CRISP-G as a Symbolic “Mood Alphabet” for Quantum Annealers, A Zaban-Inspired Diagnostic Layer for D-Wave and Other QPUs
- mansour ansari
- Dec 7, 2025
- 10 min read
When I was a kid, one of my favorite toys was a little tin pop-pop boat. It still is!. You lit a candle, the tiny boiler clicked and pulsed, and this cheap toy scooted across a bowl of water like magic. Decades later, I caught myself wondering: What if you scaled that engine up to a size you could steer? Of course, the answer is you can’t just “scale it up” and expect the same behavior—geometry, heat transfer, and fluid dynamics all gang up on you. The toy works because it lives at a particular scale.
That innocent question about a pop-pop boat led to a deeper realization: the same is true in quantum computing. On paper, Hilbert space scales beautifully—add qubits and the state space explodes. But in the lab, scaling isn’t just math; it’s noise, drift, calibration, wiring, and a very fragile machine trying to stay coherent in a loud universe. You can’t just “make it bigger” and expect the same behavior. The system has moods.
For the last few years, I’ve been living in chemical space, building QuantumCURE Pro™ and inventing CRISP-G, meaning Clustered Randomness-Indexed Symbolic Pathway Glyphs, as a way to compress the chaos of docking runs into compact symbolic fingerprints. Each glyph tells a little story about entropy, pathways, and outcomes. It started in drug discovery, but at some point, another question hit me over morning coffee in my garage.
What if I let a quantum annealer write its own glyphs?What if a D-Wave run could leave behind a “mood symbol” that captures how it was really behaving under the hood?
That is where this whitepaper comes from.
I’m proposing CRISP-G as a “mood alphabet” for quantum hardware: a way to turn raw QPU logs, schedules, gauges, energies, chain breaks, and drift into structured glyphs that cluster into recognizable families. Some glyph families might correspond to healthy, high-performance regimes. Others might mark noisy, unstable, or drifting conditions. Over time, you don’t just have numbers; you have a symbolic language of how the machine feels.
Why does this matter? Because the hard part of quantum computing now is not inventing one more clever algorithm on an idealized device, it’s understanding and managing real machines at scale. If we can read and categorize the “moods” of a QPU, we may be able to:
Detect drift earlier
Route jobs to better regimes
Choose schedules that fit the machine’s current state
And ultimately support more qubits and longer effective coherence in practice, not just in theory
I’m just one retired engineer in Oklahoma with a garage gym, a QRNG, and too much curiosity—but I believe this symbolic layer can help bridge the gap between beautiful Hilbert-space math and the messy reality of hardware. This document is my first attempt to sketch that bridge: how it started with a pop-pop boat, how it connects to CRISP-G and Zaban (my quantum linguistic framework), and where it might lead as we listen more carefully to what our quantum machines are trying to tell us.
CRISP-G as a Symbolic “Mood Alphabet” for Quantum Annealers
A Zaban-Inspired Diagnostic Layer for D-Wave and Other QPUs
Abstract
Modern quantum annealers produce enormous volumes of low-level data: problem embeddings, anneal schedules, gauge transformations, sample distributions, chain break statistics, and device calibration logs. While these data are sufficient for numerical benchmarking, they are poorly suited for human intuition and fast, pattern-based diagnostics. As quantum systems scale in qubit count and complexity, there is a growing need for compact, interpretable representations that summarize how a quantum processing unit (QPU) is “behaving” under different conditions.
This white paper proposes CRISP-G (Clustered Randomness-Indexed Symbolic Pathway Glyphs) as a symbolic telemetry layer for quantum annealers such as D-Wave. Each QPU run (or batch of runs) is converted into a structured glyph that encodes: (1) problem class and embedding, (2) entropy and schedule parameters, (3) device context and drift indicators, and (4) outcome metrics such as solution quality and chain breaks. By aggregating and clustering these glyphs over time, we obtain a “mood alphabet” of the QPU: recurring glyph families that correlate with stable performance, noise-dominated regimes, or drift events.
We outline a concrete methodology for:
Designing a CRISP-G schema specializing in QPU logs.
Running controlled D-Wave experiments across problem classes, schedules, and entropy conditions.
Generating glyphs from raw run data via a feature-extraction and symbolic-mapping pipeline.
Clustering and analyzing glyph populations to identify device “moods,” early-warning signatures, and schedule-dependent phases.
The long-term vision is that such a symbolic layer can support error handling, adaptive scheduling, and hardware characterization, ultimately contributing to more stable scaling in qubit count and effective coherence time.
1. Motivation
Quantum annealers are already used for a variety of combinatorial optimization problems, including QUBO-based formulations in chemistry, finance, logistics, and machine learning. As these devices grow in size and complexity, developers face several challenges:
Data overload: Thousands of runs, each with rich logs, are difficult to interpret beyond aggregate statistics.
Drift and mood: Device behavior changes with time, temperature, calibration, and usage patterns, often in subtle ways that are hard to detect early.
Non-trivial regimes: Different problem embeddings and anneal schedules can push the same hardware into qualitatively different operating regimes, even if high-level metrics (e.g., mean energy) look similar.
Traditional metrics (success probability, average energy, chain break percentage) are essential but do not provide a compact, visual, or symbolic fingerprint of a run’s full context and behavior.
In parallel, the Zaban framework and CRISP-G were developed as symbolic compression layers for high-dimensional, entropy-driven processes in molecular docking and chemical space exploration. These glyphs capture patterns of randomness, pathway structure, and outcome quality in a form that is both machine-readable and human interpretable.
This suggests a natural extension:
Apply CRISP-G to QPU run logs to build a symbolic “mood alphabet” for quantum hardware—revealing under-the-hood patterns of behavior that are not obvious from raw numbers alone.
2. CRISP-G Overview
CRISP-G stands for Clustered Randomness-Indexed Symbolic Pathway Glyph. It is a compact string (e.g., 16–24 characters) where each position and/or symbol is derived from a structured set of features. In its original context (drug discovery), CRISP-G encodes:
Entropy source and strength
Pathway/collapse behavior
Binding quality and toxicity screens
Novelty and clustering information
For QPUs, we reinterpret CRISP-G as a run-level fingerprint built from:
Problem & embedding context
Problem family (e.g., MaxCut, random QUBO, application-specific QUBO)
Graph size and structure
Embedding complexity (chain length statistics, embedding success rate)
Entropy & schedule parameters
Anneal schedule type (linear, paused, reverse, custom shape)
Total anneal time
Number and location of pauses/quenches
Gauge transformations / spin-reversal seeds
Device context & drift indicators
Time since last calibration
Temperature or proxy metrics
Historical drift indicators (if available)
Basic hardware mode (standard vs. advanced features)
Outcome metrics
Best and average energy vs. known optimum or classical baseline
Success probability (fraction of samples in top-K energy)
Chain break rate and repair statistics
Sample diversity and variance measures
The mapping pipeline can be conceptualized as:
Where:
3. System Architecture
The proposed system has five main components:
QPU Experiment Harness
Interface to D-Wave (or other annealer) via SDK.
Submits batches of QUBO problems under controlled parameter sweeps.
Captures all relevant log data per run.
Feature Extraction Layer
Converts raw logs into structured feature vectors.
Handles problem metadata, schedule descriptions, device context, and outcome metrics.
CRISP-G Mapper
Normalizes features (e.g., z-score, min–max, quantiles).
Maps ranges of values and categorical codes to discrete symbols.
Assembles a fixed-length glyph string per run.
Glyph Database
Stores (glyph, feature vector, raw metrics, timestamp, device ID).
Supports querying by time, device, problem class, and glyph pattern.
Analysis & Visualization Tools
Clustering and dimensionality reduction (e.g., k-means, DBSCAN, t-SNE/UMAP on feature vectors or glyph embeddings).
Time-series views of glyph populations (“mood over time”).
Correlation analysis between glyph clusters and performance/error metrics.
This architecture is deliberately modular: the CRISP-G layer is non-invasive. It sits on top of existing QPU infrastructure and can be adopted incrementally without altering low-level hardware or control code.
4. Experimental Methodology
4.1 Experimental Goals
The initial experimental campaign has three primary goals:
Establish a stable CRISP-G schema for QPU runs.
Demonstrate that glyph clusters correlate with meaningful device behaviors, such as high vs. low performance, or pre-drift vs. post-drift regimes.
Explore the possibility of early-warning signatures: glyph patterns that predict upcoming degradation in performance before simple metrics would trigger alarms.
4.2 Problem Classes
Choose a small number of representative QUBO families:
Synthetic benchmarks
Random dense/sparse QUBOs.
Planted-solution problems where the optimum is known.
Structured graphs
MaxCut on random regular graphs.
Chimera/Pegasus-native subgraphs to reduce embedding complexity.
Application-inspired problems
Simple portfolio optimization QUBOs.
Small instances of domain-relevant problems (e.g., toy versions of chemical or logistic formulations).
Each problem family is tagged in the metadata and encoded into specific CRISP-G positions.
4.3 Parameter Sweeps
For each problem instance, perform controlled sweeps over:
Anneal schedule
Linear vs. various non-linear schedules.
Different total anneal times (short, medium, long).
Optional pause-and-quench patterns.
Gauge / spin-reversal seeds
Multiple gauges per problem/schedule combination.
Repetitions
Sufficient repetitions per configuration to estimate success probability, energy distributions, and chain break behavior.
Optionally, repeat experiments across multiple days or before/after a calibration event to capture drift.
4.4 Data Collection Per Run
For each run or batch, collect:
Problem ID and family
Embedding statistics (chain length histograms, failed chains)
Anneal schedule details
Gauge index
Timestamp and device ID
Best energy and its frequency
Fraction of samples within top-K energies
Chain break rate and repair outcomes
Simple diversity metrics (e.g., number of unique solutions, Hamming distance stats)
These form the raw input for the feature extraction layer.
5. CRISP-G Generation Pipeline
5.1 Feature Normalization and Bucketing
For each numerical feature (f):
Compute global or per-experiment statistics (mean, std, quantiles).
Map values into discrete buckets, e.g.:
0–20th percentile → “A”
20–40th percentile → “B”
40–60th percentile → “C”
60–80th percentile → “D”
80–100th percentile → “E”
For categorical features (problem family, schedule type, device ID), assign specific symbol sets or 1–2 dedicated glyph positions.
5.2 Positional Semantics
Define a fixed-length glyph (e.g., 24 characters) with semantic regions, for example:
Positions 1–4: Problem & embedding
Problem family
Graph size bucket
Embedding difficulty (mean/max chain length bucket)
Embedding success/repair bucket
Positions 5–10: Entropy & schedule
Schedule type
Total anneal time bucket
Number/locations of pauses (coarse-coded)
Gauge index bucket
Repetition index or batch signature
Positions 11–18: Outcome quality
Energy gap vs. known optimum or baseline
Success probability bucket
Chain break rate bucket
Sample diversity bucket
Stability across gauges (if aggregated)
Positions 19–24: Device mood & drift indicators
Time since last calibration bucket
Long-term drift proxy (e.g., moving average of energy gaps)
Anomaly score bucket (distance from historical norms)
Optional “alert” or “calm/noisy” symbol based on combined metrics
Each position uses a small alphabet (e.g., 4–8 symbols) so that the total glyph remains compact but expressive.
5.3 Glyph Construction
Given a feature vector and bucket mapping:
For each position, pick the appropriate symbol based on the relevant feature bucket.
Concatenate all symbols into a string:
3.
This yields a per-run or per-batch CRISP-G that can be:
Displayed in UIs.
Used as input features for ML models (e.g., via one-hot encoding or embedding).
Clustered directly via string-based or embedding methods.
6. Analysis Methods
6.1 Glyph Clustering
Treat either:
The underlying feature vectors (x), or
Encoded glyphs (e.g., mapped to a vector via one-hot / learned embedding)
and apply clustering algorithms such as:
k-means or Gaussian Mixture Models for roughly spherical clusters.
DBSCAN or HDBSCAN for density-based clusters, which can reveal irregular “mood islands.”
Each cluster corresponds to a glyph family, i.e., a typical QPU “mood.”
6.2 Correlation with Performance
For each glyph cluster:
Compute average and variance of:
best energy vs. optimum or baseline,
success probability,
chain break rate,
diversity metrics.
This reveals which glyph families correspond to:
High-performance, stable regimes (favorable mood).
Noisy, unstable regimes (unfavorable mood).
Transitional regimes where small parameter changes cause large behavioral shifts.
6.3 Temporal Dynamics and Drift
Plot glyph clusters over time:
For each run timestamp, color-code the glyph by cluster and show a time series.
Overlay known events: calibrations, temperature changes, firmware updates.
This allows:
Detection of drift: gradual transitions from one dominant glyph family to another.
Identification of pre-failure signatures: glyph patterns that systematically appear before a noticeable performance drop.
6.4 Early-Warning and Anomaly Detection
Compute an anomaly score for each new glyph, for example:
Distance from nearest historical cluster center.
Likelihood under a learned probability model over glyphs.
If the anomaly score exceeds a threshold, flag:
“Unusual QPU mood — consider recalibration or closer inspection.”
Over time, these anomaly glyphs may form their own clusters, corresponding to new or rare hardware regimes.
7. Applications and Impact
7.1 QPU Diagnostics and DevOps
CRISP-G can serve as a visual and symbolic diagnostic layer:
Engineers can quickly see whether today’s runs “look like” good historical glyph families or drift into unfamiliar patterns.
Operational dashboards can show glyph distributions per device, per day, giving a high-level “health overview.”
7.2 Adaptive Scheduling and Error Handling
Schedulers can incorporate glyph patterns into decision-making:
If current glyphs match high-performance families, allow aggressive schedules and tighter error tolerance.
If glyphs drift into unstable families, automatically:
lengthen anneal times,
adjust schedules to more robust shapes,
or trigger recalibration routines.
This creates a feedback loop where the QPU’s symbolic mood influences how we drive it, potentially stabilizing performance and extending effective coherence.
7.3 Hardware Characterization and Research
For researchers and hardware designers:
CRISP-G offers a compact way to label and compare experiments across devices, firmware versions, and cooling conditions.
Glyph families can be correlated with internal hardware models, offering hints about underlying noise mechanisms and design improvements.
8. Roadmap and Future Work
The proposed project can be phased as follows:
Phase 1 – Prototype (2026 Q1–Q2)
Implement the experiment harness for a single QPU (e.g., D-Wave).
Define an initial CRISP-G schema and pipeline.
Run a limited set of benchmark problems and generate glyphs.
Build a basic analysis notebook to cluster and visualize glyphs.
Phase 2 – Extended Campaign (2026 Q2–Q4)
Expand problem classes and parameter sweeps.
Integrate time-series analysis and drift detection.
Refine glyph mapping for better interpretability and stability.
Draft a technical report or first paper: “CRISP-G: Symbolic Mood Glyphs for Quantum Annealers.”
Phase 3 – Integration & Collaboration
Explore real-time integration with D-Wave job scheduling and monitoring tools.
Share results and methodology with hardware and software teams.
Investigate extending CRISP-G to gate-model devices (e.g., IonQ) and to QPU calibration routines (e.g., mapping calibration traces to glyphs).
Phase 4 – Theoretical Exploration
Analyze whether certain glyph motifs correspond to deeper invariants or “phases” of the QPU’s operational Hilbert space.
Connect CRISP-G patterns with more formal concepts (e.g., effective temperature, energy landscape structure, or noise channel characteristics).
9. Conclusion
This white paper proposes a new, symbolic layer for understanding and managing quantum annealers: CRISP-G glyphs as a QPU mood alphabet. Instead of treating QPU runs as isolated numeric logs, we compress their context, schedule, device state, and outcomes into structured glyphs that can be clustered, visualized, and analyzed over time.
The central hypothesis is that:
Stable, interpretable glyph families will correlate with meaningful hardware and algorithmic regimes,offering a practical path toward better diagnostics, error handling, and ultimately more scalable and robust quantum computation.
Even if CRISP-G begins as a purely pragmatic telemetry tool, its patterns may reveal deeper structure in how real quantum devices move through their high-dimensional state spaces—turning noisy, opaque behavior into a quasi-linguistic system we can study, predict, and eventually converse with.
Mansour Ansari, QuantumLaso, LLC, December 2025, Oklahoma City
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