Resource For Computation Research Articles

Exponential Clustering of Bipartite Quantum Entanglement at Arbitrary Temperatures

Macroscopic quantum effects play central roles in the appearance of inexplicable phenomena in low-temperature quantum many-body physics. Such macroscopic quantumness is often evaluated using long-range entanglement, i.e., entanglement in the macroscopic length scale. The long-range entanglement not only characterizes the novel quantum phases but also serves as a critical resource for quantum computation. Thus, the problem that arises is under which conditions can the long-range entanglement be stable even at room temperatures. Here, we show that bi-partite long-range entanglement is unstable at arbitrary temperatures and exponentially decays with distance. Our theorem provides a no-go theorem on the existence of the long-range entanglement. The obtained results are consistent with the existing observations that long-range entanglement at non-zero temperatures can exist in topologically ordered phases, where tripartite correlations are dominant. In the derivation of our result, we introduce a quantum correlation defined by the convex roof of the standard correlation function. We establish an exponential clustering theorem for generic quantum many-body systems for such a quantum correlation at arbitrary temperatures, which yields our main result by relating quantum correlation to quantum entanglement. As a simple application of our analytical techniques, we derived a general limit on the Wigner-Yanase-Dyson skew information and the quantum Fisher information, which will attract significant attention in the field of quantum metrology. Our work reveals novel general aspects of low-temperature quantum physics and sheds light on the characterization of long-range entanglement.

Unveiling Quantum Entanglement in Many-Body Systems from Partial Information

Quantum entanglement is commonly assumed to be a central resource for quantum computing and quantum simulation. Nonetheless, the capability to detect it in many-body systems is severely limited by the absence of sufficiently scalable and flexible certification tools. This issue is particularly critical in situations where the structure of entanglement is a priori unknown, and where one cannot rely on existing entanglement witnesses. Here, we implement a scheme in which the knowledge of the mean value of arbitrary observables can be used to probe multipartite entanglement in a scalable, certified, and systematic manner. Specifically, we rely on positive semidefinite conditions, independent of partial-transposition-based criteria, necessarily obeyed if the data can be reproduced by a separable state. The violation of any of these conditions yields a specific entanglement witness, tailored to the data of interest, revealing the salient features of the data which are impossible to reproduce without entanglement. We validate this approach by probing theoretical many-body states of several hundreds of qubits relevant to existing experiments: a single-particle quench in a one-dimensional XX chain; a many-body quench in a two-dimensional XX model with 1/r3 interactions; and thermal equilibrium states of Heisenberg and transverse-field Ising chains. In all cases, these investigations have led us to discover new entanglement witnesses, some of which could be characterized analytically, generalizing existing results in the literature. In summary, our paper introduces a flexible data-driven entanglement detection technique for uncharacterized quantum many-body states, of immediate relevance to experiments in a quantum advantage regime.Received 11 August 2021Revised 2 December 2021Accepted 22 February 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010342Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement detectionQuantum entanglementPhysical SystemsQuantum spin modelsTechniquesData analysisSemiclassical methodsQuantum InformationAtomic, Molecular & OpticalStatistical Physics

Con-Pi: A Distributed Container-Based Edge and Fog Computing Framework

Edge and Fog computing paradigms overcome the limitations of cloud-centric execution for different latency-sensitive Internet of Things (IoT) applications by offering computing resources closer to the data sources. Small single-board computers (SBCs) like Raspberry Pis (RPis) are widely used as computing nodes in both paradigms. These devices are usually equipped with moderate speed processors and provide support for peripheral interfacing and networking, making them well-suited to deal with IoT-driven operations such as data sensing, analysis, and actuation. However, these small Edge devices are constrained in facilitating multi-tenancy and resource sharing. The management of computing and peripheral resources through centralized entities further degrades their performance and service quality significantly. To address these issues, a fully distributed framework, named Con-Pi, is proposed in this work to manage resources at the Edge or Fog environments. Con-Pi exploits the concept of containerization and harnesses Docker containers to run IoT applications as micro-services. %Moreover, Con-Pi operates in a distributed manner across multiple RPis and enables them to share resources. The software system of the proposed framework also provides a scope to integrate different IoT applications, resource and energy management policies for Edge and Fog computing. Its performance is compared with the state-of-the-art frameworks through real-world experiments. The experimental results show that Con-Pi outperforms others in enhancing response time and managing energy usage and computing resources through its distributed offloading model. Further, we have developed an automated pest bird deterrent system using Con-Pi to demonstrate its suitability in developing practical solutions for various IoT-enabled use cases, including smart agriculture.

A Multi-Objective Task Offloading Optimization for Vehicular Fog Computing

Internet of Vehicle (IoV) is one of the most basic branches of the Internet of Things (IoT), which provides many advantages for drivers and passengers to ensure safety and traffic efficiency. Most IoV applications are delay-sensitive and require resources for data storage and computation that cannot be afforded by vehicles. Thus, such tasks are always offloaded to more powerful nodes, like cloud or fog. Vehicular Fog Computing (VFC), which extends cloud computing and brings resources closer to the edge of the network, has the potential to reduce both traffic congestion and load on the cloud. Resources management and allocation process is very critical for satisfying both user and provider needs. However, the strategy of task offloading to fog node in constraints of energy and latency is still an open issue. Several research works have tackled the resource scheduling problem in the field of VFC; however, the recent studies have not carefully addressed the transmission path to the destination node, nor has it considered the energy consumption of vehicles. This paper aims to optimize the task offloading process in the VFC system in terms of latency and energy objectives while taking the deadline constraint into considerations by adopting a Multi-Objective Evolutionary Algorithm (MOEA). Four different execution/transmission models are proposed where vehicle resources are utilized for tasks execution and transmission, and the well-known Dijkstra's algorithm is adopted to find the minimum path between each two nodes. The simulation results show that the models which involve the vehicles in the transmission process have reduced the latency and the total energy for the VFC system significantly in comparison with other models and the current state of the art methods.

Generation of four-mode cluster states based on cascade four-wave mixing process

As a crucial quantum resource for quantum computing and quantum information processing, cluster state has attracted extensive attention due to its unique entanglement properties and rich structures. In this work, we theoretically propose a scheme for generating four-mode entangled states based on cascaded four-wave mixing (FWM) process. The internal entanglement characteristics are studied by using the positivity under partial transposition criterion and eigenmode decomposition. In addition, the output entangled states are reconstructed and optimized by adjusting the relative phase of balanced homodyne detection and postprocessing the signal noise, and finally three four-mode cluster states with different structures are generated. Such a method can effectively reduce the excess noise induced by finite squeezing. Our theoretical results provide a reliable way of generating scalable continuous variable cluster states based on FWM process in atomic ensemble.

Physical reservoir computing with FORCE learning in a living neuronal culture

Rich dynamics in a living neuronal system can be considered as a computational resource for physical reservoir computing (PRC). However, PRC that generates a coherent signal output from a spontaneously active neuronal system is still challenging. To overcome this difficulty, we here constructed a closed-loop experimental setup for PRC of a living neuronal culture, where neural activities were recorded with a microelectrode array and stimulated optically using caged compounds. The system was equipped with first-order reduced and controlled error learning to generate a coherent signal output from a living neuronal culture. Our embodiment experiments with a vehicle robot demonstrated that the coherent output served as a homeostasis-like property of the embodied system from which a maze-solving ability could be generated. Such a homeostatic property generated from the internal feedback loop in a system can play an important role in task solving in biological systems and enable the use of computational resources without any additional learning.

Multidimensional cluster states using a single spin-photon interface coupled strongly to an intrinsic nuclear register

Photonic cluster states are a powerful resource for measurement-based quantum computing and loss-tolerant quantum communication. Proposals to generate multi-dimensional lattice cluster states have identified coupled spin-photon interfaces, spin-ancilla systems, and optical feedback mechanisms as potential schemes. Following these, we propose the generation of multi-dimensional lattice cluster states using a single, efficient spin-photon interface coupled strongly to a nuclear register. Our scheme makes use of the contact hyperfine interaction to enable universal quantum gates between the interface spin and a local nuclear register and funnels the resulting entanglement to photons via the spin-photon interface. Among several quantum emitters, we identify the silicon-29 vacancy centre in diamond, coupled to a nanophotonic structure, as possessing the right combination of optical quality and spin coherence for this scheme. We show numerically that using this system a 2×5-sized cluster state with a lower-bound fidelity of 0.5 and repetition rate of 65 kHz is achievable under currently realised experimental performances and with feasible technical overhead. Realistic gate improvements put 100-photon cluster states within experimental reach.

Computable and Operationally Meaningful Multipartite Entanglement Measures.

Multipartite entanglement is an essential resource for quantum communication, quantum computing, quantum sensing, and quantum networks. The utility of a quantum state |ψ⟩ for these applications is often directly related to the degree or type of entanglement present in |ψ⟩. Therefore, efficiently quantifying and characterizing multipartite entanglement is of paramount importance. In this work, we introduce a family of multipartite entanglement measures, called concentratable entanglements. Several well-known entanglement measures are recovered as special cases of our family of measures, and hence we provide a general framework for quantifying multipartite entanglement. We prove that the entire family does not increase, on average, under local operations and classical communications. We also provide an operational meaning for these measures in terms of probabilistic concentration of entanglement into Bell pairs. Finally, we show that these quantities can be efficiently estimated on a quantum computer by implementing a parallelized SWAP test, opening up a research direction for measuring multipartite entanglement on quantum devices.

Complexity of Fermionic Dissipative Interactions and Applications to Quantum Computing

Interactions between particles are usually a resource for quantum computing, making quantum many-body systems intractable by any known classical algorithm. In contrast, noise is typically considered as being inimical to quantum many-body correlations, ultimately leading the system to a classically tractable state. This work shows that noise represented by two-body processes, such as pair loss, plays the same role as many-body interactions and makes otherwise classically simulable systems universal for quantum computing. We analyze such processes in detail and establish a complexity transition between simulable and nonsimulable systems as a function of a tuning parameter. We determine important classes of simulable and nonsimulable two-body dissipation. Finally, we show how using resonant dissipation in cold atoms can enhance the performance of two-qubit gates.

Spatiotemporal mode-selective quantum frequency converter

We experimentally demonstrate a mode-selective quantum frequency converter over a compound spatiotemporal Hilbert space. We show that our method can achieve high extinction for high-dimensional quantum state tomography by selectively upconverting the signal modes with a modulated and delayed pump. By preparing the pump in optimized modes through adaptive feedback control, selective frequency conversion is demonstrated with up to 30 dB extinction. The simultaneous operations over high-dimensional degrees of freedom in both spatial and temporal domains can serve as a viable resource for photon-efficient quantum communications and computation.

Subsystem analysis of continuous-variable resource states

Continuous-variable (CV) cluster states are a universal resource for fault-tolerant quantum computation when supplemented with the Gottesman-Kitaev-Preskill (GKP) bosonic code. We generalize the recently introduced subsystem decomposition of a bosonic code [Phys. Rev. Lett. 125, 040501 (2020)], and we use it to analyze CV cluster-state quantum computing with GKP states. Specifically, we decompose squeezed vacuum states and approximate GKP states to reveal their encoded logical information, and we decompose several gates crucial to CV cluster-state quantum computing. Then, we use the subsystem decomposition to quantify damage to the logical information in approximate GKP states teleported through noisy CV cluster states. Each of these studies uses the subsystem decomposition to circumvent complications arising from the full CV nature of the mode in order to focus on the encoded qubit information.

Accelerating Spark-Based Applications with MPI and OpenACC

The amount of data produced in scientific and commercial fields is growing dramatically. Correspondingly, big data technologies, such as Hadoop and Spark, have emerged to tackle the challenges of collecting, processing, and storing such large-scale data. Unfortunately, big data applications usually have performance issues and do not fully exploit a hardware infrastructure. One reason is that applications are developed using high-level programming languages that do not provide low-level system control in terms of performance of highly parallel programming models like message passing interface (MPI). Moreover, big data is considered a barrier of parallel programming models or accelerators (e.g., CUDA and OpenCL). Therefore, the aim of this study is to investigate how the performance of big data applications can be enhanced without sacrificing the power consumption of a hardware infrastructure. A Hybrid Spark MPI OpenACC (HSMO) system is proposed for integrating Spark as a big data programming model, with MPI and OpenACC as parallel programming models. Such integration brings together the advantages of each programming model and provides greater effectiveness. To enhance performance without sacrificing power consumption, the integration approach needs to exploit the hardware infrastructure in an intelligent manner. For achieving this performance enhancement, a mapping technique is proposed that is built based on the application’s virtual topology as well as the physical topology of the undelaying resources. To the best of our knowledge, there is no existing method in big data applications related to utilizing graphics processing units (GPUs), which are now an essential part of high-performance computing (HPC) as a powerful resource for fast computation.

Tunable quantum interference using a topological source of indistinguishable photon pairs

Sources of quantum light, in particular correlated photon pairs that are indistinguishable for all degrees of freedom, are the fundamental resource for photonic quantum computation and simulation. Although such sources have been recently realized using integrated photonics, they offer limited ability to tune the spectral and temporal correlations between generated photons because they rely on a single component, such as a ring resonator. Here, we demonstrate a tunable source of indistinguishable photon pairs using dual-pump spontaneous four-wave mixing in a topological system comprising a two-dimensional array of resonators. We exploit the linear dispersion of the topological edge states to tune the spectral bandwidth (by about 3.5×), and thereby, to tune quantum interference between generated photons by tuning the two pump frequencies. We demonstrate energy−time entanglement and, using numerical simulations, confirm the topological robustness of our source. Our results could lead to tunable, frequency-multiplexed quantum light sources for photonic quantum technologies.

Continuous-variable multipartite vibrational entanglement

A compact scheme for the preparation of macroscopic multipartite entanglement is proposed and analyzed. In this scheme the vibrational modes of a mechanical resonator constitute continuous variable (CV) subsystems that entangle to each other as a result of their interaction with a two-level system (TLS). By properly driving the TLS, we show that a selected set of modes can be activated and prepared in a multipartite entangled state. We first study entanglement properties of a three-mode system by evaluating the genuine multipartite entanglement. And investigate its usefulness as a quantum resource by computing the quantum Fisher information. Moreover, the robustness of the state against the qubit and thermal noises is studied, proving a long-lived entanglement. To examine the scalability and structural properties of the scheme, we derive an effective model for the multimode system through elimination of the TLS dynamics. This work provides a step towards a compact and versatile device for creating multipartite noise-resilient entangled state in vibrational modes as a resource for CV quantum metrology, quantum communication, and quantum computation.

Witnesses of coherence and dimension from multiphoton indistinguishability tests

Quantum coherence marks a deviation from classical physics, and has been studied as a resource for metrology and quantum computation. Finding reliable and effective methods for assessing its presence is then highly desirable. Coherence witnesses rely on measuring observables whose outcomes can guarantee that a state is not diagonal in a known reference basis. Here we experimentally measure a novel type of coherence witness that uses pairwise state comparisons to identify superpositions in a basis-independent way. Our experiment uses a single interferometric set-up to simultaneously measure the three pairwise overlaps among three single-photon states via Hong-Ou-Mandel tests. Besides coherence witnesses, we show the measurements also serve as a Hilbert-space dimension witness. Our results attest to the effectiveness of pooling many two-state comparison tests to ascertain various relational properties of a set of quantum states.

An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

Cloud computing is an emerging distributed computing model that offers computational capability over internet. Cloud provides a huge level collection of powerful and scalable computational resources for computation and data-intensive large scale workflow deployment. For business as well as scientific applications, optimal scheduling of workflow is emerged as a major concern. Optimization of scheduling process leads to the reduction of execution time, cost, etc. So, this paper presents an enhanced recent ant-lion optimization (ALO) algorithm hybridized with popular particle swarm optimization (PSO) algorithm to optimize a workflow scheduling specifically for cloud. A security approach called Data Encryption Standard (DES) is used for encoding the cloud information while scheduling is carried out. The research aims to contribute an enhanced workflow scheduling more safely than the existing frameworks. Enhancement procedures are evaluated in terms of cost, load, and makespan. The simulation procedures are done by utilizing the CloudSim tool. The proposed hybrid optimization results contrasted with well-known existing approaches. The existing round-robin (RR), ALO and PSO methods are selected to compare and identify the potency of the proposed system. The outcomes indicated that the proposed technique minimizes the cost by 9.8% of GA-PSO, 10% of PSO, 20% of ALO, 30% of RR and 12% of GA. Load balancing and makespan of the proposed method reduces by 8% than GA-PSO, 10% than ALO, 20% than PSO, 35% than RR and 45% than GA. The energy consumption and reliability performance are also reasonably well.

Quantum contextuality of YO-13 rays

Quantum contextuality, a more general quantum correlation, is an important resource for quantum computing and quantum information processing. Meanwhile, quantum contextuality plays an important role in fundamental quantum physics. Yu and Oh (YO) proposed a proof of the Kochen–Specker theorem for a qutrit with only 13 rays. Here, we further study quantum contextuality of YO-13 rays using the inequality approach. The maximum quantum violation value of the optimal noncontextuality inequality constructed by YO-13 rays is increased to 11.9776 in the four-dimensional system, which is larger than 11.6667 in the qutrit system. The result shows that the set of YO-13 rays has stronger quantum contextuality in the four-dimensional system. Moreover, we provide an all-versus-nothing proof (i.e. Hardy-like proof) to study YO-13 rays without using any inequality, which is easily applied to experimental tests. Our results will further deepen the understanding of YO-13 rays.

Investigating a Streaming Analytics Framework for Data Analytics Applications in the Aircraft Cabin

<div class="section abstract"><div class="htmlview paragraph">Aircraft cabin operations shift towards data-driven processes. Cabin-wide multi-system communication networks are introduced to share required data for corresponding novel data-driven applications. Examples are data-driven predictive maintenance applications to reduce the downtime of systems and increase the period of scheduled maintenance or video analytics usage to detect a strained or unruly atmosphere amongst passengers. These applications require a network to transport the associated data and resources for actual computation. Costs and weight have always been the most important factors deciding if new services are introduced within the aircraft cabin. Thus, re-using hardware with free computation capacity that is already installed in the aircraft cabin can target both aspects, weight and costs. Examples for such hardware resources could be the In-flight Entertainment (IFE) equipment being installed in every seat. By means of distributed computing these resources can be combined in order to solve a computational task. In this paper, the Apache Spark Streaming Framework is investigated as an example for a distributed computing framework with respect to its suitability for being deployed in the aircraft cabin. For this purpose, it is evaluated how Spark scales with resource-constrained computing nodes. As this type of framework is designed for a robust execution of tasks, it can also be used for non-safety, but business-critical applications. In addition, Spark is a well-established commercial off-the-shelf (COTS) software, thus, possible advantages of such software like a reduction of non-recurrent engineering costs and support for developers creating applications for the aircraft cabin are also considered.</div></div>

Deterministic multi-qubit entanglement in a quantum network.

The generation of high-fidelity distributed multi-qubit entanglement is a challenging task for large-scale quantum communication and computational networks1-4. The deterministic entanglement of two remote qubits has recently been demonstrated with both photons5-10 and phonons11. However, the deterministic generation and transmission of multi-qubit entanglement has not been demonstrated, primarily owing to limited state-transfer fidelities. Here we report a quantum network comprising two superconducting quantum nodes connected by a one-metre-long superconducting coaxial cable, where each node includes three interconnected qubits. By directly connecting the cable to one qubit in each node, we transfer quantum states between the nodes with a process fidelity of 0.911±0.008. We also prepare a three-qubit Greenberger-Horne-Zeilinger (GHZ) state12-14 in one node and deterministically transfer this state to the other node, with a transferred-state fidelity of 0.656±0.014. We further use this system to deterministically generate a globally distributed two-node, six-qubit GHZ state with a state fidelity of 0.722±0.021. The GHZ state fidelities are clearly above the threshold of 1/2 for genuine multipartite entanglement15, showing that this architecture can be used to coherently link together multiple superconducting quantum processors, providing a modular approach for building large-scale quantum computers16,17.

Gaussian Conversion Protocols for Cubic Phase State Generation

Universal quantum computing with continuous variables requires non-Gaussian resources, in addition to a Gaussian set of operations. A known resource enabling universal quantum computation is the cubic phase state, a non-Gaussian state whose experimental implementation has so far remained elusive. In this paper, we introduce two Gaussian conversion protocols that allow for the conversion of a non-Gaussian state that has been achieved experimentally, namely the trisqueezed state [Sandbo Changet al., Phys. Rev. X10, 011011 (2020)],to a cubic phase state. The first protocol is deterministic and it involves active (in-line) squeezing, achieving large fidelities that saturate the bound for deterministic Gaussian protocols. The second protocol is probabilistic and it involves an auxiliary squeezed state, thus removing the necessity of in-line squeezing but still maintaining significant success probabilities and fidelities even larger than for the deterministic case. The success of these protocols provides strong evidence for using trisqueezed states as resources for universal quantum computation.