Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>Recent advances in the field of adiabatic quantum computing and the closely related field of quantum annealing have centered around using more advanced and novel Hamiltonian representations to solve optimization problems. One of these advances has centered around the development of driver Hamiltonians that commute with the constraints of an optimization problem—allowing for another avenue to satisfying those constraints instead of imposing penalty terms for each of them. In particular, the approach is able to use sparser connectivity to embed several practical problems on quantum devices in comparison to the standard approach of using penalty terms. However, designing the driver Hamiltonians that successfully commute with several constraints has largely been based on strong intuition for specific problems and with no simple general algorithm for generating them for arbitrary constraints. In this work, we develop a simple and intuitive algebraic framework for reasoning about the commutation of Hamiltonians with linear constraints—one that allows us to classify the complexity of finding a driver Hamiltonian for an arbitrary set of linear constraints as NP-complete. Because unitary operators are exponentials of Hermitian operators, these results can also be applied to the construction of mixers in the quantum alternating operator ansatz framework.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The satellite-based measurement-device-independent quantum key distribution can promote the realization of quantum communication networks. Under the condition of the limited data set, it is necessary to optimize all parameters. For communication networks, real-time prediction and optimization are also indispensable. With the development of machine learning, cross-combination with machine learning has also become the mainstream of parameter optimization in various disciplines. This paper discusses the asymmetric MDI-QKD based on the satellite in the case of statistical fluctuations, and uses the local search algorithm to achieve full parameter optimization under the condition of considering the probability of sending the signal. Compared with fixed related parameters, the key rate is increased by an order of magnitude. On this basis, random forest is used to predict the high-precision optimal parameters, thereby eliminating the simulation and iteration required by the search method to meet the real-time optimization of the future QKD network.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We study a quantum many-body system of attracting bosons confined in a ring-shaped potential and interrupted by a weak link. With such architecture, the system defines atomtronic quantum interference devices harnessing quantum solitonic currents. We demonstrate that the system is characterized by the specific interplay between the interaction and the strength of the weak link. In particular, we find that, depending on the operating conditions, the current can be a universal function of the relative size between the strength of the impurity and interaction. The low lying many-body states are studied through a quench dynamical protocol that is the atomtronic counterpart of Rabi interferometry. With this approach, we demonstrate how our system defines a two level system of coupled solitonic currents. The current states are addressed through the analysis of the momentum distribution.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Quantum gates are essential for the realization of quantum computer and have been implemented in various types of two-level systems. However, high-dimensional quantum gates are rarely investigated both theoretically and experimentally even that high-dimensional quantum systems exhibit remarkable advantages over two-level systems for some quantum information and quantum computing tasks. Here we experimentally demonstrate the four-dimensional <jats:italic>X</jats:italic> gate and its unique higher orders with the average conversion efficiency 93%. All these gates are based on orbital-angular-momentum degree of freedom (DoF) of single photons. Besides, a set of controlled quantum gates is implemented by use of polarization DoF. Our work is an important step toward the goal of achieving arbitrary high-dimensional quantum circuit and paves a way for the implementation of high-dimensional quantum communication and computation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The impossibility of deterministic and error-free discrimination among nonorthogonal quantum states lies at the core of quantum theory and constitutes a primitive for secure quantum communication. Demanding determinism leads to errors, while demanding certainty leads to some inconclusiveness. One of the most fundamental strategies developed for this task is the optimal unambiguous measurement. It encompasses conclusive results, which allow for error-free state retrodictions with the maximum success probability, and inconclusive results, which are discarded for not allowing perfect identifications. Interestingly, in high-dimensional Hilbert spaces the inconclusive results may contain valuable information about the input states. Here, we theoretically describe and experimentally demonstrate the discrimination of nonorthogonal states from both conclusive and inconclusive results in the optimal unambiguous strategy, by concatenating a minimum-error measurement at its inconclusive space. Our implementation comprises four- and nine-dimensional spatially encoded photonic states. By accessing the inconclusive space to retrieve the information that is wasted in the conventional protocol, we achieve significant increases of up to a factor of 2.07 and 3.73, respectively, in the overall probabilities of correct retrodictions. The concept of concatenated optimal measurements demonstrated here can be extended to other strategies and will enable one to explore the full potential of high-dimensional nonorthogonal states for quantum communication with larger alphabets.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We present <jats:monospace>Qibo</jats:monospace>, a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interest in quantum computing and the recent developments of quantum hardware devices motivates the development of new advanced computational tools focused on performance and usage simplicity. In this work we introduce a new quantum simulation framework that enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem and quantum algorithms at hand. This software is designed from scratch with simulation performance, code simplicity and user friendly interface as target goals. It takes advantage of hardware acceleration such as multi-threading Central Processing Unit (CPU), single Graphics Processing Unit (GPU) and multi-GPU devices.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Estimating the difference between quantum data is crucial in quantum computing. However, as typical characterizations of quantum data similarity, the trace distance and quantum fidelity are believed to be exponentially-hard to evaluate in general. In this work, we introduce hybrid quantum–classical algorithms for these two distance measures on near-term quantum devices where no assumption of input state is required. First, we introduce the variational trace distance estimation (VTDE) algorithm. We in particular provide the technique to extract the desired spectrum information of any Hermitian matrix by local measurement. A novel variational algorithm for trace distance estimation is then derived from this technique, with the assistance of a single ancillary qubit. Notably, VTDE could avoid the barren plateau issue with logarithmic depth circuits due to a local cost function. Second, we introduce the variational fidelity estimation algorithm. We combine Uhlmann’s theorem and the freedom in purification to translate the estimation task into an optimization problem over a unitary on an ancillary system with fixed purified inputs. We then provide a purification subroutine to complete the translation. Both algorithms are verified by numerical simulations and experimental implementations, exhibiting high accuracy for randomly generated mixed states.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a <jats:italic>hybrid quantum autoencoder</jats:italic> (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically represent—their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states—which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the filtering variational quantum eigensolver which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the quantum approximate optimization algorithm. We also demonstrate the experimental feasibility of our algorithms on a Quantinuum trapped-ion quantum processor powered by Honeywell.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Understanding the emergence of objectivity from the quantum realm has been a long standing issue strongly related to the quantum to classical crossover. Quantum Darwinism (QD) provides an answer, interpreting objectivity as consensus between independent observers. Quantum computers provide an interesting platform for such experimental investigation of QD, fulfiling their initial intended purpose as quantum simulators. Here we assess to what degree current Noisy intermediate-scale quantum devices can be used as experimental platforms in the field of QD. We do this by simulating an exactly solvable stochastic collision model, taking advantage of the analytical solution to benchmark the experimental results.</jats:p>