Benchmark of Pauli Correlation Encoding for different optimisation problems
- URL: http://arxiv.org/abs/2606.18914v2
- Date: Mon, 22 Jun 2026 08:08:36 GMT
- Title: Benchmark of Pauli Correlation Encoding for different optimisation problems
- Abstract summary: We study a quantum-classical optimisation framework based on Pauli Correlation.<n>The proposed PCE-based framework achieves competitive performance against the benchmark.
- Score: 38.36049497195
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The continuous progress of quantum technologies has spurred the exploration of their potential applications across diverse fields, particularly in combinatorial optimisation. In this work, we study a quantum-classical optimisation framework based on Pauli Correlation Encoding, an encoding scheme that can represent m binary variables using a polynomial number of qubits. To evaluate the performance of the method, we use three classical optimisation problems against the instances of the QOPTLib benchmark. The study includes an analysis of the impact of the compression order of the encoding scheme, the problem structure, and hyperparameter selection on solution quality, as well as the role of post-processing in improving performance. Additionally, we study the effect of shot-based execution and hardware noise, showing how these factors influence both the accuracy of expected value estimation and the overall dynamics of the optimisation process. The results indicate that the proposed PCE-based framework achieves competitive performance against the benchmark and, in several cases, obtains equivalent or even superior solutions, highlighting its potential as an efficient encoding strategy for quantum optimisation in the NISQ and near fault-tolerant era.
Related papers
- Enhancing Pauli Correlation Encoding for quantum optimization via systematic expressivity analysis [2.6945797019995363]
Pauli Correlation atz (PCE) has emerged as a promising framework for quantum devices with limited qubit resources.<n>We show that PCE attains comparable solution quality with substantially fewer trainable parameters.<n>We propose a multistage continuation framework that gradually transforms a smooth relaxed objective into a sharper objective.
arXiv Detail & Related papers (2026-09-09T05:03:07Z) - Scalable Variational Quantum Optimization via Pauli Correlation Encoding: Application to Large-Scale Power Demand Portfolio Optimization [14.022615381217628]
We introduce a scalable variational framework based on Pauli correlation encoding (PCE) and apply it to electric power demand portfolio optimization.<n> Numerical simulations demonstrate near-optimal performance across problem sizes ranging from $m$=18 to 10,296, with normalized cost gaps on the order of $10-4$ relative to solutions with certified optimality.
arXiv Detail & Related papers (2026-07-27T17:55:23Z) - Pauli Correlation Encoding for Budget-Constrained Optimization [35.18016233072556]
Pauli Correlation.<n>(PCE) was recently introduced as an alternative paradigm that reduces qubit requirements by embedding problem variables into Pauli correlations.<n>We extend the PCE framework to constrained optimization problems and evaluate its performance across multiple problem sizes.
arXiv Detail & Related papers (2026-02-19T15:47:13Z) - Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
arXiv Detail & Related papers (2026-01-10T02:36:03Z) - A Comparative Study of Quantum Optimization Techniques for Solving Combinatorial Optimization Benchmark Problems [4.266376725904727]
We introduce a comprehensive benchmarking framework designed to evaluate quantum optimization techniques against well-established NP-hard problems.<n>Our framework focuses on key problem classes, including the Multi-Dimensional Knapsack Problem (MDKP), Maximum Independent Set (MIS), Quadratic Assignment Problem (QAP), and Market Share Problem (MSP)
arXiv Detail & Related papers (2025-03-15T13:02:22Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Benchmarking Metaheuristic-Integrated QAOA against Quantum Annealing [0.0]
The study provides insights into the strengths and limitations of both Quantum Annealing and metaheuristic-integrated QAOA across different problem domains.
The findings suggest that the hybrid approach can leverage classical optimization strategies to enhance the solution quality and convergence speed of QAOA.
arXiv Detail & Related papers (2023-09-28T18:55:22Z) - An Empirical Evaluation of Zeroth-Order Optimization Methods on
AI-driven Molecule Optimization [78.36413169647408]
We study the effectiveness of various ZO optimization methods for optimizing molecular objectives.
We show the advantages of ZO sign-based gradient descent (ZO-signGD)
We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
arXiv Detail & Related papers (2022-10-27T01:58:10Z) - Quantum variational optimization: The role of entanglement and problem
hardness [0.0]
We study the role of entanglement, the structure of the variational quantum circuit, and the structure of the optimization problem.
Our numerical results indicate an advantage in adapting the distribution of entangling gates to the problem's topology.
We find evidence that applying conditional value at risk type cost functions improves the optimization, increasing the probability of overlap with the optimal solutions.
arXiv Detail & Related papers (2021-03-26T14:06:54Z) - Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.
We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT)
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
arXiv Detail & Related papers (2020-10-01T18:14:11Z) - Cross Entropy Hyperparameter Optimization for Constrained Problem
Hamiltonians Applied to QAOA [68.11912614360878]
Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications.
Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem.
In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
arXiv Detail & Related papers (2020-03-11T13:52:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.