Hybrid Quantum-Classical Multi-cut Benders Approach with a Power System Application

Paterakis, Nikolaos G.

doi:10.48550/arxiv.2112.05643

Cited by 2 publications

(3 citation statements)

References 37 publications

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“…To solve MILP problems, [14] and [15] have presented hybrid techniques based on the Benders technique to decompose the original problem into a MILP master problem and a convex linear programming subproblem. In [14], continuous variables are discretized to convert the master problem into a QUBO.…”

Section: Introductionmentioning

confidence: 99%

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Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Mahroo,

Kargarian

2023

IEEE Trans. Quantum Eng.

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The advent of quantum computing can potentially revolutionize how complex problems are solved. This paper proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distributed optimization. The aim is to facilitate employing noisy near-term quantum machines with a limited number of qubits to solve practical power system optimization problems such as generation scheduling. The outer loop is a 3-block quantum alternative direction method of multipliers (QADMM) algorithm that decomposes the generation scheduling problem into three subproblems, including one quadratically unconstrained binary optimization (QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The proposed T-QAOA translates interactions of quantumclassical machines as sequential information and uses a recurrent neural network to estimate variational parameters of the quantum circuit with a proper sampling technique. T-QAOA determines the QUBO solution in a few quantum-learner iterations instead of hundreds of iterations needed for a quantum-classical solver. The outer 3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to the original problem. The conditions under which the proposed QADMM is guaranteed to converge are discussed. Two mathematical and three generation scheduling cases are studied. Analyses performed on quantum simulators and classical computers show the effectiveness of the proposed algorithm. The advantages of T-QAOA are discussed and numerically compared with QAOA which uses a stochastic gradient descent-based optimizer.

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Section: Introductionmentioning

confidence: 99%

“…This discretization needs ancillary qubits. In [15], a Benders cut selection scheme manages the size of the master problem. The cut selection strategy is a QUBO problem assigned to a QPU.…”

Section: Introductionmentioning

confidence: 99%

Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Mahroo,

Kargarian

2023

IEEE Trans. Quantum Eng.

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show abstract

“…To solve MIP problems, [14] and [15] have presented hybrid techniques based on the Benders technique to decompose the original problem into a MIP master problem and a convex linear programming subproblem. In [14], continuous variables are discretized to convert the master problem into a QUBO.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Quantum-Classical Unit Commitment

Mahroo

Kargarian

2022

2022 IEEE Texas Power and Energy Conference (TPEC)

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The advent of quantum computing can potentially revolutionize how complex problems are solved. This paper proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distributed optimization. The aim is to facilitate employing noisy near-term quantum machines with a limited number of qubits to solve practical power system optimization problems such as generation scheduling. The outer loop is a 3-block quantum alternative direction method of multipliers (QADMM) algorithm that decomposes the generation scheduling problem into three subproblems, including one quadratically unconstrained binary optimization (QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The proposed T-QAOA translates interactions of quantum-classical machines as sequential information and uses a recurrent neural network to estimate variational parameters of the quantum circuit with a proper sampling technique. T-QAOA determines the QUBO solution in a few quantum-learner iterations instead of hundreds of iterations needed for a quantumclassical solver. The outer 3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to the original problem. The conditions under which the proposed QADMM is guaranteed to converge are discussed. Two mathematical and three generation scheduling cases are studied. Analyses performed on quantum simulators and classical computers show the effectiveness of the proposed algorithm. The advantages of T-QAOA are discussed and numerically compared with QAOA which uses a stochastic gradient descent-based optimizer.

show abstract

Hybrid Quantum-Classical Multi-cut Benders Approach with a Power System Application

Cited by 2 publications

References 37 publications

Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Hybrid Quantum-Classical Unit Commitment

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