Hardness-dependent quantum adiabatic schedules for the maximum-independent-set problem
- URL: http://arxiv.org/abs/2410.08995v3
- Date: Thu, 12 Jun 2025 14:05:58 GMT
- Title: Hardness-dependent quantum adiabatic schedules for the maximum-independent-set problem
- Abstract summary: We propose a numerical approach to design highly efficient adiabatic schedules for analog quantum computing.<n>We show that the optimum schedules depend principally on the hardness of the problem rather than on its size.<n>This allows us to extrapolate the results to larger graphs and to successfully solve moderately hard problems using QuEra's 256-qubit Aquila computer.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a numerical approach to design highly efficient adiabatic schedules for analog quantum computing, focusing on the maximum-independent-set problem and neutral atom platforms. On the basis of a representative dataset of small graphs, we present numerical evidence that the optimum schedules depend principally on the hardness of the problem rather than on its size. These schedules perform better than the benchmark protocols and admit a straightforward implementation in the hardware. This allows us to extrapolate the results to larger graphs and to successfully solve moderately hard problems using QuEra's 256-qubit Aquila computer. We believe that extending our approach to hybrid algorithms could be the key to solving the hardest instances with the current technology, making yet another step toward real-world applications.
Related papers
- Grid-Partitioned MWIS Solving with Neutral Atom Quantum Computing for QUBO Problems [0.6649512409446104]
We propose a hybrid quantum-classical framework to address Quadratic Unconstrained Binary Optimization problems.<n>Our framework offers a promising path toward solving large-scale optimization problems efficiently.
arXiv Detail & Related papers (2025-10-21T11:36:13Z) - Approximate combinatorial optimization with Rydberg atoms: the barrier of interpretability [0.0]
We evaluate the ability of two interpretation strategies to correct errors in the recently introduced Crossing Lattice embedding.<n>It is unlikely that a scalable and generic improvement in quality can be achieved with Rydberg platforms.
arXiv Detail & Related papers (2025-07-30T15:22:50Z) - An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.
We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.
We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
arXiv Detail & Related papers (2025-04-13T11:49:53Z) - A quantum wire approach to weighted combinatorial graph optimisation problems [0.0]
We present and experimentally demonstrate an efficient encoding scheme based on chains of Rydberg-blockaded atoms.<n>We embed maximum weighted independent set (MWIS) and quadratic unconstrained binary optimization (QUBO) problems on a neutral atom architecture.
arXiv Detail & Related papers (2025-03-21T13:00:51Z) - A Quantum Genetic Algorithm Framework for the MaxCut Problem [49.59986385400411]
The proposed method introduces a Quantum Genetic Algorithm (QGA) using a Grover-based evolutionary framework and divide-and-conquer principles.<n>On complete graphs, the proposed method consistently achieves the true optimal MaxCut values, outperforming the Semidefinite Programming (SDP) approach.<n>On ErdHos-R'enyi random graphs, the QGA demonstrates competitive performance, achieving median solutions within 92-96% of the SDP results.
arXiv Detail & Related papers (2025-01-02T05:06:16Z) - Fast, Scalable, Warm-Start Semidefinite Programming with Spectral
Bundling and Sketching [53.91395791840179]
We present Unified Spectral Bundling with Sketching (USBS), a provably correct, fast and scalable algorithm for solving massive SDPs.
USBS provides a 500x speed-up over the state-of-the-art scalable SDP solver on an instance with over 2 billion decision variables.
arXiv Detail & Related papers (2023-12-19T02:27:22Z) - Fast Numerical Solver of Ising Optimization Problems via Pruning and Domain Selection [4.460518115427853]
We propose a fast and efficient solver for the Ising optimization problems.
Our solver can be an order of magnitude faster than the classical solver, and at least two times faster than the quantum-inspired annealers.
arXiv Detail & Related papers (2023-12-10T09:43:15Z) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - Efficient protocol for solving combinatorial graph problems on
neutral-atom quantum processors [0.0]
We propose a novel protocol for solving hard graph problems that combines variational quantum computing and machine learning.
Our numerical simulations show that the proposed protocol can reduce dramatically the number of iterations to be run on the quantum device.
arXiv Detail & Related papers (2022-07-26T16:40:14Z) - Optimization of Robot Trajectory Planning with Nature-Inspired and
Hybrid Quantum Algorithms [0.0]
We solve robot trajectory planning problems at industry-relevant scales.
Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques.
We show how the latter can be integrated into our larger pipeline, providing a quantum-ready hybrid solution to the problem.
arXiv Detail & Related papers (2022-06-08T02:38:32Z) - Quantum Optimization of Maximum Independent Set using Rydberg Atom
Arrays [39.76254807200083]
We experimentally investigate quantum algorithms for solving the Maximum Independent Set problem.
We find the problem hardness is controlled by the solution degeneracy and number of local minima.
On the hardest graphs, we observe a superlinear quantum speedup in finding exact solutions.
arXiv Detail & Related papers (2022-02-18T19:00:01Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - A Hybrid Quantum-Classical Algorithm for Robust Fitting [47.42391857319388]
We propose a hybrid quantum-classical algorithm for robust fitting.
Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs.
We present results obtained using an actual quantum computer.
arXiv Detail & Related papers (2022-01-25T05:59:24Z) - Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity.
We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree.
These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
arXiv Detail & Related papers (2022-01-06T21:02:30Z) - Combining Deep Learning and Optimization for Security-Constrained
Optimal Power Flow [94.24763814458686]
Security-constrained optimal power flow (SCOPF) is fundamental in power systems.
Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs.
This paper proposes a novel approach that combines deep learning and robust optimization techniques.
arXiv Detail & Related papers (2020-07-14T12:38:21Z) - Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field.
We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem.
We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
arXiv Detail & Related papers (2020-06-17T18:14:30Z) - Quantum Approximate Optimization of Non-Planar Graph Problems on a
Planar Superconducting Processor [29.928684308464796]
We demonstrate the application of the Google Sycamore superconducting qubit quantum processor to optimization problems with the quantum approximate optimization algorithm (QAOA)
For the first time, we observe, for the first time, that performance increases with circuit depth.
This behavior highlights the challenge of using near-term quantum computers to optimize problems on graphs differing from hardware connectivity.
arXiv Detail & Related papers (2020-04-08T18:44:34Z)
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.