{"id":"oxtr-qrc-01","section":"summary","availableSections":["summary","sequences","methods","measurements","comparison","next_test","timing","downloads","continuation"],"data":{"question":"Do quantum responses generated from Fibonacci and M9 P24 representations improve our ability to predict transcription factor occupancy around OXTR?","phase":"Completed measurement qualification plus an exploratory classical prediction continuation. Quantum binding prediction has not yet been tested.","goal":"Test whether quantum responses add useful information for predicting transcription-factor occupancy around OXTR.","what_ran":"Two unchanged 256-base DNA samples. Each sample supplied numerical input in eight preparations: ordinary, Fibonacci, Phi Ratio, Alpha, Beta, Gamma, Epsilon and shuffled. Six simulated or physical atoms; 16 CUDA-Q GPU programs and 16 Aquila programs, each with 100 measurements.","result":"All 16 GPU runs passed the recorded numerical agreement check. All 16 Aquila runs returned; agreement with the model was mixed. These are measurements of atomic excitation, not probabilities that proteins bind DNA.","useful_finding":"The input preparation changes the quantum response. Gamma and Epsilon raise the calculated mean excitation relative to ordinary input in both samples. The shuffled control is larger in the second sample, so response size alone is not a Fibonacci-specific benefit.","success_next":"Build a binding-prediction method using one set of DNA regions. Keep that method fixed and predict different DNA before revealing its laboratory binding results. A useful signal is more accurate predictions than the strongest comparison method using the same DNA information.","null_next":"No clear improvement within uncertainty is inconclusive. Equal or worse predictions give no demonstrated predictive benefit and require revision or a negative result.","next_action":"Astra has now completed public DNA preparation and a classical prediction pilot, and prepared a matched pair with 16 local CUDA-Q checks. Read continuation for actual findings. No new Aquila or qBraid jobs were submitted.","ctcf":"CTCF is one transcription factor: a protein that binds DNA. It is a proposed first test target, not a replacement term for all transcription factors. ENCODE peaks mark binding regions, not exact contacted bases.","trust":"The mathematical calculation tests the specified model. CUDA-Q samples that model on a classical GPU. Aquila measures actual neutral atoms, including device noise. Agreement checks execution; laboratory binding data is the evidence for biological prediction. There is no universal trust score ranking one machine above the other.","nature":"The 2022 Nature study s41586-022-04853-4 used a Fibonacci-ordered drive in a trapped-ion system and found longer-lived edge quantum information. It motivates Fibonacci research. Our Aquila measurement batch did not test that stability mechanism or prove that hexagons cause it.","hypotheses":"The proposed link from M9 P24 geometry, Odom’s construction and honeycomb efficiency to quantum stability remains a working hypothesis. Mathematical relationships and the honeycomb theorem do not by themselves establish this physical mechanism.","hardware_fee_usd":20.8,"gpu_cost_credits":0,"hardware_valid_shots":1541,"links":{"page":"https://universalmind.tv/informatics/?area=quantum&entry=research","json":"https://universalmind.tv/informatics/quantum/experiments/oxtr-qrc-01-qualification/r1/experiment.json","csv":"https://universalmind.tv/informatics/quantum/experiments/oxtr-qrc-01-qualification/r1/experiment.csv","complete_dataset":"https://universalmind.tv/informatics/connect.html#downloads"},"continuation":{"verdict":"Inconclusive classical pilot","difference_average_precision":0.0019879267780844173,"details":"continuation"}},"sourceSHA256":"adc1914cac349c28afc5f4d8c72dbddf80f38063ab1bef3e93e21cea8d415876","url":"https://universalmind.tv/informatics/quantum/experiments/oxtr-agent-follow-through-pilot/r1/experiment.json","pageURL":"https://universalmind.tv/informatics/?area=quantum&entry=research"}