No Arabic abstract
We consider a basic quantum hybrid network model consisting of a number of nodes each holding a qubit, for which the aim is to drive the network to a consensus in the sense that all qubits reach a common state. Projective measurements are applied serving as control means, and the measurement results are exchanged among the nodes via classical communication channels. We show how to carry out centralized optimal path planning for this network with all-to-all classical communications, in which case the problem becomes a stochastic optimal control problem with a continuous action space. To overcome the computation and communication obstacles facing the centralized solutions, we also develop a distributed Pairwise Qubit Projection (PQP) algorithm, where pairs of nodes meet at a given time and respectively perform measurements at their geometric average. We show that the qubit states are driven to a consensus almost surely along the proposed PQP algorithm, and that the expected qubit density operators converge to the average of the networks initial values.
In this work, we consider distributed agreement tasks in microbial distributed systems under stochastic population dynamics and competitive interactions. We examine how competitive exclusion can be used to solve distributed agreement tasks in the microbial setting. To this end, we develop a new technique for analyzing the time to reach competitive exclusion in systems with two competing species under biologically realistic population dynamics. We use this technique to analyze a protocol that exploits competitive interactions to solve approximate majority consensus efficiently in microbial systems. To corroborate our analytical results, we use computer simulations to show that these consensus dynamics occur within practical time scales.
Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or contexts. In parallel, quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations, which could generate distributions that are hard for a classical computer to produce. Here we propose a hybrid quantum-classical convolutional neural network (QCCNN), inspired by convolutional neural networks (CNNs) but adapted to quantum computing to enhance the feature mapping process. QCCNN is friendly to currently noisy intermediate-scale quantum computers, in terms of both number of qubits as well as circuits depths, while retaining important features of classical CNN, such as nonlinearity and scalability. We also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical algorithms. We demonstrate the potential of this architecture by applying it to a Tetris dataset, and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN.
Tensor network theory and quantum simulation are respectively the key classical and quantum computing methods in understanding quantum many-body physics. Here, we introduce the framework of hybrid tensor networks with building blocks consisting of measurable quantum states and classically contractable tensors, inheriting both their distinct features in efficient representation of many-body wave functions. With the example of hybrid tree tensor networks, we demonstrate efficient quantum simulation using a quantum computer whose size is significantly smaller than the one of the target system. We numerically benchmark our method for finding the ground state of 1D and 2D spin systems of up to $8times 8$ and $9times 8$ qubits with operations only acting on $8+1$ and $9+1$ qubits,~respectively. Our approach sheds light on simulation of large practical problems with intermediate-scale quantum computers, with potential applications in chemistry, quantum many-body physics, quantum field theory, and quantum gravity thought experiments.
Quantum networks will provide multi-node entanglement over long distances to enable secure communication on a global scale. Traditional quantum communication protocols consume pair-wise entanglement, which is sub-optimal for distributed tasks involving more than two users. Here we demonstrate quantum conference key agreement, a quantum communication protocol that exploits multi-partite entanglement to efficiently create identical keys between N users with up to N-1 rate advantage in constrained networks. We distribute four-photon Greenberger-Horne-Zeilinger (GHZ) states generated by high-brightness, telecom photon-pair sources across up to 50 km of fibre, implementing multi-user error correction and privacy amplification on resulting raw keys. Under finite-key analysis, we establish $1.15times10^6$ bits of secure key, which are used to encrypt and securely share an image between the four users in a conference transmission. We have demonstrated a new protocol tailored for multi-node networks leveraging low-noise, long-distance transmission of GHZ states that will pave the way forward for future multiparty quantum information processing applications.
Quantum aided Byzantine agreement (QBA) is an important distributed quantum algorithm with unique features in comparison to classical deterministic and randomized algorithms, requiring only a constant expected number of rounds in addition to giving higher level of security. In this paper, we analyze details of the high level multi-party algorithm, and propose elements of the design for the quantum architecture and circuits required at each node to run the algorithm on a quantum repeater network. Our optimization techniques have reduced the quantum circuit depth by 44% and the number of qubits in each node by 20% for a minimum five-node setup compared to the design based on the standard arithmetic circuits. These improvements lead to an architecture with $KQ approx 1.3 times 10^{5}$ per node and error threshold $1.1 times 10^{-6}$ for the total nodes in the network. The evaluation of the designed architecture shows that to execute the algorithm once on the minimum setup, we need to successfully distribute a total of 648 Bell pairs across the network, spread evenly between all pairs of nodes. This framework can be considered a starting point for establishing a road-map for light-weight demonstration of a distributed quantum application on quantum repeater networks.