🧠 JOEL Programming Language

JOEL (Just-Objects-Events Language) is a polymodal programming language that can be compiled or interpreted based on a simple file header. Write once, run anywhere — from systems programming to AI, blockchain to UI.

🚀 Quick Start

Installation

Quick Install (Recommended)

curl -fsSL https://joel.val-x.com/api/install | bash

Manual Install

# Clone the repository
git clone https://github.com/JJ-Dynamite/JOEL.git
cd JOEL

# Build and install
cargo build --release
sudo cp target/release/joel /usr/local/bin/joel
sudo chmod +x /usr/local/bin/joel

Local Install (No sudo)

cargo build --release
export PATH="$PATH:$(pwd)/target/release"

See INSTALL.md for detailed instructions.

Verify Installation

joel version
# Should output: JOEL Language v0.1.0

Run Your First Program

joel run examples/hello.joel

Example

Create a file hello.joel:

[Interpreted]

fn greet(name: str) -> str {
  return "Hello " + name
}

fn main() {
  print(greet("JOEL"))
  print("2 + 3 =", 2 + 3)
}

main()

Run it:

joel run hello.joel

✨ Features

Phase 1 - MVP ✅

✅ Lexer - Tokenizes JOEL source code
✅ Parser - Parses tokens into an AST
✅ VM/Interpreter - Executes [Interpreted] mode
✅ Header Detection - Supports [Compiled] and [Interpreted] modes
✅ Target Hints - Supports [target native], [target wasm32], [target evm], etc.
✅ Basic Types - Numbers, strings, booleans, lists, maps
✅ Control Flow - if/else, while, for loops
✅ Functions - Function definitions and calls
✅ Actors - Actor-based concurrency (syntax support)
✅ Contracts - Smart contract syntax (syntax support)
✅ Components - UI component syntax (syntax support)
✅ Flows - Workflow syntax (syntax support)

Phase 2 - Compilation & Types ✅

✅ Type System - Comprehensive type system with primitives, collections, and generics
✅ Static Type Checking - Full type checking for [Compiled] mode
✅ Type Inference - Automatic type inference with explicit annotations
✅ Ownership System - Rust-like borrow checker for memory safety
✅ Error Diagnostics - Detailed error messages with source location tracking
✅ LLVM Backend - Infrastructure for native code generation (LLVM IR)
✅ WASM Backend - Infrastructure for WebAssembly compilation
✅ Standard Library - Core modules (core, math, string, collections)

🏗️ Project Structure

JOEL/
├── src/              # Rust source code
│   ├── main.rs       # CLI entry point
│   ├── lexer.rs      # Tokenizer
│   ├── parser.rs     # Parser
│   ├── ast.rs        # Abstract Syntax Tree
│   ├── vm.rs         # Virtual Machine / Interpreter
│   ├── types.rs      # Type system
│   ├── type_checker.rs # Static type checker
│   ├── ownership.rs  # Borrow checker
│   ├── compiler.rs   # Compilation backends (LLVM, WASM, EVM)
│   ├── diagnostics.rs # Error diagnostics
│   └── stdlib.rs     # Standard library modules
├── examples/         # Example JOEL files
├── docs/             # Nextra documentation site
│   ├── pages/        # Documentation pages (MDX)
│   ├── components/   # React components
│   └── styles/       # Custom styles
├── Cargo.toml        # Rust project config
└── Dockerfile        # Docker build for docs

📚 Language Syntax

Header Modes

[Compiled]        # AOT/JIT compilation mode
[Interpreted]     # VM interpretation mode
[target wasm32]   # Optional target hint

Basic Syntax

[Interpreted]

# Variables
let x = 10
let name: str = "JOEL"

# Functions
fn add(a: i32, b: i32) -> i32 {
  return a + b
}

# Control Flow
if x > 5 {
  print("High")
} else {
  print("Low")
}

# Loops
for i in range(0, 5) {
  print(i)
}

# Actors
actor Counter {
  state let n: i64 = 0
  
  fn inc() {
    self.n += 1
  }
}

# Contracts
[Compiled]
[target evm]

contract Vault {
  state let balance: uint256 = 0
  
  fn deposit() {
    balance += tx.value
  }
}

🛠️ Commands

# Run in interpreted mode
joel run <file.joel>

# Build for a target
joel build <file.joel> --target native
joel build <file.joel> --target wasm32
joel build <file.joel> --target evm

# Build with optimizations
joel build <file.joel> --target native --optimize

# Build with debug symbols
joel build <file.joel> --target native --debug

# Build for specific architecture
joel build <file.joel> --target native --arch arm64

# Show version
joel version

📖 Documentation

Complete documentation is available online:

🌐 Live Documentation: https://joel.val-x.com

The documentation is built with Nextra (the same framework used for Next.js documentation) and features:

✨ Beautiful UI matching Next.js docs
🔍 Built-in search functionality
🌙 Dark mode support
📱 Mobile responsive
⚡ Fast page loads
📝 Markdown/MDX support
🎨 Syntax highlighting

Local Development

To run the documentation locally:

# Navigate to docs directory
cd docs

# Install dependencies
npm install

# Start development server
npm run dev

Then open http://localhost:3000 in your browser.

📖 Examples

The examples/ directory contains several example files:

hello.joel - Basic syntax and functions
arithmetic.joel - Math operations
control_flow.joel - Control structures
actor.joel - Actor-based concurrency
contract.joel - Smart contract example
ui_component.joel - UI component example
flow.joel - Workflow example
deployment.joel - Container deployment
build_native.joel - Native compilation example
build_wasm.joel - WebAssembly compilation example
build_evm.joel - EVM smart contract example
build_ios.joel - iOS framework example
build_android.joel - Android library example
build_cosmos.joel - Cosmos SDK contract example

Run any example:

joel run examples/hello.joel

Build examples:

# Build for native
joel build examples/build_native.joel --target native

# Build for WASM with debug
joel build examples/build_wasm.joel --target wasm32 --debug

# Build smart contract
joel build examples/build_evm.joel --target evm

# Build for iOS
joel build examples/build_ios.joel --target ios

# Build for Android
joel build examples/build_android.joel --target android

# Build Cosmos contract
joel build examples/build_cosmos.joel --target cosmos

See TESTING.md for detailed test information.

🗺️ Roadmap

Phase 1: Core Language ✅

Lexer - Tokenizes JOEL source code
Parser - Builds AST from tokens
VM/Interpreter - Executes [Interpreted] mode
Basic syntax support (variables, functions, control flow)
Built-in functions (print, range)
Documentation site with Nextra
CLI tool (joel command)
Example programs

Phase 2: Compilation & Types ✅

LLVM backend infrastructure for [Compiled] mode
WASM target compilation infrastructure
Static type checking
Type inference improvements
Ownership system (Rust-like borrow checker)
Error messages and diagnostics
Standard library core modules

Phase 3: Specialized Targets ✅

LLVM IR generation for native target
WASM binary generation for web target
EVM bytecode generation for Ethereum
Solana BPF generation
Optimization support (--optimize flag)
Debug symbols support (--debug flag)
Source map generation for WASM
Cross-compilation support (x86_64, ARM64, RISC-V) Goal: Complete compilation backends for various deployment targets

3.1 Blockchain Targets

3.2 Native & Web Targets

3.3 Mobile Targets

iOS target - Compile to native iOS frameworks
- LLVM IR generation for iOS
- ARM64 iOS architecture support
- Framework packaging
Android target - Compile to native Android libraries
- LLVM IR generation for Android
- ARM64 Android architecture support
- AAR packaging
React Native integration - Seamless mobile development

Phase 4: Advanced Features 🚧

Goal: Enable advanced programming paradigms and domain-specific features

4.1 UI & Frontend

4.2 Container & Infrastructure

4.3 Concurrency & Parallelism

4.4 Pattern Matching & Control Flow

Phase 5: Ecosystem & Tooling

Goal: Build a complete development ecosystem

5.1 Package Management

5.2 Developer Experience

5.3 Testing & Quality

5.4 Documentation

Documentation generator - API documentation
- Markdown/HTML output
- Interactive examples
- Type documentation
Tutorial system - Interactive learning

Phase 6: Database Programming

Goal: Native SQL query support like DuckDB - write SQL directly in JOEL

6.1 SQL as First-Class Datatype

SQL datatype - SQL is a native datatype in JOEL

[Compiled]

# SQL query as a variable with type inference
sql result = 
  SELECT name, age, salary 
  FROM employees 
  WHERE age > 30 
  ORDER BY salary DESC

# Explicit type annotation
sql result: sql<{name: str, age: i32, salary: f64}> = 
  SELECT name, age, salary FROM employees

# SQL in function parameters and returns
fn get_employees(min_age: i32) -> sql<{name: str, age: i32}> {
  sql result = 
    SELECT name, age 
    FROM employees 
    WHERE age > min_age
  return result
}

SQL parser and executor - Full SQL standard support
- SELECT, INSERT, UPDATE, DELETE statements
- JOIN operations (INNER, LEFT, RIGHT, FULL, CROSS)
- Subqueries and CTEs (Common Table Expressions)
- Window functions (ROW_NUMBER, RANK, PARTITION BY, OVER)
- Aggregations (SUM, COUNT, AVG, MAX, MIN, GROUP BY, HAVING)
- Set operations (UNION, INTERSECT, EXCEPT)
Query optimization - Automatic query optimization
- Query planner with cost-based optimization
- Index selection and usage
- Join reordering and algorithms
- Predicate pushdown
- Projection pushdown
Columnar storage - Efficient analytical queries
- Columnar data format
- Compression (RLE, Dictionary encoding, Delta encoding)
- Vectorized execution engine
- SIMD-optimized operations

6.2 SQL Type System & Safety

Type-safe SQL queries - Compile-time SQL validation

[Compiled]

# Type inference from SQL schema
sql employees: sql<{name: str, age: i32, salary: f64}> = 
  SELECT name, age, salary FROM employees

# Access results with type safety
for emp in employees {
  print(emp.name, emp.age, emp.salary)
}

# Type checking for SQL operations
sql joined: sql<{name: str, dept: str}> = 
  SELECT e.name, d.name as dept
  FROM employees e
  JOIN departments d ON e.department_id = d.id

SQL schema definition - Define tables in JOEL

[Compiled]

table employees {
  id: i64 PRIMARY KEY AUTO_INCREMENT,
  name: str NOT NULL,
  age: i32 CHECK (age >= 18),
  salary: f64 DEFAULT 0.0,
  department_id: i64 FOREIGN KEY REFERENCES departments(id),
  created_at: timestamp DEFAULT NOW()
}

# Indexes
index idx_employees_age ON employees(age)
index idx_employees_dept ON employees(department_id)

Parameterized queries - Safe SQL with type-checked parameters

[Compiled]

fn find_users(min_age: i32, city: str) -> sql<{id: i64, name: str}> {
  sql results = 
    SELECT id, name 
    FROM users 
    WHERE age > min_age AND city = city
  return results
}

# Or with explicit parameter binding
sql query: sql<{name: str}> = 
  SELECT name FROM users WHERE age > ? AND city = ?
let results = query.bind(25, "NYC")

SQL expressions in JOEL - Mix SQL and JOEL seamlessly

[Compiled]

fn calculate_stats() {
  sql avg_salary: sql<{avg: f64}> = 
    SELECT AVG(salary) as avg FROM employees
  
  let avg = avg_salary.first().avg
  
  sql top_earners: sql<{name: str, salary: f64}> = 
    SELECT name, salary 
    FROM employees 
    WHERE salary > avg
    ORDER BY salary DESC
    LIMIT 10
  
  for emp in top_earners {
    print(emp.name, emp.salary)
  }
}

6.3 Database Connectivity & Operations

Embedded database - DuckDB-like in-process database
- No external server required
- Fast analytical queries
- Direct file access (CSV, Parquet, JSON, Arrow)
- In-memory and persistent modes

Database connections - Connect to external databases

[Compiled]

# Connection as a type
db postgres = connect("postgresql://localhost/mydb")

# Use connection in SQL
sql users: sql<{id: i64, name: str}> = 
  SELECT id, name FROM users
USING postgres

# Multiple database support
db analytics = connect("clickhouse://analytics-server/data")
sql metrics: sql<{metric: str, value: f64}> = 
  SELECT metric, value FROM metrics
USING analytics

Database drivers - Native database support
- PostgreSQL, MySQL, SQLite
- ClickHouse, TimescaleDB
- MongoDB (with SQL interface)
- Redis (with SQL-like queries)
- DuckDB (embedded analytics)

Connection pooling - Efficient database connections

[Compiled]

pool db_pool = connection_pool(
  url: "postgresql://localhost/mydb",
  min_connections: 5,
  max_connections: 20
)

Transaction support - ACID transactions with SQL syntax

[Compiled]

transaction {
  sql INSERT INTO accounts (id, balance) VALUES (1, 1000)
  sql INSERT INTO accounts (id, balance) VALUES (2, 500)
  commit
}

# Or with automatic rollback on error
transaction {
  sql UPDATE accounts SET balance = balance - 100 WHERE id = 1
  sql UPDATE accounts SET balance = balance + 100 WHERE id = 2
  # Auto-commit on success, auto-rollback on error
}

6.4 Advanced SQL Features

Complex queries - Advanced SQL capabilities

[Compiled]

# Recursive CTEs
sql org_tree: sql<{id: i64, name: str, level: i32}> = 
  WITH RECURSIVE org AS (
    SELECT id, name, 0 as level FROM departments WHERE parent_id IS NULL
    UNION ALL
    SELECT d.id, d.name, o.level + 1
    FROM departments d
    JOIN org o ON d.parent_id = o.id
  )
  SELECT * FROM org

# PIVOT operations
sql sales_by_region: sql<{region: str, q1: f64, q2: f64, q3: f64, q4: f64}> = 
  SELECT * FROM (
    SELECT region, quarter, amount FROM sales
  ) PIVOT (
    SUM(amount) FOR quarter IN ('Q1', 'Q2', 'Q3', 'Q4')
  )

# LATERAL joins
sql top_products: sql<{category: str, product: str, sales: f64}> = 
  SELECT c.name as category, p.name as product, p.sales
  FROM categories c
  CROSS JOIN LATERAL (
    SELECT name, sales FROM products
    WHERE category_id = c.id
    ORDER BY sales DESC LIMIT 3
  ) p

SQL views - Reusable query definitions as first-class objects

[Compiled]

view high_earners: sql<{name: str, salary: f64, department: str}> = 
  SELECT e.name, e.salary, d.name as department
  FROM employees e
  JOIN departments d ON e.department_id = d.id
  WHERE e.salary > 100000

# Use view like any SQL query
sql top_10: sql<{name: str, salary: f64}> = 
  SELECT name, salary FROM high_earners
  ORDER BY salary DESC
  LIMIT 10

SQL functions - Database functions in JOEL

[Compiled]

# SQL function that returns SQL result
fn get_employee_stats(dept_id: i64) -> sql<{count: i64, avg_salary: f64}> {
  sql stats = 
    SELECT COUNT(*) as count, AVG(salary) as avg_salary
    FROM employees
    WHERE department_id = dept_id
  return stats
}

# SQL function with SQL body
sql_function calculate_bonus(salary: f64, performance: f64) -> f64 = 
  SELECT salary * performance * 0.1

SQL triggers - Event-driven database operations

[Compiled]

trigger on_employee_update AFTER UPDATE ON employees {
  sql INSERT INTO audit_log (table_name, action, timestamp)
  VALUES ('employees', 'UPDATE', NOW())
}

SQL indexes - Automatic and manual index management

[Compiled]

# Automatic index creation from queries
# Manual index definition
index idx_employees_name_salary ON employees(name, salary DESC)
index idx_employees_dept_hash ON employees USING HASH(department_id)

6.5 Data Processing & Analytics

SQL result manipulation - Work with SQL results in JOEL

[Compiled]

sql sales: sql<{region: str, amount: f64}> = 
  SELECT region, amount FROM sales

# Iterate over results
for sale in sales {
  print(sale.region, sale.amount)
}

# Convert to list
let sales_list = sales.to_list()

# Filter and transform
sql filtered: sql<{region: str, total: f64}> = 
  SELECT region, SUM(amount) as total
  FROM sales
  WHERE amount > 1000
  GROUP BY region

# Chain SQL operations
sql result = 
  SELECT region, SUM(amount) as total
  FROM (SELECT * FROM sales WHERE date > '2024-01-01')
  GROUP BY region
  HAVING SUM(amount) > 10000

Streaming SQL - Real-time query processing

[Compiled]

# Continuous queries
stream sensor_stream = 
  SELECT sensor_id, value, timestamp
  FROM sensor_readings
  WHERE timestamp > NOW() - INTERVAL '1 minute'

# Window functions over streams
sql windowed: sql<{sensor_id: i64, avg_value: f64}> = 
  SELECT 
    sensor_id,
    AVG(value) OVER (
      PARTITION BY sensor_id 
      ORDER BY timestamp 
      ROWS BETWEEN 9 PRECEDING AND CURRENT ROW
    ) as avg_value
  FROM sensor_stream

Data connectors - Query external data sources directly

[Compiled]

# Query CSV files directly (no import needed)
sql csv_data: sql<{name: str, value: f64}> = 
  SELECT name, value FROM 'data.csv'

# Query Parquet files
sql parquet_data: sql<{id: i64, metric: f64}> = 
  SELECT id, metric 
  FROM 'analytics.parquet' 
  WHERE date > '2024-01-01'

# Query JSON files
sql json_data: sql<{id: i64, name: str}> = 
  SELECT id, name FROM 'users.json'

# Query HTTP APIs as tables
sql api_data: sql<{id: i64, title: str}> = 
  SELECT id, title 
  FROM http('https://api.example.com/posts')

# Query multiple files
sql combined: sql<{source: str, value: f64}> = 
  SELECT 'csv' as source, value FROM 'data.csv'
  UNION ALL
  SELECT 'parquet' as source, value FROM 'data.parquet'

Time-series queries - Temporal data handling

[Compiled]

sql hourly_avg: sql<{hour: timestamp, avg_temp: f64}> = 
  SELECT 
    time_bucket('1 hour', timestamp) as hour,
    AVG(temperature) as avg_temp
  FROM sensor_data
  WHERE timestamp > NOW() - INTERVAL '24 hours'
  GROUP BY hour
  ORDER BY hour

# Time-series functions
sql trends: sql<{timestamp: timestamp, value: f64, moving_avg: f64}> = 
  SELECT 
    timestamp,
    value,
    AVG(value) OVER (
      ORDER BY timestamp 
      ROWS BETWEEN 11 PRECEDING AND CURRENT ROW
    ) as moving_avg
  FROM time_series_data

Geospatial queries - Location-based SQL

[Compiled]

sql nearby: sql<{name: str, distance: f64}> = 
  SELECT 
    name, 
    ST_Distance(location, POINT(40.7128, -74.0060)) as distance
  FROM places
  WHERE ST_DWithin(location, POINT(40.7128, -74.0060), 1000)
  ORDER BY distance

# Geospatial operations
sql coverage: sql<{area: f64, population: i64}> = 
  SELECT 
    ST_Area(coverage_zone) as area,
    SUM(population) as population
  FROM regions
  WHERE ST_Intersects(coverage_zone, query_area)
  GROUP BY coverage_zone

6.6 SQL Toolchain (`joelsql`)

6.7 SQL Performance & Optimization

Query caching - Automatic caching of query results

[Compiled]

# Automatic caching for repeated queries
sql cached_result: sql<{id: i64, name: str}> = 
  SELECT id, name FROM users
CACHE FOR 5 MINUTES

# Manual cache control
cache.clear()
cache.invalidate("users")

Materialized views - Pre-computed query results

[Compiled]

materialized view daily_sales: sql<{date: date, total: f64}> = 
  SELECT 
    DATE(created_at) as date,
    SUM(amount) as total
  FROM sales
  GROUP BY DATE(created_at)

# Auto-refresh materialized views
REFRESH MATERIALIZED VIEW daily_sales

Query profiling - Analyze query performance

[Compiled]

sql result = SELECT * FROM employees WHERE age > 30
let profile = result.profile()
print("Execution time:", profile.execution_time)
print("Rows scanned:", profile.rows_scanned)
print("Indexes used:", profile.indexes_used)

Execution plans - EXPLAIN query plans with SQL syntax

[Compiled]

sql plan: sql<{operation: str, cost: f64}> = 
  EXPLAIN SELECT * FROM employees WHERE age > 30

# Or get plan as structured data
let plan_data = explain(
  SELECT * FROM employees WHERE age > 30
)
print(plan_data)

Parallel query execution - Multi-threaded queries

[Compiled]

# Automatic parallelization
sql parallel_result = 
  SELECT * FROM large_table
  PARALLEL 4  # Use 4 threads

# Parallel aggregations
sql aggregated = 
  SELECT region, SUM(sales) as total
  FROM sales
  GROUP BY region
  PARALLEL

Query hints - Manual optimization hints

[Compiled]

sql optimized = 
  SELECT /*+ USE_INDEX(idx_age) */ *
  FROM employees
  WHERE age > 30

# Join hints
sql joined = 
  SELECT * FROM employees e
  /*+ USE_HASH_JOIN */ JOIN departments d ON e.dept_id = d.id

Query compilation - Compile SQL to native code

[Compiled]

# JIT-compiled queries for hot paths
sql compiled_query: sql<{result: f64}> = 
  SELECT SUM(amount) as result FROM sales
COMPILE  # Compile to native code for repeated execution

Phase 7: Quantum Programming (High-Level)

Goal: High-level quantum computing support and abstractions

7.1 Quantum Circuit Compilation

7.2 Quantum Backends

7.3 Hybrid Classical-Quantum

Phase 8: Performance & Optimization

Goal: Maximize runtime performance and efficiency

8.1 Compiler Optimizations

8.2 Memory Management

8.3 Performance Tooling

Phase 9: Security & Safety

Goal: Enterprise-grade security features and safety guarantees

9.1 Security Features

9.2 Formal Verification

9.3 Security Tooling

Phase 10: Interoperability

Goal: Seamless integration with existing ecosystems

10.1 Foreign Function Interface (`joelffi`)

10.2 Protocol Support

10.3 Data Format Support

Phase 11: Distributed Systems

Goal: Built-in distributed computing support

11.1 Distributed Computing

11.2 Decentralized Systems

Phase 12: AI/ML Integration

Goal: Native machine learning capabilities

12.1 Machine Learning

12.2 LLM Integration

Phase 13: Real-time & Streaming

Goal: Real-time data processing and event streaming

13.1 Streaming (`joelstream`)

13.2 Real-time Systems

Phase 14: Quantum Programming (Qubit-Level)

Goal: Low-level qubit manipulation and quantum circuit programming

14.1 Qubit Operations

Qubit datatype - Native qubit type in JOEL

[Compiled]

# Qubit declaration and initialization
qubit q0 = |0⟩
qubit q1 = |1⟩
qubit q2 = |+⟩  # Superposition state

# Qubit registers
qubit[3] register = [|0⟩, |0⟩, |0⟩]
qubit[5] entangled_pair = create_bell_pair()

Quantum state manipulation - Direct qubit state operations

[Compiled]

# Initialize qubit in specific state
qubit q = |0⟩

# Apply quantum gates
q = H(q)        # Hadamard gate
q = X(q)        # Pauli-X (NOT gate)
q = Y(q)        # Pauli-Y gate
q = Z(q)        # Pauli-Z gate

# Rotation gates
q = RX(π/2, q)  # Rotation around X-axis
q = RY(π/4, q)  # Rotation around Y-axis
q = RZ(π/8, q)  # Rotation around Z-axis

# Controlled operations
qubit control = |1⟩
qubit target = |0⟩
target = CNOT(control, target)  # Controlled-NOT
target = CZ(control, target)     # Controlled-Z

Quantum gates library - Comprehensive gate operations
- Single-qubit gates (H, X, Y, Z, S, T, Phase)
- Two-qubit gates (CNOT, CZ, SWAP, iSWAP)
- Multi-qubit gates (Toffoli, Fredkin, CCX, CCZ)
- Custom unitary gates
- Parameterized gates

14.2 Quantum Circuit Construction

Quantum circuit DSL - Build circuits with qubit-level control

[Compiled]

# Define quantum circuit
quantum_circuit bell_state() -> qubit[2] {
  qubit[2] q = [|0⟩, |0⟩]
  
  # Apply Hadamard to first qubit
  q[0] = H(q[0])
  
  # Apply CNOT
  q[1] = CNOT(q[0], q[1])
  
  return q
}

# Execute circuit
let result = bell_state()

Circuit composition - Combine and compose quantum circuits

[Compiled]

quantum_circuit sub_circuit(qubit q) -> qubit {
  q = H(q)
  q = T(q)
  return q
}

quantum_circuit main_circuit() -> qubit[3] {
  qubit[3] q = [|0⟩, |0⟩, |0⟩]
  
  # Apply sub-circuit to each qubit
  for i in 0..3 {
    q[i] = sub_circuit(q[i])
  }
  
  # Entangle qubits
  q[1] = CNOT(q[0], q[1])
  q[2] = CNOT(q[1], q[2])
  
  return q
}

Quantum circuit optimization - Automatic circuit optimization

[Compiled]

quantum_circuit optimized() -> qubit[2] {
  qubit[2] q = [|0⟩, |0⟩]
  # Circuit operations...
  return q
}

# Optimize circuit
let optimized_circuit = optimize(optimized, 
  target_gates: ["CNOT", "H", "T"],
  optimize_depth: true
)

14.3 Quantum Measurement & Observables

Measurement operations - Measure qubits in different bases

[Compiled]

qubit q = |+⟩

# Measure in computational basis
let result_z: bool = measure(q)  # Returns 0 or 1

# Measure in different bases
let result_x: bool = measure_x(q)  # X-basis measurement
let result_y: bool = measure_y(q)  # Y-basis measurement

# Partial measurement
qubit[3] register = [|0⟩, |+⟩, |1⟩]
let first_qubit: bool = measure(register[0])
# Other qubits remain in superposition

Observable operators - Quantum observables and expectation values

[Compiled]

qubit q = |+⟩

# Define observables
observable PauliX = X
observable PauliY = Y
observable PauliZ = Z

# Calculate expectation values
let exp_x: f64 = expectation(PauliX, q)
let exp_y: f64 = expectation(PauliY, q)
let exp_z: f64 = expectation(PauliZ, q)

# Multi-qubit observables
observable ZZ = Z ⊗ Z
let correlation: f64 = expectation(ZZ, [q0, q1])

Quantum state tomography - Reconstruct quantum states

[Compiled]

# Perform state tomography
qubit q = prepare_unknown_state()
let reconstructed_state = tomography(q, 
  measurements: 1000,
  bases: ["X", "Y", "Z"]
)

14.4 Quantum Algorithms at Qubit Level

Quantum algorithm primitives - Low-level algorithm building blocks

[Compiled]

# Quantum Fourier Transform (QFT)
quantum_circuit qft(qubit[n] q) -> qubit[n] {
  for i in 0..n {
    q[i] = H(q[i])
    for j in (i+1)..n {
      q[j] = controlled_phase(2π/2^(j-i+1), q[i], q[j])
    }
  }
  return reverse(q)
}

# Quantum phase estimation
quantum_circuit phase_estimation(
  qubit[n] ancilla,
  qubit target,
  fn U: qubit -> qubit
) -> qubit[n] {
  # Apply QFT to ancilla
  ancilla = qft(ancilla)
  
  # Apply controlled-U operations
  for i in 0..n {
    for _ in 0..(2^i) {
      target = controlled(U, ancilla[i], target)
    }
  }
  
  # Inverse QFT
  ancilla = inverse_qft(ancilla)
  return ancilla
}

Grover's algorithm - Quantum search at qubit level

[Compiled]

quantum_circuit grover_search(
  qubit[n] database,
  fn oracle: qubit[n] -> qubit[n]
) -> qubit[n] {
  # Initialize superposition
  for i in 0..n {
    database[i] = H(database[i])
  }
  
  # Grover iterations
  let iterations = floor(π/4 * sqrt(2^n))
  for _ in 0..iterations {
    database = oracle(database)
    database = grover_diffuser(database)
  }
  
  return database
}

Shor's algorithm - Quantum factorization

[Compiled]

quantum_circuit shor_factorization(n: i64) -> i64 {
  # Quantum period finding
  qubit[log2(n)] register = initialize_superposition()
  qubit target = |1⟩
  
  # Modular exponentiation
  register = modular_exponentiation(register, target, n)
  
  # Quantum Fourier Transform
  register = qft(register)
  
  # Measure and extract period
  let measurement = measure_all(register)
  let period = continued_fractions(measurement, n)
  
  # Classical post-processing
  return factor_from_period(period, n)
}

14.5 Quantum Error Correction

Error correction codes - Implement QEC codes

[Compiled]

# Three-qubit bit-flip code
quantum_circuit encode_bit_flip(qubit logical) -> qubit[3] {
  qubit[3] physical = [|0⟩, |0⟩, |0⟩]
  physical[0] = logical
  physical[1] = CNOT(logical, physical[1])
  physical[2] = CNOT(logical, physical[2])
  return physical
}

# Shor's 9-qubit code
quantum_circuit encode_shor(qubit logical) -> qubit[9] {
  # Encode logical qubit into 9 physical qubits
  # Protects against bit-flip and phase-flip errors
}

# Surface code
quantum_circuit surface_code_encode(qubit logical) -> qubit[n] {
  # Topological quantum error correction
}

Syndrome measurement - Detect and correct errors

[Compiled]

quantum_circuit error_correction(qubit[n] encoded) -> qubit[n] {
  # Measure stabilizers
  let syndrome = measure_stabilizers(encoded)
  
  # Decode syndrome to error location
  let error_location = decode_syndrome(syndrome)
  
  # Apply correction
  encoded = apply_correction(encoded, error_location)
  
  return encoded
}

14.6 Quantum Simulation & Emulation

State vector simulation - Simulate quantum states

[Compiled]

# Simulate quantum circuit
qubit[3] q = initialize_state([|0⟩, |0⟩, |0⟩])
q = apply_circuit(q, my_circuit)

# Get state vector
let state: complex[8] = get_state_vector(q)

# Calculate probabilities
let probabilities: f64[8] = |state|^2

Density matrix simulation - Mixed state simulation

[Compiled]

# Create mixed state
let rho: density_matrix = 0.5 * |0⟩⟨0| + 0.5 * |1⟩⟨1|

# Apply quantum channel
rho = apply_channel(rho, depolarizing_channel(0.1))

# Calculate fidelity
let fidelity: f64 = calculate_fidelity(rho, target_state)

Noise models - Realistic quantum noise simulation

[Compiled]

# Define noise model
noise_model model = {
  gate_error: 0.001,      # 0.1% gate error
  measurement_error: 0.01, # 1% measurement error
  decoherence: {
    T1: 100e-6,  # 100 microseconds
    T2: 50e-6    # 50 microseconds
  }
}

# Simulate with noise
qubit q = |+⟩
q = simulate_with_noise(q, my_circuit, model)

14.7 Quantum Hardware Integration

Hardware abstraction - Unified interface for quantum hardware

[Compiled]

# Connect to quantum hardware
quantum_backend ibm = connect("ibm_quantum", api_key: "...")
quantum_backend google = connect("cirq", processor: "sycamore")
quantum_backend ionq = connect("ionq", api_key: "...")

# Execute on hardware
qubit[5] q = prepare_circuit()
let result = execute(q, ibm, shots: 1024)

Pulse-level control - Direct control of quantum hardware

[Compiled]

# Define pulse sequences
pulse drive_pulse = gaussian_pulse(
  duration: 100e-9,  # 100 nanoseconds
  amplitude: 0.5,
  frequency: 5.0e9   # 5 GHz
)

pulse readout_pulse = square_pulse(
  duration: 1e-6,
  amplitude: 0.3
)

# Schedule pulses
schedule pulses = {
  t=0ns: drive_pulse(channel: "q0_drive"),
  t=100ns: readout_pulse(channel: "q0_readout")
}

# Execute pulse sequence
let result = execute_pulses(pulses, hardware: ibm)

Calibration - Quantum hardware calibration

[Compiled]

# Calibrate qubit
let calibration = calibrate_qubit(
  qubit: "q0",
  gates: ["X", "Y", "Z", "H"],
  optimize: true
)

# Use calibrated parameters
qubit q = |0⟩
q = X_calibrated(q, calibration.parameters["X"])

14.8 Quantum-Classical Hybrid Programming

Hybrid algorithms - Combine quantum and classical computation

[Compiled]

fn variational_quantum_eigensolver(
  hamiltonian: matrix,
  ansatz: quantum_circuit
) -> f64 {
  # Classical optimizer
  let optimizer = Adam(learning_rate: 0.01)
  let params = initialize_parameters()
  
  for iteration in 0..100 {
    # Quantum part: measure expectation value
    qubit[n] q = ansatz(params)
    let energy: f64 = expectation(hamiltonian, q)
    
    # Classical part: update parameters
    let gradient = calculate_gradient(energy, params)
    params = optimizer.update(params, gradient)
  }
  
  return energy
}

Parameterized quantum circuits - Trainable quantum circuits

[Compiled]

quantum_circuit parameterized_ansatz(
  params: f64[n],
  qubit[n] q
) -> qubit[n] {
  for i in 0..n {
    q[i] = RY(params[i], q[i])
    if i < n-1 {
      q[i+1] = CNOT(q[i], q[i+1])
    }
  }
  return q
}

Phase 15: Web3 & Blockchain Integration

Goal: Comprehensive blockchain and Web3 development support

15.1 Blockchain Development

15.2 DeFi Integration

DeFi protocols - Decentralized finance support
- DEX integration (Uniswap, SushiSwap)
- Lending protocols (Aave, Compound)
- Yield farming automation
NFT support - Non-fungible token operations
- NFT minting
- NFT marketplace integration
- Metadata management

15.3 Cross-Chain Operations

Cross-chain bridges - Multi-chain interoperability
- Bridge protocol integration
- Asset transfers
- Cross-chain messaging

Phase 16: Game Development

Goal: Native game development capabilities

16.1 Game Engine Integration

16.2 Graphics & Rendering

16.3 Game Systems

Phase 17: Scientific Computing

Goal: High-performance scientific computing

17.1 Numerical Computing

17.2 Data Analysis

Phase 18: Embedded Systems

Goal: Embedded and IoT development support

18.1 Embedded Targets

Microcontroller support - MCU compilation
- ARM Cortex-M support
- AVR support
- RISC-V embedded
Resource constraints - Memory-constrained environments
- Stack size optimization
- Heap management for embedded
- Flash memory optimization

18.2 IoT Integration

Phase 19: Cloud Native

Goal: Cloud-native application development

19.1 Cloud Services

19.2 Observability

Phase 20: Language Evolution

Goal: Continuous language improvement and evolution

20.1 Language Features

20.2 Standard Library Expansion

Note: Phases are not strictly sequential. Some features may be developed in parallel or reordered based on community needs and priorities.

📚 Additional Resources

Installation Guide - Detailed installation instructions
Quick Start Guide - Get started quickly
Architecture - Technical architecture details
Testing Guide - Testing information

🤝 Contributing

This is an early-stage project. Contributions welcome!

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

MIT License

JOEL - One Language, All Layers 🚀

Name		Name	Last commit message	Last commit date
Latest commit History 63 Commits
docs		docs
examples		examples
src		src
.dockerignore		.dockerignore
.gitignore		.gitignore
ARCHITECTURE.md		ARCHITECTURE.md
Cargo.toml		Cargo.toml
Dockerfile		Dockerfile
INSTALL.md		INSTALL.md
QUICKSTART.md		QUICKSTART.md
README.md		README.md
TESTING.md		TESTING.md
test_examples.sh		test_examples.sh

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