Supply Chain Logistics with Quantum and Classical Annealing Algorithms

Weinberg, Sean J.; Sanches, Fábio de Oliveira; Ide, Takanori; Kamiya, Kazumitzu; Correll, Randall R.

doi:10.48550/arxiv.2205.04435

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…One common approach for tackling a wide variety of combinatorial optimization problems is to convert them into quadratic unconstrained binary optimization (QUBO) problems, map the problem to an Ising Hamiltonian, and then determine the Hamiltonian's ground state by variational quantum eigensolver (VQE) [9]- [13] or quantum annealing [3], [5], where the ground state corresponds to the solution of the original problem [3], [4]. Quantum optimization has been successfully applied to real-world challenges, such as portfolio optimization [5]- [8], industrial optimization [14], [15], recommendation system [16], [17], and traveling salesman problems [18], [19].…”

Section: Introductionmentioning

confidence: 99%

University Technology Commercialization in Taiwan: National Taiwan University (NTU) and National Taiwan University of Science and Technology (NTUST)

Liu¹,

Chen²,

Chiou³

et al. 2011

Academic Entrepreneurship in Asia

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The large amount of data collected by LiDAR sensors brings the issue of LiDAR point cloud compression (PCC). Previous works on LiDAR PCC have used range image representations and followed the predictive coding paradigm to create a basic prototype of a coding framework. However, their prediction methods give an inaccurate result due to the negligence of invalid pixels in range images and the omission of future frames in the time step. Moreover, their handcrafted design of residual coding methods could not fully exploit spatial redundancy. To remedy this, we propose a coding framework BIRD-PCC. Our prediction module is aware of the coordinates of invalid pixels in range images and takes a bidirectional scheme. Also, we introduce a deep-learned residual coding module that can further exploit spatial redundancy within a residual frame. Experiments conducted on SemanticKITTI and KITTI-360 datasets show that BIRD-PCC outperforms other methods in most bitrate conditions and generalizes well to unseen environments.

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Section: Introductionmentioning

confidence: 99%

University Technology Commercialization in Taiwan: National Taiwan University (NTU) and National Taiwan University of Science and Technology (NTUST)

Liu¹,

Chen²,

Chiou³

et al. 2011

Academic Entrepreneurship in Asia

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“…( 3), and then VQE is used to find the optimal coefficients with the cost function of Eq. (10). the full-system ground state energy using the full-system Hamiltonian H with the VQE algorithm.…”

Section: Amplitude Optimizationmentioning

confidence: 99%

“…A popular choice for solving a large class of combinatorial optimization problems that can be converted into quadratic unconstrained binary optimization (QUBO) problems is mapping the problem to an Ising Hamiltonian and solving for the corresponding ground state of the Hamiltonian, which is related to the solution of the original problem [4,5]. Applications of quantum optimization to real-world problems have been demonstrated for portfolio optimizations [6][7][8][9], industrial optimization problems [10,11], and traveling salesman problems [12,13]. To find the optimal solution of the corresponding Hamiltonian, the gate-based quantum computing with variational quantum eigensolver (VQE) uses parameterized gates to construct a trial state and optimizes the best set of parameters that can approximate the ground state of the Ising Hamiltonian [14].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems

Liu¹,

Goan²

2022

Preprint

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One of the major problems of most quantum computing applications is that the required number of qubits to solve a practical problem is much larger than that of today's quantum hardware. Therefore, finding a way to make the best of today's quantum hardware has become a critical issue. In this work, we propose an algorithm, called large-system sampling approximation (LSSA), to solve Ising problems with sizes up to N gb 2 N gb by an N gb -qubit gate-based quantum computer, and with sizes up to Nan2 N gb by a hybrid computational architecture of an Nan-qubit quantum annealer and an N gbqubit gate-based quantum computer. By dividing the full-system problem into smaller subsystem problems, the LSSA algorithm then solves the subsystem problems by either gate-based quantum computers or quantum annealers, and optimizes the amplitude contributions of the solutions of the different subsystems with the full-problem Hamiltonian by the variational quantum eigensolver (VQE) on a gate-based quantum computer. After optimizing the VQE amplitude contributions, the approximated ground-state configuration, which is the approximated solution of a corresponding quadratic unconstrained binary optimization (QUBO) problem, will be determined. We apply the level-1 approximation of LSSA to solving fully-connected random Ising problems up to 160 variables using a 5-qubit gate-based quantum computer, and solving portfolio optimization problems up to 4096 variables using a 100-qubit quantum annealer and a 7-qubit gate-based quantum computer. Moreover, LSSA can be further extended to a deeper level of approximation. We demonstrate the use of the level-2 approximation of LSSA to solve the portfolio optimization problems up to 5120 (N gb 2 2N gb ) variables with pretty good performance by using just a 5-qubit (N gb -qubit) gate-based quantum computer. The effects of different subsystem sizes, numbers of subsystems, and full problem sizes on the performance of LSSA are investigated on both simulators and real hardware. The completely new computational concept of the hybrid gate-based and annealing quantum computing architecture opens a promising possibility to investigate large-size Ising problems and combinatorial optimization problems, making practical applications by quantum computing possible in the near future.

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Section: Introductionmentioning

confidence: 99%

Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems

Liu,

Goan

2024

Preprint

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One of the major problems of quantum computing applications is that the required number of qubits to solve a practical problem is much larger than that of today's quantum hardware. In this work, we introduce the large-system sampling approximation (LSSA) algorithm to solve Ising problems with sizes up to Ngb2Ngb by an Ngb-qubit gate-based quantum computer, and problems with sizes up to Nan2Ngb by a hybrid computational architecture of an Nan-qubit quantum annealer and an Ngb-qubit gate-based quantum computer. By dividing the full-system problem into smaller subsystem problems, the LSSA algorithm then solves the subsystem problems by either gate-based quantum computers or quantum annealers, and optimizes the amplitude contributions of the solutions of the different subsystems with the full-problem Hamiltonian by the variational quantum eigensolver (VQE) on a gate-based quantum computer to determine the approximated ground-state configuration. LSSA has polynomial time complexity and can be further extended to a deeper level of approximation with computational overhead that grows linearly with the problem size. The effects of different subsystem sizes, numbers of subsystems, and full problem sizes on the performance of LSSA are investigated on both simulators and real hardware. The completely new computational concept of the hybrid gate-based and annealing quantum computing architecture opens a promising possibility to investigate large-size Ising problems and combinatorial optimization problems, making practical applications by quantum computing possible in the near future.

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Supply Chain Logistics with Quantum and Classical Annealing Algorithms

Cited by 4 publications

References 12 publications

University Technology Commercialization in Taiwan: National Taiwan University (NTU) and National Taiwan University of Science and Technology (NTUST)

University Technology Commercialization in Taiwan: National Taiwan University (NTU) and National Taiwan University of Science and Technology (NTUST)

Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems

Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems

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