Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>We review some of the recent efforts in devising and engineering bosonic qubits for superconducting devices, with emphasis on the Gottesman–Kitaev–Preskill (GKP) qubit. We present some new results on decoding repeated GKP error correction using finitely-squeezed GKP ancilla qubits, exhibiting differences with previously studied stochastic error models. We discuss circuit-QED ways to realize CZ gates between GKP qubits and we discuss different scenarios for using GKP and regular qubits as building blocks in a scalable superconducting surface code architecture.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>PyGSTi is a Python software package for assessing and characterizing the performance of quantum computing processors. It can be used as a standalone application, or as a library, to perform a wide variety of quantum characterization, verification, and validation (QCVV) protocols on as-built quantum processors. We outline pyGSTi’s structure, and what it can do, using multiple examples. We cover its main characterization protocols with end-to-end implementations. These include gate set tomography, randomized benchmarking on one or many qubits, and several specialized techniques. We also discuss and demonstrate how power users can customize pyGSTi and leverage its components to create specialized QCVV protocols and solve user-specific problems.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Now that ever more sophisticated devices for quantum computing are being developed, we require ever more sophisticated benchmarks. This includes a need to determine how well these devices support the techniques required for quantum error correction. In this paper we introduce the <jats:monospace>topological_codes</jats:monospace> module of Qiskit-Ignis, which is designed to provide the tools necessary to perform such tests. Specifically, we use the <jats:monospace>RepetitionCode</jats:monospace> and <jats:monospace>GraphDecoder</jats:monospace> classes to run tests based on the repetition code and process the results. As an example, data from a 43 qubit code running on IBM’s <jats:italic>Rochester</jats:italic> device is presented.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Implementing a gate-based quantum algorithm on an noisy intermediate scale quantum (NISQ) device has several challenges that arise from the fact that such devices are noisy and have limited quantum resources. Thus, various factors contributing to the depth and width as well as to the noise of an implementation of a gate-based algorithm must be understood in order to assess whether an implementation will execute successfully on a given NISQ device. In this contribution, we discuss these factors and their impact on algorithm implementations. Especially, we will cover state preparation, oracle expansion, connectivity, circuit rewriting, and readout: these factors are very often ignored when presenting a gate-based algorithm but they are crucial when implementing such an algorithm on near-term quantum computers. Our contribution will help developers in charge of realizing gate-based algorithms on such machines in (i) achieving an executable implementation, and (ii) assessing the success of their implementation on a given machine.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Variational quantum algorithms are a leading candidate for early applications on noisy intermediate-scale quantum computers. These algorithms depend on a classical optimization outer-loop that minimizes some function of a parameterized quantum circuit. In practice, finite sampling error and gate errors make this a stochastic optimization with unique challenges that must be addressed at the level of the optimizer. The sharp trade-off between precision and sampling time in conjunction with experimental constraints necessitates the development of new optimization strategies to minimize overall wall clock time in this setting. In this work, we introduce two optimization methods and numerically compare their performance with common methods in use today. The methods are surrogate model-based algorithms designed to improve reuse of collected data. They do so by utilizing a least-squares quadratic fit of sampled function values within a moving trusted region to estimate the gradient or a policy gradient. To make fair comparisons between optimization methods, we develop experimentally relevant cost models designed to balance efficiency in testing and accuracy with respect to cloud quantum computing systems. The results here underscore the need to both use relevant cost models and optimize hyperparameters of existing optimization methods for competitive performance. The methods introduced here have several practical advantages in realistic experimental settings, and we have used one of them successfully in a separately published experiment on Google’s Sycamore device.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>The Quantum approximate optimization algorithm (QAOA) constitutes one of the often mentioned candidates expected to yield a quantum boost in the era of near-term quantum computing. In practice, quantum optimization will have to compete with cheaper classical heuristic methods, which have the advantage of decades of empirical domain-specific enhancements. Consequently, to achieve optimal performance we will face the issue of algorithm selection, well-studied in practical computing. Here we introduce this problem to the quantum optimization domain. Specifically, we study the problem of detecting those problem instances of where QAOA is most likely to yield an advantage over a conventional algorithm. As our case study, we compare QAOA against the well-understood approximation algorithm of Goemans and Williamson on the Max-Cut problem. As exactly predicting the performance of algorithms can be intractable, we utilize machine learning (ML) to identify when to resort to the quantum algorithm. We achieve cross-validated accuracy well over 96%, which would yield a substantial practical advantage. In the process, we highlight a number of features of instances rendering them better suited for QAOA. While we work with simulated idealised algorithms, the flexibility of ML methods we employed provides confidence that our methods will be equally applicable to broader classes of classical heuristics, and to QAOA running on real-world noisy devices.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Artificial intelligence algorithms largely build on multi-layered neural networks. Coping with their increasing complexity and memory requirements calls for a paradigmatic change in the way these powerful algorithms are run. Quantum computing promises to solve certain tasks much more efficiently than any classical computing machine, and actual quantum processors are now becoming available through cloud access to perform experiments and testing also outside of research labs. Here we show in practice an experimental realization of an artificial feed-forward neural network implemented on a state-of-art superconducting quantum processor using up to 7 active qubits. The network is made of quantum artificial neurons, which individually display a potential advantage in storage capacity with respect to their classical counterpart, and it is able to carry out an elementary classification task which would be impossible to achieve with a single node. We demonstrate that this network can be equivalently operated either via classical control or in a completely coherent fashion, thus opening the way to hybrid as well as fully quantum solutions for artificial intelligence to be run on near-term intermediate-scale quantum hardware.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Precision measurements in molecules have advanced rapidly in recent years through developments in techniques to cool, trap, and control. The complexity of molecules makes them a challenge to study, but also offers opportunities for enhanced sensitivity to many interesting effects. Polyatomic molecules offer additional complexity compared to diatomic molecules, yet are still ‘simple’ enough to be laser-cooled and controlled. While laser cooling molecules is still a research frontier itself, there are many proposed and ongoing experiments seeking to combine the advanced control enabled by ultracold temperatures with the intrinsic sensitivity of molecules. In this perspective, we discuss some applications where laser-cooled polyatomic molecules may offer advantages for precision measurements of fundamental physics, both within and beyond the Standard Model.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We propose and analyze an efficient scheme for realizing the high transfer rate for quantum router composed of two coupled-resonator waveguides (CRWs) channels coupled with <jats:italic>N</jats:italic> sequential cavities with embedded <jats:italic>N</jats:italic> four-level atoms. In this paper, we focus on the effect of the phase difference between different coupling constants which has been ignored in the previous works. In this scheme, we demonstrate that the phase difference between the coupling constants has a very large impact on the band spectra of single-photon propagating from one of the CRW into the other. Besides, as the number of atoms embedded in the two CRWs increases, the efficiency of a single photon being routed into other CRW can reach 100%. More importantly, we also find that if the phase difference of the coupling strength exists, even if there is atomic dissipation in the system, the transfer rate of a single photon not only it not decrease but it increases.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We consider non-local sensing of scalar signals with specific spatial dependence in the Bayesian regime. We design schemes that allow one to achieve optimal scaling and are immune to noise sources with a different spatial dependence than the signal. This is achieved by using a sensor array of spatially separated sensors and constructing a multi-dimensional decoherence free subspace. While in the Fisher regime with sharp prior and multiple measurements only the spectral range Δ is important, in single-shot sensing with broad prior the number of available energy levels <jats:italic>L</jats:italic> is crucial. We study the influence of <jats:italic>L</jats:italic> and Δ also in intermediate scenarios, and show that these quantities can be optimized separately in our setting. This provides us with a flexible scheme that can be adapted to different situations, and is by construction insensitive to given noise sources.</jats:p>