Haomu Yuan

Transformers as Intrinsic Optimizers for Quantum Approximate Optimization Algorithm

Kuan-Cheng Chen, Xiaotian Xu, Hiromichi Matsuyama, Wei-Hao Huang et autres

The Quantum Approximate Optimization Algorithm (QAOA) is a leading variational framework for combinatorial optimization on noisy intermediate-scale quantum hardware, but its practical performance depends strongly on the classical optimizer used to train its variational parameters. This outer-loop optimization is often nonconvex, initialization-sensitive, …

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Haomu Yuan

gb (code pays fourni par la source)

Test of uploading

Haomu Yuan

gb (code pays fourni par la source)

Towards Exponential Quantum Improvements in Solving Cardinality-Constrained Binary Optimization

Haomu Yuan, Hanqing Wu, Kuan-Cheng Chen, Bin Cheng et autres

Cardinality-constrained binary optimization is a fundamental computational primitive with broad applications in machine learning, finance, and scientific computing. In this work, we introduce a Grover-based quantum algorithm that exploits the structure of the fixed-cardinality feasible subspace under a natural promise on solution …

Towards Exponential Quantum Improvements in Solving Cardinality-Constrained Binary Optimization

Haomu Yuan, Hanqing Wu, Kuan-Cheng Chen, Bin Cheng et autres

Cardinality-constrained binary optimization is a fundamental computational primitive with broad applications in machine learning, finance, and scientific computing. In this work, we introduce a Grover-based quantum algorithm that exploits the structure of the fixed-cardinality feasible subspace under a natural promise on solution …

sg, gb, Soudan du Sud, se (code pays fourni par la source)

Quantifying the advantages of applying quantum approximate algorithms to portfolio optimisation

Haomu Yuan, Christopher K. Long, Hugo V. Lepage, Crispin H.W. Barnes

Abstract We present a quantum algorithm for portfolio optimisation. Specifically, We present an end-to-end quantum approximate optimisation algorithm to solve the discrete global minimum variance portfolio model. This model finds a portfolio of risky assets with the lowest possible risk contingent on …

gb (code pays fourni par la source)

Classical Optimization Strategies for Variational Quantum Algorithms: A Systematic Study of Noise Effects and Parameter Efficiency

Tomáš Bezděk, Haomu Yuan, Silvie Illésová, Martin Beseda

This study systematically benchmarks classical optimization strategies for the Quantum Approximate Optimization Algorithm when applied to Generalized Mean-Variance Problems under near-term Noisy Intermediate-Scale Quantum conditions. We evaluate Dual Annealing, Constrained Optimization by Linear Approximation, and the Powell Method across noiseless, sampling noise, …

Exponential Speed-ups for Structured Goemans-Williamson relaxations via Quantum Gibbs States and Pauli Sparsity

Haomu Yuan, Daniel Stilck França, I. A. Luchnikov, Egor Tiunov et autres

Quadratic Unconstrained Binary Optimization (QUBO) problems are prevalent in various applications and are known to be NP-hard. The seminal work of Goemans and Williamson introduced a semidefinite programming (SDP) relaxation for such problems, solvable in polynomial time that upper bounds the optimal …

Iterative quantum optimisation with a warm-started quantum state

Haomu Yuan, Songqinghao Yang, C. H. W. Barnes

We provide a method to prepare a warm-started quantum state from measurements with an iterative framework to enhance the quantum approximate optimisation algorithm (QAOA). The numerical simulations show the method can effectively address the "stuck issue" of the standard QAOA using a …

Quantifying the advantages of applying quantum approximate algorithms to portfolio optimisation

Haomu Yuan, Christopher K. Long, Hugo V. Lepage, C. H. W. Barnes

We present a quantum algorithm for portfolio optimisation. Specifically, We present an end-to-end quantum approximate optimisation algorithm (QAOA) to solve the discrete global minimum variance portfolio (DGMVP) model. This model finds a portfolio of risky assets with the lowest possible risk contingent …

A novel quantum algorithm for converting between one-hot and binary encodings

Bingren Chen, Han-Qing Wu, Haomu Yuan, Lei Wu et autres

Quasi-binary encoding based quantum alternating operator ansatz

Bingren Chen, Hanqing Wu, Haomu Yuan, Lei Wu et autres

This paper proposes a quasi-binary encoding based algorithm for solving a specific quadratic optimization models with discrete variables, in the quantum approximate optimization algorithm (QAOA) framework. The quadratic optimization model has three constraints: 1. Discrete constraint, the variables are required to be …