Comparison of heuristic approaches to PCI planning for Quantum Computers
Bibliographic record
Abstract
Quantum Computing (QC) provides the possibility to develop new approaches to tackle complex problems. Real-world applications, however, cannot yet be managed directly due to the limitation of present and near-future noisy intermediate-scale quantum (NISQ) computers. Decomposition into smaller and manageable subproblems is often needed to take advantage of QC even when using hybrid (classical-quantum) solvers or solvers that already apply decomposition techniques. In this paper, heuristic decomposition algorithms to solve the Physical Cell Identifier (PCI) problem in 4G cellular networks in a way suitable for QC are presented. The PCI problem can be viewed as a map coloring problem with additional constraints and has been represented in a Quadratic Unconstrained Binary Optimization (QUBO) model, a form that, for instance, a quantum annealing machine could crunch. We propose two strategies, with variable decomposition granularity. The first one solves the problem recursively through bisection (max-cut problem), to use only one qubit to represent the status of the objects, avoiding one-hot encoding and thus minimizing the qubit requirement. The second is a multi-step approach, finally solving sets of randomized modified max-k-cut problems of customizable qubit size. We executed the algorithms on real cellular networks of one of the main Italian national telecom operators (TIM). The results show that all proposed QUBO approaches can be effectively applied to very large problems with similar or better performance of the reference classical algorithm, paving the way for the use on NISQ computers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".