportation.
Quantum machine learning represents a promising approach to disclose novel possibilities for quantum information protocols. Thus, recent efforts have been dedicated to determine how to merge, or extend, machine learning approaches in the quantum domain. A relevant example is provided by a quantum-optical neural network (QONN), a class of physical systems that aim to represent the quantum-optical version of classical neural networks. This architecture was first introduced to identify the structure of optical circuits in analogy to their classical counterpart, showing the possibility of tackling different quantum tasks. However, the full potential of such an approach still needs to be explored. In this work, we provide a step forward in this direction by employing it to tackle the fundamental task of optical quantum cloning. The employed architecture has been successfully trained to perform deterministic universal quantum cloning on a photonic platform, achieving results that are very close to that of an optimal universal quantum cloner. Furthermore, we have verified numerically the robustness of this approach with respect to experimental imperfections relevant for future implementations. This work thus shows the capabilities and flexibility of QONNs, which might prove important for designing even more complex optical circuits.
The optimal phase-covariant quantum cloning machine (PQCM) broadcasts the information associated to an input qubit into a multiqubit system, exploiting a partial a priori knowledge of the input state. This additional a priori information leads to a higher fidelity than for the universal cloning. The present article first analyzes different innovative schemes to implement the $1\ensuremath{\rightarrow}3$ PQCM. The method is then generalized to any $1\ensuremath{\rightarrow}M$ machine for an odd value of MMM by a theoretical approach based on the general angular momentum formalism. Finally different experimental schemes based either on linear or nonlinear methods and valid for single photon polarization encoded qubits are discussed.