When are Quantum Algorithms Applicable for Signal Decoding in Wireless Communication?
Bibliographic record
Abstract
Multiple-input multiple-output (MIMO) technology utilizes multiple antennas at the transmitter and receiver to enhance data transmission speeds and reliability. Traditional MIMO decoding methods, however, can become increasingly complex as the number of antennas and modulation order rises. Quantum computing brings forth a new realm of information processing with significant potential. This study investigates the applicability of quantum algorithms for decoding information in MIMO wireless communication systems. Specifically, the Dürr-Høyer quantum search, based on Grover’s algorithm, and optimal quantum sorting algorithms are leveraged to reduce the query complexity with an analysis of the complexity and achievable bit error rate performance of the quantum-assisted decoders. The study considers cases of MIMO diversity, spatial modulation, and multiplexing transmission using both maximum-likelihood and fixed-complexity sphere decoders. By examining a variety of communication scenarios, this work aims to assess the applicability of quantum algorithms across different operational regimes and aids in extending the methodology to other communication systems.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.013 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".