Pedro Maciel Xavier, Pedro Ripper, Tiago Andrade, Joaquim Dias Garcia, Nelson Maculan, David E. Bernal
No strong AI-core implementation/artifact signals were detected from current providers.
We present QUBO.jl, an end-to-end Julia package for working with QUBO (Quadratic Unconstrained Binary Optimization) instances. This tool aims to convert a broad range of JuMP problems for straightforward application in many physics and physics-inspired solution methods whose standard optimization form is equivalent to the QUBO. These methods include quantum annealing, quantum gate-circuit optimization algorithms (Qua ...
ntum Optimization Alternating Ansatz, Variational Quantum Eigensolver), other hardware-accelerated platforms, such as Coherent Ising Machines and Simulated Bifurcation Machines, and more traditional methods such as simulated annealing. Besides working with reformulations, QUBO.jl allows its users to interface with the aforementioned hardware, sending QUBO models in various file formats and retrieving results for subsequent analysis. QUBO.jl was written as a JuMP / MathOptInterface (MOI) layer that automatically maps between the input and output frames, thus providing a smooth modeling experience.
No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.
We present QUBO.jl, an end-to-end Julia package for working with QUBO (Quadratic Unconstrained Binary Optimization) instances.
This is primarily a method paper. Reproduce it within a maintained framework baseline instead of chasing paper-specific repos.
Evidence graph: 2 refs, 1 links.
Utility signals: depth 60/100, grounding 58/100, status medium.
Compare maintenance quality, reproducibility coverage, and evidence confidence before choosing a reproduction baseline.
Strong overlap with paper title keywords · Community adoption signal (41 stars)
Risk flags
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
No verified implementation available
No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.
No additional verified repositories beyond the primary recommendation.
These repositories had low-confidence matching signals and are hidden by default.
No trustworthy direct or curated related Hugging Face artifacts were found yet.
Continue with targeted Hugging Face searches derived from the paper title and method context:
Datasets
Tip: start with models, then check datasets/spaces if you need evaluation data or demos.
Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.
6
Citations
0
References
Tasks
Quantum annealing, Simulated annealing, Computer science, Quantum computer, Binary number, Quantum, Quadratic equation, Theoretical computer science
Methods
Quadratic unconstrained binary optimization, Bayesian optimization, Algorithm, Mathematical optimization
Domains
Artificial Intelligence
Evaluation & Human Feedback Data
Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.
Open in HFEPXExplore Similar Papers
Jump to Paper2Code search queries derived from this paper's research context.
Need human evaluators for your AI research? Scale annotation with expert AI Trainers.