QiPAI Store: Persistent Quantum State Storage for Quantum-Inspired AI

As quantum-inspired computation becomes more powerful and symbolic, one of the critical challenges is how to persist, organize, and query quantum state data efficiently. Enter qipai-store, the persistent storage layer for the QiPAI framework, designed specifically to handle sparse quantum state tensors, entanglement structures, and rich metadata — all while keeping things fast, compact, and queryable.

🚀 What Is `qipai-store`?

The qipai-store module provides persistent storage and retrieval capabilities for quantum states (QTensor objects) within the QiPAI framework. It aims for efficiency and is designed to handle the specific needs of storing complex amplitudes, entanglement information, and associated metadata.

🧱 Module Architecture

The store is organized into several submodules:

`format/`

qstate.bin.js: Handles encoding/decoding of the primary state binary format.
qindex.js: Manages index structures for container files.
qmeta.js: Encodes/decodes state metadata (using JSON initially).

`engine/`

reader.js: Low-level binary reader (potentially stream-based).
writer.js: Low-level binary writer.
compressor.js: Optional compression algorithms (RLE, Gzip, etc.).

`fs/`

flatfile.js: One state per file strategy.
container.js: Multiple states within a single indexed file.
dirmapper.js: Organizes flat files into directories based on IDs/metadata.

`query-engine/`

queryBuilder.js: Defines the chainable Functional Query API.

`index.js`

The main public API entry point for the module.

📦 Binary Format: `qstate.bin`

The core storage format is designed to be compact and efficient:

HEADER (fixed size)

Magic number (4 bytes)
Version (1 byte)
Num Qubits (2 bytes)
Sparse Count (4 bytes)
Entanglement Group Count (2 bytes)
Metadata Length (2 bytes)

BODY

Sparse amplitudes: [index (uint16), real (float32), imag (float32)] × Sparse Count
Entanglement groups: [[q1_idx, q2_idx, …], [qA_idx, qB_idx, …], …] (Encoded efficiently)
Metadata: UTF-8 JSON blob (or potentially MsgPack)

This format prioritizes fast access to amplitude data and supports sparse states common in quantum simulations.

📃 Storage Strategies

The store supports multiple ways to organize data on disk via the strategy option in saveState and loadState:

flatfile: Simple, one .qstate.bin file per quantum state.
container: Efficient for many states. Stores multiple states in one large file with an internal index.
dirmapper: Uses flat files but organizes them into directories based on stateId or metadata.

🧠 Functional Query API

The primary way to interact with stored states beyond simple load/save is the Functional Query API, accessed via the query() method exported by qipai-store/index.js.

const qb = qStore.query({ storageOptions: { strategy: 'dirmapper', path: './data/run1' } });

🔎 Filtering Methods

.whereMetadata({ key: value })
.wherePhaseNear(targetPhase, tolerance?)
.whereAmplitudeAbove(threshold, qubitIndices?)
.entangledWith(qubitIndexOrGroup)

🛠 Action Methods (Conceptual)

.entangle(target)
.interfere(otherState)
.measure(basis, targetQubits)
.collapse()

🧪 Execution Methods

.limit(count)
.sort(field, direction)
.listIds()
.run()
.output()

✅ Example

import * as qStore from './qipai-store/index.js';

const results = await qStore.query({
  storageOptions: { strategy: 'dirmapper', path: './states/exp_C' }
})
  .whereMetadata({ status: 'processed', type: 'memory' })
  .entangledWith(0)
  .limit(5)
  .run();

console.log(`Found ${results.length} states.`);

🔬 Semantic Search (Conceptual)

A planned advanced feature is interference-based semantic search — finding states that constructively interfere with a given input state:

const similarStates = await qStore.interferenceSearch({
  storageOptions: { strategy: 'container', path: './memory.qdb' },
  inputState: currentThoughtState,
  basis: "meaning",
  maxResults: 10
});

⚡ Performance Optimizations for Large-Scale Quantum States

🧹 Sparse Quantum Tensor Representation

The QTensorSparse class provides a memory-efficient way to store quantum states:

const sparseTensor = new QTensorSparse({
  numQubits: 30,
  nonzeroAmplitudes: new Map([
    [0, qMath.complex(0.7071, 0)],
    [1073741823, qMath.complex(0.7071, 0)]
  ])
});

const memoryStats = sparseTensor.getMemoryComparison();
// { sparse: 40 bytes, dense: 17GB+, savings: ~99.9999998% }

🌐 Distributed Architecture for Massive Scale

QiPAI-Store includes a sharded, horizontally scalable architecture:

const store = new DistributedQStore({
  metadata: {
    type: 'elasticsearch',
    endpoints: ['http://elasticsearch:9200']
  },
  stateStorage: {
    type: 's3',
    config: { bucket: 'quantum-states', region: 'us-west-2' },
    shardingFactor: 32
  }
});

📄 QiPAI-Store as a Standalone Quantum Database

A prototype server is available that exposes HTTP endpoints:

GET /api/states
POST /api/states
GET /api/states/:id
POST /api/qql

JavaScript client library:

const client = new QStoreClient();
await client.createState({
  id: 'bell_state_01',
  numQubits: 2,
  metadata: { name: 'Bell State |00⟩ + |11⟩' },
  amplitudes: {
    0: { re: 0.7071, im: 0 },
    3: { re: 0.7071, im: 0 }
  }
});

📜 Quantum Query Language (QQL)

QQL is a symbolic DSL that abstracts quantum data operations into readable scripts. Currently implemented commands:

LOAD STATE s
WHERE s.metadata.tag = "apple"
USING STORE { strategy: 'flatfile', path: './data/apple.qstate.bin' }

INTERFERE s WITH input_state
ENTANGLE s WITH "fruit"
MEASURE s ON QUBITS [0, 1]
RETURN LAST_RESULT

📘 Advanced Example with X-basis

LOAD STATE s
WHERE s.metadata.tag = "apple"
USING STORE { strategy: 'flatfile', path: './data/apple.qstate.bin' }

ENTANGLE s WITH "fruit"
INTERFERE s WITH input_state
MEASURE s IN BASIS_X ON QUBITS [0, 1]
RETURN COLLAPSE s

This DSL is ideal for configuration files, research pipelines, and AI-directed memory access.

🔍 Looking Ahead

QiPAI-Store is well on its way to becoming the world’s first domain-specific database for quantum-inspired computation. With performance tuning, distributed support, symbolic query languages, and real-world application potential in quantum chemistry, simulation, and cognitive systems — it’s designed for the next generation of intelligent software.

qql quantum query language