14 papers
Custom Penalty QAOA via Walsh-Hadamard Transform
Xin Wei LEE, Siong Thye GOH, Hoong Chuin LAU
Inequality constraints in combinatorial optimization are conventionally handled by introducing slack variables and quadratic penalties, which increase the qubit count and distort t…
Slepian Bounds on the Success Probability of Virtual Distillation
Masayuki Ohzeki, Sasuke Kimata, Xinwei Lee +1
Virtual distillation is a powerful near-term error-mitigation primitive, but it is also a spectral filter: it amplifies the dominant eigenvector component already present in the no…
From Circuits to Hardware: Benchmarking Standard and Qubit-Efficient Quantum Optimization on Real Hardware
Monit Sharma, Hoong Chuin Lau
Despite rapid progress in quantum optimization, broad real-hardware benchmarks comparing multiple algorithmic families across diverse combinatorial problems under a common protocol…
Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum Execution
Monit Sharma, Hoong Chuin Lau
Hybrid quantum optimization for vehicle routing faces a practical bottleneck: direct QUBO encodings of CVRP quickly exceed near-term qubit and gate budgets, while quantum evaluatio…
AutoQResearch: LLM-Guided Closed-Loop Policy Search for Adaptive Variational Quantum Optimization
Monit Sharma, Hoong Chuin Lau
Configuring variational quantum algorithms for combinatorial optimization remains a difficult, expert-driven process requiring coordinated choices over solver family, ansatz, objec…
CVaR-Assisted Custom Penalty Function for Constrained Optimization
Xin Wei Lee, Hoong Chuin Lau
We propose a slack-free penalty formulation for constrained binary optimization that eliminates auxiliary slack variables and preserves the feasibility structure of the original pr…
Understanding the Nature of Depth-1 Equivariant Quantum Circuit
Jonathan Teo, Lee Xin Wei, Hoong Chuin Lau
The Equivariant Quantum Circuit (EQC) for the Travelling Salesman Problem (TSP) has been shown to achieve near-optimal performance in solving small TSP problems (up to 20 nodes) us…
Diagnosing Simulation and Hardware Barriers to Cross-Size Transfer in Equivariant Quantum Reinforcement Learning
Monit Sharma, Hoong Chuin Lau
Equivariant quantum circuits (EQCs) parameterise reinforcement-learning policies for combinatorial optimisation with a size-independent parameter count, suggesting policies trained…
Hybrid Learning and Optimization methods for solving Capacitated Vehicle Routing Problem
Monit Sharma, Hoong Chuin Lau
The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem in logistics. Augmented Lagrangian Methods (ALM) for solving CVRP performance depends heavily on wel…
Cutting Slack: Quantum Optimization with Slack-Free Methods for Combinatorial Benchmarks
Monit Sharma, Hoong Chuin Lau
Constraint handling remains a key bottleneck in quantum combinatorial optimization. While slack-variable-based encodings are straightforward, they significantly increase qubit coun…
Adaptive Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems
Monit Sharma, Hoong Chuin Lau
A range of quantum algorithms, especially those leveraging variational parameterization and circuit-based optimization, are being studied as alternatives for solving classically in…
Implementing Slack-Free Custom Penalty Function for QUBO on Gate-Based Quantum Computers
Xin Wei Lee, Hoong Chuin Lau
Solving NP-hard constrained combinatorial optimization problems using quantum algorithms remains a challenging yet promising avenue toward quantum advantage. Variational Quantum Al…
Solving Constrained Combinatorial Optimization Problems with Variational Quantum Imaginary Time Evolution
Xin Wei Lee, Hoong Chuin Lau
Solving combinatorial optimization problems using variational quantum algorithms (VQAs) has emerged as a promising research direction. Since the introduction of the Quantum Approxi…
Light Cone Cancellation for Variational Quantum Eigensolver in Solving Noisy Max-Cut
Xinwei Lee, Xinjian Yan, Ningyi Xie +5
Variational Quantum Eigensolver (VQE) is a quantum-classical hybrid algorithm used to estimate the ground energy of a given Hamiltonian. It consists of a parameterized quantum circ…