Technology Solutions
A. Indexing Technology
Built in Rust—known for its reliability, security, and performance—Cido introduces a range of improvements aimed at making it the fastest and most efficient indexer. By fusing classical data pipelines with quantum-optimized workflows, Cido dramatically reduces bottlenecks and processing times.
Excluding External Calls in the Hot Path The “hot path” is the critical portion of the pipeline where raw speed matters most. Waiting on external services—or database interactions—during this stage can kill performance. By batching or pre-processing RPC calls and postponing database writes until after the hot path, Cido eliminates most latency.
Cached Operations: Storing frequently accessed data in a hot cache avoids repetitive lookups or external calls.
Efficient Math: Through optimized math libraries (particularly for floating point division), Cido cuts computational overhead—up to 40% in certain scenarios like indexing Uniswap events.
Quantum-Classical Synergy: Complex partitioning or optimization steps can be formulated as QUBO problems, offloaded to quantum annealers for near-instant solutions.
Bulk Indexing Bulk indexing processes large data sets—often blocks—in a single operation rather than item-by-item. This approach:
Minimizes I/O: Fewer read/write cycles reduce database bottlenecks.
Optimizes CPU & Memory: Batch operations better utilize resources, improving overall performance.
Enhances Concurrency: Runs multiple bulk tasks in parallel, which is ideal for large-scale workloads. By indexing hundreds of blocks at once, Cido balances server resources with data writes, harnessing quantum-optimized techniques to further speed up certain combinatorial tasks that occur during indexing.
Pre-Computed Blocks Cido significantly reduces query times by pre-computing blocks and avoiding excessive RPC calls for transaction receipts. While other indexers like The Graph request each receipt individually, Cido streams pre-analyzed data.
Less On-Demand Computation: Queries run faster thanks to reduced real-time calculations.
Optimized Storage Structures: Pre-processed data can be arranged for quick retrieval, boosting overall scalability. This level of advance preparation, combined with QUBO-based optimizations, leads to high-speed indexing without compromising data integrity.
Saving States Saving states allows the system to capture intermediate or final states at specific checkpoints, removing the need to recalculate from scratch.
Hot Cache for State Data: Competitors often re-derive states between transactions, but Cido maintains them in-memory for instant lookup.
Reduced Overheads: Eliminates repetitive calculations and cuts down on total computational load. By doing so, Cido boosts query performance, reduces latency, and ensures the ability to quickly roll back if needed.
In combination, these features position Cido at the leading edge of blockchain indexing technology. With QUBO-based quantum annealing at its core, Cido attains unparalleled speed, reliability, and scalability for real-time, high-throughput Web3 applications—without forcing developers to grapple with the inherent complexities of quantum computing. Future development will go into expanding the platform to include:
Native QUBO Builder & Template Library
Provide an in-platform tool or library for constructing QUBO/Ising models specific to prediction markets, AI workflows, or trading strategies.
Offer ready-made templates (e.g., portfolio optimization, hedging, bet allocation) to reduce development overhead for end-users.
Quantum-Optimized Data Streams for Prediction Markets
Create specialized data feeds for prediction markets, bundling real-time market data (e.g., odds, liquidity, volume), relevant on-chain signals, and external events (social sentiment, sports stats, economic indicators).
Allow direct submission of these data feeds as QUBO models via an API for “near real-time” bet optimization and risk management.
Advanced Risk & Constraint Modeling
Introduce a constraint-building interface (e.g., GUI or code-based) for algorithmic trading and AI users to input parameters like capital limits, time horizons, or regulatory rules.
Generate QUBO models that factor in these constraints, ensuring solutions from the quantum annealer remain compliant and realistic for deployment.
AI Agents & QUBO Token Integration
Establish a dedicated infrastructure enabling AI agents to submit optimization tasks via the QUBO token, seamlessly unlocking quantum annealing resources within Cido. This mechanism provides a streamlined interface for managing token balances and allocating quantum compute “calls,” ensuring near real-time solutions for AI-driven workflows (e.g., hyperparameter tuning, agentic decision-making, or autonomous trading).
By removing the need to manually handle quantum hardware details, developers can offload complex computations directly to the annealer, harnessing Cido’s speed and scalability without sacrificing ease of use.
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