arXiv subjects

Milad Hasanzadeh

Publications and source records attributed to Milad Hasanzadeh.

10 recordsLinked to original sources

arXiv2026

A Distributed Quantum Approximate Optimization Algorithm For Unit Commitment

This paper presents a distributed quantum approximate optimization algorithm (DQAOA)-enabled three-block alternating direction method of multipliers (ADMM) framework for unit commitment (UC). The relaxed commitment and dispatch variables are solved in a continuous quadratic programming block, while the binary commitment block is formulated as a quadratic unconstrained binary optimization (QUBO) problem. The DQAOA interface allows this QUBO to be solved using brute-force enumeration, monolithic QAOA, or distributed QAOA, while the remaining ADMM updates are kept unchanged. In the distributed mode, the logical commitment qubits are allocated across multiple capacity-constrained quantum processing units (QPU), avoiding the requirement that the complete binary problem fits on a single device. The framework is evaluated on a five-unit UC instance containing 15 binary variables. All three solver modes reduce the ADMM primal residual below a certain tolerance and recover the same commitment schedule, dispatch, and operating cost. The results demonstrate solution consistency across the three solver modes and the multi-QPU capacity accommodation provided by the distributed QAOA method.

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arXiv2026

Integrated Eco-Driving and Powertrain Optimization for Hybrid Vehicles in Complex Urban Traffic

Urban eco-driving requires the simultaneous planning of speed, acceleration, lane decisions, surrounding-vehicle safety, intersection rules, and vehicle energy use. Existing studies commonly optimize only selected aspects of urban driving, such as longitudinal motion, lane changing, intersection crossing, or powertrain energy management, while treating the remaining decisions separately. Unlike these studies, this paper develops an integrated finite-horizon eco-driving planning framework for urban hybrid vehicles that jointly optimizes traffic behavior and powertrain operation within a unified mixed-integer formulation. The novelty of the proposed method lies in simultaneously modeling longitudinal motion, lane occupancy and changes, car-following and lane-change safety, signalized and unsignalized intersection behavior, and hybrid powertrain operation. The formulation captures lane availability, lane-dependent speed limits, mandatory and emergency lane changes, safe-to-clear signal conditions, downstream clearance, stop-and-yield rules, engine and motor power, battery state of charge, regenerative braking, and fuel consumption. Simulation studies compare the proposed method with rule-following, signal-aware, overtaking-enabled, and kinematic-only optimization baselines. The results demonstrate improved energy performance while maintaining feasible, safe, comfortable, and traffic-rule-compliant urban driving plans.

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arXiv2026

A Distributed Quantum Approximate Optimization Algorithm Simulator for Engineering Design Optimization

This paper presents a Qiskit-compatible distributed quantum approximate optimization algorithm (DQAOA) simulator for quadratic unconstrained binary optimization (QUBO) problems arising in engineering design and decision applications. The open-source simulator is available through the RAISE LAB website and GitHub repository, with README documentation for installation, input formatting, configurable parameters, and example workflows. The package addresses the need for a reusable simulator that can solve and compare QUBO instances across different QAOA execution modes. It supports monolithic QAOA on a single quantum processing unit (QPU) and distributed QAOA across a user-specified number of QPUs with configurable capacities. The workflow canonicalizes the QUBO model, maps it to a cost Hamiltonian, allocates variables across QPUs, identifies local and cross-QPU couplings, and constructs the corresponding circuits. Runtime optimizations, including parameterized circuit reuse, objective reuse at fixed depth, batched evaluations, and parallel multi-start execution, reduce repeated overhead. A Streamlit graphical user interface is also provided for entering or uploading QUBO instances, configuring solver settings, running selected modes, and visualizing solution-quality metrics without editing Python scripts. The package is demonstrated on standalone QUBO benchmarks and a power generation unit commitment application. In the unit commitment case, brute force, monolithic QAOA, and distributed QAOA recover the same commitment bitstring and operating cost. Across multiple case studies, the simulator produces results consistent with classical monolithic QAOA references in terms of optimal bitstrings and costs. Staged runtime analysis shows substantial runtime reduction across implementation stages, while distributed QAOA remains more demanding because cross-QPU couplings require remote operations.

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arXiv2026

Dynamic Quantum-Assisted Co-Design of Controller and Lyapunov Candidate Parameters for Nonlinear Systems

This paper proposes a dynamic quantum-assisted co-design framework for nonlinear closed-loop systems in which controller parameters and Lyapunov candidate parameters are redesigned jointly at successive decision epochs. Unlike conventional nonlinear control designs that typically tune controller gains offline and perform stability analysis separately, the proposed method embeds performance improvement and sample-based Lyapunov verification within a unified online optimization loop. The main novelty is a two-step computational structure that first contracts the continuous admissible search region around the current operating condition using a Black-Hole calibration procedure and then constructs a finite binary representation only over this calibrated region. The encoded objective is obtained from sampled nonlinear closed-loop evaluations and approximated by a local quadratic pseudo-Boolean surrogate, enabling an Ising-type Hamiltonian representation suitable for quantum-assisted optimization. Quantum imaginary time evolution is then used to explore the encoded Hamiltonian, and the resulting candidate bitstrings are decoded into continuous controller and Lyapunov parameters. To reduce dependence on the surrogate model, the decoded candidates are re-evaluated using the original nonlinear closed-loop cost and Lyapunov penalties before the final update is applied. The framework can accommodate sampled forms of different Lyapunov decay specifications by modifying the corresponding penalty and is numerically evaluated on first-order nonlinear consensus, second-order nonlinear consensus, and induction motor drive control examples. The implementation code used to generate the reported results is available at \href{https://github.com/LSU-RAISE-LAB/DQCLS-NS}{GitHub}.

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arXiv2026

Dynamic Quantum Optimal Communication Topology Design for Consensus Control in Linear Multi-Agent Systems

This paper proposes a quantum framework for the design of communication topologies in consensus-based multi-agent systems. The communication graph is selected online by solving a mixed-integer quadratic program (MIQP) that minimizes a cost combining communication and distance penalties with degree-regularization terms, while enforcing exact connectivity through a flow-based formulation. To cope with the combinatorial complexity of this NP-hard problem, we develop a three-block ADMM scheme that decomposes the MIQP into a convex quadratic program in relaxed edge and flow variables, a pure binary unconstrained subproblem, and a closed-form auxiliary update. The binary subproblem is mapped to a quadratic unconstrained binary optimization (QUBO) Hamiltonian and approximately solved via quantum imaginary time evolution (QITE). The resulting time-varying, optimizer-generated Laplacians are applied to linear first- and second-order consensus dynamics. Numerical simulations on networks demonstrate that the proposed method produces connected topologies that satisfy degree constraints, achieve consensus, and incur costs comparable to those of classical mixed-integer solvers, thereby illustrating how quantum algorithms can be embedded as topology optimizers within closed-loop distributed control architectures.

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arXiv2026

All-Pass Fractional OPF: A Solver-Friendly, Physics-Preserving Approximation of AC OPF

This paper presents a fractional approximation of the AC optimal power flow (AC OPF) problem based on an all-pass approximation of the exponential power flow kernel. The classical AC OPF relies on trigonometric coupling between bus voltage phasors, which yields a nonconvex program with oscillatory derivatives that can slow, or in some cases destabilize, interior-point methods. We replace the trigonometric terms with an all-pass fractional (APF) approximation whose real and imaginary components act as smooth surrogates for the cosine and sine functions, and we introduce a pre-rotation to shift the argument of the approximation toward its most accurate region, ensuring that the reformulated power flow model preserves physical loss behavior, maintains the symmetry of the classical kernels, and improves the conditioning of the Jacobian and Hessian matrices. The proposed APF OPF formulation remains nonconvex, as in the classical model, but it eliminates trigonometric evaluations and empirically produces larger and more stable Newton steps under standard interior-point solvers. Numerical results on more than 25 IEEE and PGLib test systems ranging from 9 to 10{,}000 buses demonstrate that the APF OPF model achieves solutions with accuracy comparable to that of the classical formulation while reducing solver times, indicating a more solver-friendly nonconvex representation of AC OPF. All code, functions, verification scripts, and generated results are publicly available on \href{https://github.com/LSU-RAISE-LAB/APF-OPF}{GitHub}, along with a README describing how to run and reproduce the experiments.

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arXiv2026

A Survey on Applications of Quantum Computing for Unit Commitment

Unit Commitment (UC) is a core optimization problem in power system operation and electricity market scheduling. It determines the optimal on/off status and dispatch of generating units while satisfying system, operational, and market constraints. Traditionally, UC has been solved using mixed-integer programming, dynamic programming, or metaheuristic methods, all of which face scalability challenges as systems grow in size and uncertainty. Recent advances in quantum computing, spanning quantum annealing, variational algorithms, and hybrid quantum classical optimization, have opened new opportunities to accelerate UC solution processes by exploiting quantum parallelism and entanglement. This paper presents a comprehensive survey of existing research on the applications of quantum computing for solving the UC problem. The reviewed works are categorized based on the employed quantum paradigms, including annealing-based, variational hybrid, quantum machine learning, and quantum-inspired methods. Key modeling strategies, hardware implementations, and computational trade-offs are discussed, highlighting the current progress, limitations, and potential future directions for large-scale quantum-enabled UC.

quant-ph↗

arXiv2025

D2-UC: A Distributed-Distributed Quantum-Classical Framework for Unit Commitment

This paper introduces D2-UC, a quantum-ready framework for the unit commitment (UC) problem that prepares UC for near-term hybrid quantum-classical solvers by combining distributed classical decomposition with distributed quantum execution. We reformulate deterministic and stochastic UC into a three-block alternating direction method of multipliers (ADMM): (i) a convex quadratic subproblem for dispatch and reserves, (ii) a binary subproblem expressed as a quadratic unconstrained binary optimization (QUBO), and (iii) a proximal slack update for consensus. The core contributions are fivefold. First, we demonstrate how the full UC problem can be expressed as a single monolithic QUBO, establishing a direct interface to quantum solvers. Second, we decompose this large binary block into three type-specific QUBOs for commitment, startup, and shutdown, making the problem more tractable but revealing slower ADMM convergence. Third, we restore local logical couplings through per-unit-time micro-QUBOs, which accelerate convergence. Fourth, we batch micro-QUBOs into K non-overlapping block-diagonal problems, reducing many subproblems to a fixed number of solver-ready QUBOs per iteration, compatible with distributed variational quantum eigensolvers (DVQE). Fifth, we integrate an accept-if-better safeguard with DVQE to stabilize hybrid updates and prevent oscillations. Case studies confirm that the proposed methods deliver feasible schedules, faster convergence, and QUBO sizes aligned with current and near-term quantum hardware capabilities. All detailed data, codes, and parameter values are available at https://github.com/LSU-RAISE-LAB/3B-ADMM-UC-DVQE .

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arXiv2025

Two-stage Distributed Variational Quantum Eigensolver Software for QUBO and Quadratic Programming

This paper proposes a two-stage distributed variational quantum eigensolver (DVQE) software for solving quadratic unconstrained binary optimization (QUBO) problems and bounded constrained quadratic programming (QP) problems. The proposed DVQE solver supports both monolithic and distributed quantum-circuit execution and evaluates QUBO objectives directly from measured bitstrings. To improve variational training, DVQE uses a two-stage procedure that combines metaheuristic warm-start initialization with sampling-based variational refinement. The software supports several metaheuristic approaches as warm-start strategies. To extend QUBO-based quantum optimization to constrained continuous problems, this paper also develops a sequential QP to QUBO framework, called QQP. QQP first scales the bounded continuous variables to a normalized box and then handles equality and inequality constraints using a Powell-Hestenes-Rockafellar (PHR) augmented-Lagrangian formulation. Under a fixed PHR active region, the constrained augmented-Lagrangian subproblem becomes an ordinary bounded quadratic problem. QQP then solves this bounded quadratic problem through repeated local one-bit QUBO reformulations, where each binary variable represents a local up/down move of one continuous variable inside a trust region. In this way, QQP converts a constrained continuous QP into a sequence of QUBO subproblems without introducing slack variables. Each local QUBO subproblem can be solved using either a classical QUBO backend or the proposed DVQE solver. Numerical experiments evaluate the proposed software on QUBO and QP test problems. The results show that the distributed DVQE framework can recover high-quality QUBO solutions, and that the QQP framework can solve bounded constrained QP instances with small optimality, feasibility, and solution gaps.

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arXiv2025

A Benchmark Library for Distributed Power System Analysis and Optimization

DPLib is an open-source benchmark library created to support research and development in distributed power system analysis and optimization. Unlike centralized tools such as MATPOWER and PGLib, no general purpose, reproducible data library package currently exists for distributed power system studies. DPLib, available at \href{https://github.com/LSU-RAISE-LAB/DPLib.git}{GitHub}, fills this gap by providing 40 multi-region benchmark test cases ranging from 5 buses to 20758 buses, along with a graph-based partitioning toolkit that converts MATPOWER-compatible systems into distributed regional datasets. The toolkit generates standardized \texttt{.mat}, \texttt{.csv}, and \texttt{.m} files, regional MATPOWER version 2 cases, local and global bus mappings, generator and cost assignments, explicit inter-regional tie-line records, and bus-to-region partition maps. It supports unweighted, electrically weighted, and user-defined partitions, and is compared with METIS, KaFFPa, and an IPA-inspired baseline. DPLib also provides ADMM-based distributed DC and AC OPF solvers for validation. Numerical studies report partitioning sensitivity, centralized run times, distributed OPF iterations, run times, and optimality gaps. These results establish DPLib as a reproducible data layer for distributed power system research.

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