> For the complete documentation index, see [llms.txt](https://cido.gitbook.io/cido.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cido.gitbook.io/cido.ai/technology-solutions.md).

# 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.

1. **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.

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2. B**ulk 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.

***

3. **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.

***

4. **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:

1. **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.

2. **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.

3. **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.

4. 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.