mqt-quantum-classical-compiler

name	mqt-quantum-classical-compiler
description	MQT Compiler Collection - Future-proof quantum-classical compilation framework built on MLIR. Supports complex optimizations and HPC integration. Activation: quantum compiler, MQT, quantum-classical compilation, MLIR quantum, quantum circuit optimization.

MQT Quantum-Classical Compiler Collection

A blueprint for future-proof quantum-classical compilation framework built on Multi-Level Intermediate Representation (MLIR), supporting full compilation pipeline from high-level algorithms to hardware-specific instructions.

Overview

As quantum computing hardware capabilities rise, algorithms become increasingly complex, requiring sophisticated compilation frameworks that translate high-level algorithms into executable code.

Classical-First vs Quantum-First

Approach	Strengths	Best For
Quantum-First	Direct quantum optimization	Pure quantum circuits
Classical-First	Handles control flow, HPC integration	Hybrid algorithms, error correction

MQT Compiler embraces classical-first approach using MLIR.

Core Architecture

MLIR-Based Design

Built on LLVM's Multi-Level Intermediate Representation:

High-Level Algorithm
        ↓
   [Quantum Dialect]
        ↓
   [Circuit Dialect]  
        ↓
   [Hardware Dialect]
        ↓
Hardware Instructions

Key Components

1. Frontend: High-level language parsing (QASM, Q#, Python)
2. IR Optimizer: Complex optimizations beyond gate cancellation
3. Backend: Hardware-specific code generation
4. HPC Integration: Classical control flow and HPC environment support

Activation Keywords

• MQT compiler
• quantum-classical compilation
• MLIR quantum
• quantum compiler framework
• quantum circuit optimization
• quantum compilation pipeline

Tools Used

• exec: Run compiler commands
• write: Generate IR code
• read: Load quantum programs

Implementation

Step 1: Input Parsing

Parse high-level quantum programs:

# QASM input
from mqt import Compiler

compiler = Compiler()
program = compiler.parse_qasm("""
OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[0];
cx q[0], q[1];
""")

Step 2: MLIR Generation

Convert to MLIR quantum dialect:

// Example MLIR quantum IR
func @quantum_circuit(%qubits: !quantum.qubits<2>) -> !quantum.qubits<2> {
  %q0 = quantum.extract %qubits[0]
  %q0_h = quantum.H %q0
  %q1 = quantum.extract %qubits[1]
  %q0_out, %q1_out = quantum.CNOT %q0_h, %q1
  %result = quantum.insert %qubits[0], %q0_out
  %result = quantum.insert %result[1], %q1_out
  return %result
}

Step 3: Optimization Passes

Apply MLIR optimization passes:

# Optimization pipeline
pass_manager = compiler.create_pass_manager()
pass_manager.add_pass("quantum-gate-fusion")
pass_manager.add_pass("quantum-cancellation")
pass_manager.add_pass("classical-control-flow-analysis")
pass_manager.run(program)

Step 4: Hardware Lowering

Lower to hardware-specific instructions:

# Target-specific backend
target = compiler.get_target("ibm_sherbrooke")
executable = compiler.compile(program, target=target)

Advanced Features

Classical Control Flow Support

Handle structured control flow (conditionals, loops):

# Hybrid quantum-classical program
program = """
def variational_circuit(params):
    for i in range(iterations):
        apply_circuit(params)
        expectation = measure()
        params = optimizer.step(params, expectation)
    return params
"""
compiled = compiler.compile_with_control_flow(program)

Error Correction Integration

Native support for error correction codes:

# Surface code compilation
from mqt import ErrorCorrection

surface_code = ErrorCorrection.SurfaceCode(distance=3)
compiled_ft = compiler.compile_fault_tolerant(circuit, surface_code)

HPC Integration

Seamless integration with high-performance computing:

# MPI-distributed quantum simulation
from mpi4py import MPI

compiler.enable_mpi_distribution(comm=MPI.COMM_WORLD)
compiled = compiler.compile_for_distributed(circuit, num_qubits=30)

Usage Patterns

Pattern 1: Basic Compilation

from mqt import Compiler

# Compile QASM to IBM backend
compiler = Compiler()
program = compiler.load("circuit.qasm")
optimized = compiler.optimize(program, level=3)
executable = compiler.compile(optimized, target="ibm_sherbrooke")

Pattern 2: Custom Optimization Passes

# Define custom pass
class GateFusionPass(compiler.OptimizationPass):
    def run(self, circuit):
        # Custom fusion logic
        return fused_circuit

compiler.register_pass(GateFusionPass)
compiler.add_pass_to_pipeline("gate-fusion")

Pattern 3: Quantum-Classical Hybrid

# VQE with classical optimization
vqe_program = compiler.parse_python("""
from scipy.optimize import minimize

def vqe_energy(params):
    circuit = build_ansatz(params)
    return execute(circuit).expectation_value()

result = minimize(vqe_energy, x0=initial_params)
""")
compiled = compiler.compile_hybrid(vqe_program)

Configuration

Optimization Levels

Level	Passes	Use Case
0	None	Debugging
1	Basic gate cancellation	Quick compilation
2	+ Gate fusion, scheduling	Balanced
3	+ Advanced pattern matching	Best performance

Supported Targets

Backend	Status	Features
IBM Quantum	✓ Complete	Full gate set, dynamic circuits
Rigetti	✓ Complete	Native gates, parametric
IonQ	✓ Complete	Native gates, high fidelity
Simulation	✓ Complete	State vector, tensor network

References

• arXiv:2604.08674 - "The MQT Compiler Collection: A Blueprint for a Future-Proof Quantum-Classical Compilation Framework"
• MLIR Documentation - https://mlir.llvm.org/
• Munich Quantum Toolkit - https://github.com/munich-quantum-toolkit/core

Related Skills

• quantum-circuit-optimization
• quantum-error-correction
• high-performance-computing
• mlir-programming

Notes

• Open source: https://github.com/munich-quantum-toolkit/core
• Extensible pass system
• Native quantum error correction support
• Designed for future algorithm complexity
• HPC-ready architecture