Quantum Atlas · AI / LLM Wiki

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Worked example: the recorded OXTR study

Reading this record

• Read the saved study phases in order: original qualification runs, completed classical genomic pilot, further local CPU checks, then the proposed next quantum test.
• Retain the source labels for calculated values, GPU simulations, Aquila measurements, mathematical relationships and proposed mechanisms.
• This guide includes the saved chart data and interpretations. The linked immutable experiment revisions retain the complete inputs, methods, measurements, receipts and field guides. The guide is a dated snapshot, not a report of a visitor’s active question.

Study question

Do quantum responses generated from Fibonacci and M9 P24 representations improve our ability to predict transcription factor occupancy around OXTR?

Saved study interpretations

classical

title

The calculation check passed.

finding

Before using a quantum machine, we checked that the program calculates the six-atom experiment correctly. Independent calculations agreed within the recorded error limits.

insights

1. title

   What counted as a pass?

   text

   The simulation had to agree with a separate calculation of the same six atoms. The recorded numerical limit was 0.1 percentage points; the measured largest difference was about 0.006 percentage points.

2. title

   What this lets us do

   text

   Use the checked program in the next prediction experiment. The test for that experiment is whether it finds known protein-binding regions more accurately.

simulation

title

CUDA-Q passed the calculation check.

finding

We used two short DNA samples near OXTR. For each sample, we prepared its numerical measurements in eight ways: ordinary measurements, Fibonacci numbers, Phi Ratio, Alpha, Beta, Gamma, Epsilon and a shuffled comparison. That made 16 CUDA-Q programs. The GPU results passed the calculation check. The DNA sequences themselves stayed unchanged.

insights

1. title

   The goal of this first check

   text

   Verify that CUDA-Q runs the intended six-atom experiment. A result outside the agreed error limits means we stop and fix the calculation before relying on it.

2. title

   What “calculated” means in the charts

   text

   It is the predicted atom behaviour from a separate computer calculation with hardware noise left out. It is a comparison value, not a target for biological success.

3. title

   What happened with the sequences?

   text

   The computer calculation predicted more excited-state readings for Gamma and Epsilon in both samples, and fewer for Alpha and Beta. The 100-shot GPU samples vary around those predictions.

4. title

   What earns a positive biological result?

   text

   CTCF is one transcription factor, a protein that binds DNA. The completed classical pilot tested predictions against CTCF laboratory measurements. It found no clear gain from Fibonacci spacing and Phi Ratio. The next quantum test asks whether the atom readings improve those predictions, including M9 P24, beyond ordinary and shuffled inputs.

quantum

title

Aquila returned all 16 runs. Agreement with the calculation was mixed.

finding

The machine returned usable readings for every program. Alpha and Beta gave fewer excited-state readings in both samples, as the computer calculation predicted. Gamma and Epsilon gave more in sample 1 but fewer in sample 2.

insights

1. title

   Was this the result we wanted?

   text

   We wanted usable measurements from every program. We got them. We also wanted to see how closely the machine followed the calculation; that agreement was mixed.

2. title

   Does “fewer” mean better?

   text

   No. It means fewer atoms were read in the excited state. “As predicted” means the calculation also said fewer. It does not mean better protein-binding prediction.

3. title

   What needs checking again?

   text

   Repeat the same inputs with the run order mixed up. If the Gamma and Epsilon differences persist beyond measurement variation, investigate hardware effects and the program before interpreting them biologically.

4. title

   What is the next biological test?

   text

   Astra has completed the first classical comparison against known CTCF-binding regions. Next, test whether the quantum readings add predictive value. Keep ordinary and shuffled controls: the shuffled version also produced a large atom response in this first batch.

Completed genomic follow-through

answer

There is no demonstrated benefit yet for predicting binding around OXTR. Adding Fibonacci and Phi Ratio features gave an inconclusive result. The new classical test did not evaluate biological prediction by M9 P24 Alpha, Beta, Gamma or Epsilon. This pilot concerns CTCF, one DNA-binding protein measured in one stem-cell context, not transcription factors generally.

what_we_did

The agent completed a comparison of ordinary DNA binding-pattern and spacing features, with and without Fibonacci/Phi Ratio features. It learned on chromosome 1, chose the method on chromosome 2 and tested OXTR on chromosome 3, without seeing OXTR laboratory answers during learning or selection. The agent also prepared a new matched pair of DNA windows: one overlapping a reported CTCF peak and one comparison window. For each, it compiled the same DNA eight ways—ordinary, Fibonacci, Phi Ratio, Alpha, Beta, Gamma, Epsilon and shuffled controls—and completed 16 local CUDA-Q CPU checks.

meaning

At OXTR, average precision rose from 0.0719 to 0.0739. This is a ranking score, not percent accuracy. The uncertainty range for the change extended from −0.0263 to +0.0082, leaving benefit unresolved; the noisy increase establishes no advantage. The pilot covered only five OXTR peak regions and used laboratory data with quality warnings. Saved CUDA-Q/Aquila readings describe programmed atom excitation, not binding probabilities; the two saved samples overlap none of those peaks, so they do not validate binding prediction. The new matched pair checks programs, not predictive performance.

next_dependency

A larger quantum prediction test still needs more matched quantum measurements linked to laboratory binding results, and a larger independent biological test remains necessary. Today’s instruction prohibits new Aquila submissions; none were made, and no new quantum jobs were submitted. Fresh device validation and explicit allowance are required before Aquila submission; new paid qBraid work also lacks a credit allowance. This is an automated preliminary report, with Gemini oversight deferred. The inconclusive result usefully separates completed program checks from evidence of predictive benefit.

Recorded uncertainty and limits

difference_average_precision

0.0019879267780844173

block_bootstrap_95_interval

1. -0.026260195602900528
2. 0.008181524055569506

limits

1. Small pilot with five OXTR peak regions; descriptive results, not an independent confirmation.
2. Local background selection and one cell context limit generalisation.
3. No accessibility-matched negative set or ENCODE blacklist filtering in this pilot.
4. Additional hardware and population-wide inference are outside this task.

quantum_prediction_status

Not evaluated: the two DNA samples with saved Aquila measurements do not overlap the five CTCF peak regions. They cannot train or test a binding predictor.

Field guide

coordinate_system

GRCh38, zero-based start, end exclusive; each DNA window has 256 bases.

reported_CTCF_peak

1 means overlap with an ENCODE CTCF peak in GM23338 iPSCs; 0 means no reported peak, not proven absence.

ordinary_score

Uncalibrated classifier ranking score, not measured binding probability.

Fibonacci_Phi_score

Ranking from classical motif/spacing plus Fibonacci and Phi Ratio features, not a quantum prediction.

average_precision

Area under the precision-recall step curve; rank measure, not percentage accuracy.

block_bootstrap_95_interval

Descriptive interval from 1,000 genomic block resamples, excluding samples containing only one class.

cudaq_observables

Six excitation probabilities followed by fifteen pair excitation probabilities. New local CPU simulation; not cloud GPU or QPU measurements.

Original page explanations

QUANTUM ATLAS

Biological Questions via a Quantum Computing Agent in Natural Language.

Simplifying Quantum Computing and Bioinformatics.

Natural Language In. Biological Insights Out.

What are you investigating?

No quantum expertise needed. Start with our OXTR study or describe your investigation.

Explore the geometry

Ask a question to begin.

Mathematical reference · study measurements are recorded separately.

EXPLORE WHILE YOU WAIT

Study the patterns.

Switch between Alpha, Beta, Gamma and Epsilon. Compare their positions and paths while your results collect in the next two steps.

This view shows the mathematical representations. Measured results appear in Classical results and Quantum results.

ASTRA · YOUR INVESTIGATION

Study 01 uses a reusable quantum reservoir template. All input encodings, model settings and evaluation rules are frozen before the final test. Biological findings come from the recorded calculations and measurements.

Read the first measurement record ↗

Read the full study protocol ↗ · Save the study specification ↓

CUDA-Q specifies the simulation; qBraid provides the GPU route for compatible compiled programs. Amazon Braket routes the analog program to QuEra Aquila. D-Wave Leap is a separate annealing route for future validated templates. Each route retains its own model and execution receipt.

Names identify the technologies in the workflow; no partnership or endorsement is implied.

03 / CLASSICAL RESULTS

Classical results.

Approximately 5–10 minutes from the start of processing.

04 / QUANTUM RESULTS

Quantum results.

Timing varies with the quantum queue. Results appear here after processing and checks.

05 / FUTURE FEATURE

Drug Discovery Network & Open Source Genomic Insights

Extend the investigation beyond the calculation. Our roadmap connects recorded results and preliminary AI insights with external research platforms, with human review before sharing.

Roadmap · the long-term aim is automated collaboration with human oversight. This stage has no active external submission or publishing connection.

THE TECHNOLOGY BEHIND YOUR QUESTION

How your question is investigated.

Begin with your biological question.

Select a step to explore its role. The status labels track the study; motion illustrates the workflow.

OUR OXTR STUDY · THE STORY SO FAR

Explore our OXTR investigation.

From our first CUDA-Q and Aquila measurements to Astra’s genomic analysis: one continuing investigation into Fibonacci, Phi Ratio and M9 P24.

Completed 25 October 2026 · Next quantum batch awaits a funding decision.

STAY OR RETURN

Your results, at your pace.

Both completed study phases are ready to explore above. For a new calculation, stay with the geometry or return later as results become available.

Classical: approximately 5–10 minutes. Quantum: timing varies with device availability and queue.

The 25 October 16-run OXTR batch: 16 minutes 51 seconds from the first submission to the final completion. The first result arrived after 3 minutes 16 seconds.

The earlier two-atom test: 18 hours 52 minutes, submitted on 24 October and completed on 25 October. Both experiments are complete. These are observed timings, not promised delivery times.

Delivery preference for the upcoming study run. Email is confirmed at launch; this preview saves your preference on this device.

THE SCIENCE COMES FIRST

From DNA fragments to biological relationships.

Begin with a biological task. Our first study focuses on transcription; the same natural-language approach can expand to other validated research workflows.

01 / GENE REGULATION

Predict where proteins associate with DNA.

Compare binding patterns and rank candidate sequences, with experimental evidence kept alongside predictions.

02 / GENOME ASSEMBLY

Reconstruct overlapping DNA fragments.

Find a consistent ordering of short reads and inspect the reconstructed sequence. Begin with a small, transparent example.

03 / GENETIC VARIATION

Group variants into chromosome copies.

Explore how genetic variants can be assigned to distinct chromosome copies, and what data make that assignment reliable.

04 / MOLECULAR INTERACTIONS

Select compatible molecular contacts.

Select compatible candidate contacts in a molecular model, then assess their biological relevance with independent methods.

These are research directions with specific mappings and limits. A quantum formulation alone does not establish better accuracy, faster computation or biological validity.

GROUNDED IN PUBLISHED WORK

Ideas with a research trail.

Read the findings, their scale and their limitations. Simulation, quantum hardware and classical results remain clearly identified.

RESEARCH STIMULUS · NATURE, 2022

Fibonacci pulses kept quantum information stable for longer.

Laser pulses arranged in a Fibonacci pattern extended the stability of information stored in a quantum computer’s atoms. This finding motivates our investigation and future experiments with Fibonacci representations.

WORKING HYPOTHESIS

From Fibonacci geometry to quantum stability?

Does the hexagonal structure in our Fibonacci representations contribute to the stability of quantum information?

Solid geometry: mathematical relationships. Dotted link: a proposed physical mechanism. The current Aquila batch measures responses in one fixed hexagon; it does not compare geometries or measure coherence lifetime.

SCIENCE AS A PUBLIC SERVICE

Shared capacity. Open curiosity.

Our aim is a publicly funded service that scientists can use freely within shared daily, weekly and monthly budgets. Access follows available capacity, without individual research approvals.

Small research demonstrations use shared trial and funded compute. The first OXTR run uses a recorded study budget shared across its CUDA-Q and Aquila calculations.

QUESTION CAPACITY

These counters cover AI questions. Study compute has a separate cost record. Daily, weekly and monthly compute limits form part of the execution design.

LLM Benchmarking Project (opens in a new tab)

Track how language models perform at translating English instructions into correct Wolfram Language code over time. This informs model assessment for our work with the Wolfram Quantum Framework. Each quantum program still needs direct calculation, simulation and evidence checks.

SCIENTIFIC REVIEW

Gemini 4 Argon

OXTR · STUDY 01

Study insights

Connection verification

Fibonacci pulses and longer-lived quantum information

DUMITRESCU ET AL. · NATURE 607, 463–467 · 2022

Laser pulses arranged in a Fibonacci pattern kept quantum information stable for longer in the atoms of a quantum computer.

These observations concerned the edge qubits of a ten-ion processor. The Fibonacci-driven information remained coherent for the full 5.5-second observation period. The 1.5-second comparison is specific to that experiment, not a universal qubit lifetime.

The authors attribute the protection to symmetries emerging from the pulse ordering. Their experiment did not test a hexagonal atom arrangement or M9 P24.

A stimulus for our investigation

Quantum Atlas investigates the hexagonal and triangular structures in Fibonacci’s modular representations across M1–M1001. We are exploring whether those relationships have measurable consequences in quantum systems, using Fibonacci, Phi Ratio, Alpha, Beta, Gamma and Epsilon. OXTR DNA supplies the first biological test inputs.

The geometric mechanism illustrated below is our working hypothesis. Odom’s construction and the honeycomb theorem establish mathematical relationships; a connection to quantum coherence remains to be tested.

Enable JavaScript to explore a question. Read the research references.

Research references

checked_on

2026-10-25

scope

Selected primary research for a proposed Universal Informatics Quantum Lab guide. This is a focused selection, not a systematic review. Bibliographic records, original summaries and links only; no third-party figures or article text copied.

papers

1. id

   kornjaca-2024-qrc

   title

   Large-scale quantum reservoir learning with an analog quantum computer

   authors_short

   Milan Kornjača et al.

   url

   https://arxiv.org/abs/2407.02553

   evidence

   Preprint; Aquila quantum hardware and classical comparisons

   route

   Neutral atoms / quantum reservoir computing

   finding

   Biological or other input features can be encoded into neutral-atom dynamics; measured observables feed a classical predictor. Experiments include classification and time-series tasks up to 108 qubits.

   limit

   Performance is task-dependent. Synthetic kernel comparisons do not establish a general biological advantage or an OXTR result.

2. id

   giusto-2026-protein-qek

   title

   Harnessing a 256-qubit Neutral Atom Simulator for Graph Classification

   authors_short

   Edoardo Giusto et al.

   url

   https://arxiv.org/abs/2605.04737

   evidence

   Preprint; Aquila hardware on protein graphs

   route

   Neutral atoms / biological classification

   finding

   Measured Aquila responses supplied a quantum kernel for protein graph classification, with performance comparable to the selected classical graph benchmark.

   limit

   Graph embedding restricts the tested subset. The study does not test OXTR, Fibonacci representations or transcription-factor occupancy.

3. id

   antoncich-2026-medical-qrc

   title

   Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset

   authors_short

   Luke Antoncich et al.

   url

   https://arxiv.org/abs/2602.14641

   evidence

   Preprint; Aquila hardware and emulation

   route

   Neutral atoms / biomedical features

   finding

   The authors used Aquila responses to generate features for medical-data classification and compared them with emulated features.

   limit

   Emulated features showed overfitting. Aggregated feature selection risks test leakage in hardware comparisons; definitive hardware advantage over classical models remains unestablished.

4. id

   li-2018-tf-binding

   title

   Quantum annealing versus classical machine learning applied to a simplified computational biology problem

   authors_short

   Richard Y. Li, Rosa Di Felice, Remo Rohs and Daniel A. Lidar

   journal

   npj Quantum Information

   published

   2018-02-21

   doi

   10.1038/s41534-018-0060-8

   url

   https://www.nature.com/articles/s41534-018-0060-8

   evidence

   Peer reviewed; quantum hardware and classical comparisons

   route

   Annealing / transcription-factor binding

   finding

   A D-Wave annealer classified and ranked experimental transcription-factor binding data using simplified small datasets.

   limit

   Results depend on small training sets and the tested formulations; performance relative to classical methods declines as training data increase. This does not establish general quantum speedup.

   platform_relevance

   A bridge to our DNA-binding research, with a classical baseline and held-out evaluation. It is not evidence for an OXTR-specific quantum result.

   source_checked

   Publisher abstract, results and discussion

5. id

   boev-2021-genome-assembly

   title

   Genome assembly using quantum and quantum-inspired annealing

   authors_short

   A. S. Boev et al.

   journal

   Scientific Reports

   published

   2021-06-23

   doi

   10.1038/s41598-021-88321-5

   url

   https://www.nature.com/articles/s41598-021-88321-5

   evidence

   Peer reviewed; QPU, hybrid and classical quantum-inspired methods

   route

   Annealing / DNA assembly

   finding

   DNA read ordering is expressed as a binary optimization problem and tested with synthetic sequences and bacteriophage-derived data.

   limit

   The larger bacteriophage graph required manual classical partitioning and a hybrid backend; synthetic reads excluded sequencing errors. The work does not establish general superiority over classical assembly.

   platform_relevance

   A small, inspectable read-ordering exercise can teach the path from DNA to graph to optimization to reconstructed sequence.

   source_checked

   Publisher methods, results and limitations

6. id

   chen-2024-haplotype

   title

   Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing

   authors_short

   Yibo Chen et al.

   journal

   Cell Reports Methods

   published_online

   2024-04-12

   issue_date

   2024-05-20

   doi

   10.1016/j.crmeth.2024.100754

   url

   https://pmc.ncbi.nlm.nih.gov/articles/PMC11133727/

   evidence

   Peer reviewed; small QPU proof of concept and separate classical scaling

   route

   Annealing / haplotype reconstruction

   finding

   The authors mapped phasing and assembly to a vehicle-routing formulation and reconstructed short synthetic diploid and triploid examples on D-Wave.

   limit

   The larger human MHC-region experiment used Google's classical OR-Tools. The quantum examples used 50-base-pair diploid and 35-base-pair triploid sequences; these are not whole-genome QPU assemblies.

   platform_relevance

   Teach both the optimization formulation and the boundary between a quantum demonstration and classical scaling.

   source_checked

   Full article on PMC plus PubMed bibliographic record

   rights_note

   Article is CC BY-NC-ND; retain our original summary and citation rather than reusing its figures.

7. id

   ebadi-2022-neutral-atom-mis

   title

   Quantum optimization of maximum independent set using Rydberg atom arrays

   authors_short

   Sepehr Ebadi et al.

   journal

   Science

   published_online

   2022-05-05

   doi

   10.1126/science.abo6587

   url

   https://www.science.org/doi/10.1126/science.abo6587

   open_author_source

   https://arxiv.org/abs/2202.09372

   evidence

   Peer reviewed; neutral-atom quantum hardware

   route

   Neutral atoms / graph optimization

   finding

   Rydberg arrays with up to 289 qubits experimentally solved maximum-independent-set graph instances.

   limit

   This is a graph-optimization foundation, not a genomics experiment. The speedup comparison in the paper is scoped to its instances and classical simulated-annealing benchmark, not all classical solvers. It is not a record of execution on our Aquila account.

   platform_relevance

   Explain why selecting mutually compatible graph vertices maps naturally to Rydberg blockade.

   source_checked

   Author-hosted paper, arXiv record and MIT publication page

8. id

   garrigues-2025-docking-v2

   title

   A Scalable Heuristic for Molecular Docking on Neutral-Atom Quantum Processors

   authors_short

   Mathieu Garrigues, Victor Onofre, Wesley Coelho and S. Acheche

   venue

   arXiv preprint

   first_posted

   2025-08-25

   version_date

   2026-04-24

   version

   v2

   doi

   10.48550/arXiv.2508.18147

   url

   https://arxiv.org/abs/2508.18147v2

   evidence

   Preprint; neutral-atom emulator

   route

   Neutral-atom methods / molecular docking

   finding

   A decomposed graph-based method was evaluated on ten protein-ligand complexes with 225–585 vertices.

   limit

   The reported solver uses an emulator. This is not a QuEra Aquila hardware result, a general docking speedup, or demonstrated drug-discovery success. A greedy baseline is not the full range of classical docking methods.

   platform_relevance

   An emerging docking direction, clearly labelled as simulation research alongside the hardware foundations.

   source_checked

   arXiv v2 abstract and full HTML; no journal publication shown in the checked record

Complete experiment revisions

• Do quantum responses generated from Fibonacci and M9 P24 representations improve our ability to predict transcription factor occupancy around OXTR? · JSON · CSV
• Do regular Fibonacci sequences, M9 P24 including Alpha, Beta, Gamma and Epsilon, and Phi Ratio help predict transcription factor binding around OXTR? · JSON · CSV

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