n-qubit operations on sphere and queueing scaling limits for programmable quantum computer

Dai, Wanyang

doi:10.1007/s11128-023-03851-3

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…The first example is corresponding to a 2-user case as shown in Figure 7, which can be used to model the DaiCoin based digital payment system with two types of Ethereums (corresponding to two DaiCoins) as in Maker [18]. It can also be used to model a MIMO wireless communication channel (i.e., a single base station equipped with two antennas) shared by two-users or a quantum computer with two eigenmodes (see e.g., Dai [9,12]). In this case, we are interested in the problem about how to price the two users and conduct the computing rate (i.e., power) resource allocation cooperatively inside a service system.…”

Section: The First Examplementioning

confidence: 99%

Game-theoretic policy computing and simulation for blockchained buffering system via diffusion approximation

Dai

2024

Prob. Eng. Inf. Sci.

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We study 2-stage game-theoretic problem oriented 3-stage service policy computing, convolutional neural network (CNN) based algorithm design, and simulation for a blockchained buffering system with federated learning. More precisely, based on the game-theoretic problem consisting of both “win-lose” and “win-win” 2-stage competitions, we derive a 3-stage dynamical service policy via a saddle point to a zero-sum game problem and a Nash equilibrium point to a non-zero-sum game problem. This policy is concerning users-selection, dynamic pricing, and online rate resource allocation via stable digital currency for the system. The main focus is on the design and analysis of the joint 3-stage service policy for given queue/environment state dependent pricing and utility functions. The asymptotic optimality and fairness of this dynamic service policy is justified by diffusion modeling with approximation theory. A general CNN based policy computing algorithm flow chart along the line of the so-called big model framework is presented. Simulation case studies are conducted for the system with three users, where only two of the three users can be selected into the service by a zero-sum dual cost game competition policy at a time point. Then, the selected two users get into service and share the system rate service resource through a non-zero-sum dual cost game competition policy. Applications of our policy in the future blockchain based Internet (e.g., metaverse and web3.0) and supply chain finance are also briefly illustrated.

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Section: The First Examplementioning

confidence: 99%

Game-theoretic policy computing and simulation for blockchained buffering system via diffusion approximation

Dai

2024

Prob. Eng. Inf. Sci.

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“…In 2023, Mariella et al. [ 22 ] proposed a qubit routing optimization method for 2DNN architecture based on doubly stochastic matrices. Compared with SABER, this method had a significant improvement in the number of SWAP gates added and the calculation time of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Mapping Quantum Circuits to 2D Nearest‐Neighbor Architectures

Li,

Liu,

2024

Adv Quantum Tech

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Recently, quantum computing is considered as a promising future computing paradigm. However, when implementing quantum circuits on a quantum device, it is necessary to ensure that quantum circuits satisfy nearest‐neighbor architecture constraints. When performing nearest‐neighbor architecture mapping for quantum circuits, it is inevitable to introduce SWAP gates, which will increase the overhead and reduce the fidelity. Therefore, it is crucial to complete the mapping with the minimum SWAP. In this paper, a 2D nearest‐neighbor architecture mapping method for quantum circuits is proposed based on deep reinforcement learning. In the initial mapping, an isomorphic graph initial mapping search algorithm is designed to quickly find the isomorphic graph between quantum circuit and architecture constraint graph. For quantum circuits without isomorphic graph mapping, the reordering algorithm of qubit impact factors is designed to obtain the initial placement position of qubits. In the SWAP gate addition, a qubit local reordering algorithm based on dueling deep‐Q‐network is designed to reduce SWAP gates. Experiments on the benchmark set B131 and IBM Q20 Tokyo verify that the proposed method can add fewer SWAP gates. Compared with the state‐of‐the‐art method, the average running time is accelerated by 63.1%, and the average SWAP gates added are reduced by 24.7%.

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n-qubit operations on sphere and queueing scaling limits for programmable quantum computer

Cited by 2 publications

References 43 publications

Game-theoretic policy computing and simulation for blockchained buffering system via diffusion approximation

Game-theoretic policy computing and simulation for blockchained buffering system via diffusion approximation

Deep Reinforcement Learning for Mapping Quantum Circuits to 2D Nearest‐Neighbor Architectures

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