"""
Quantum Symbolic Interface Module for NSAF
-----------------------------------------
Author: Bolorerdene Bundgaa
Contact: bolor@ariunbolor.org
Website: https://bolor.me
Provides a high-level interface for quantum symbolic computations.
"""
from typing import Dict, List, Optional, Union, Any, Tuple
from dataclasses import dataclass
from enum import Enum
import numpy as np
from .circuit import QuantumCircuit, GateType, QuantumState
from .symbolic import (
SymbolicProcessor,
SymbolicExpression,
SymbolicType,
LogicalOperator
)
class ComputationType(Enum):
"""Types of quantum symbolic computations"""
LOGICAL_INFERENCE = "logical_inference"
CONSTRAINT_SOLVING = "constraint_solving"
OPTIMIZATION = "optimization"
THEOREM_PROVING = "theorem_proving"
PATTERN_MATCHING = "pattern_matching"
@dataclass
class ComputationResult:
"""Result of a quantum symbolic computation"""
success: bool
result: Any
probability: float
quantum_state: Optional[QuantumState] = None
metadata: Optional[Dict[str, Any]] = None
class QuantumSymbolicInterface:
"""Main interface for quantum symbolic computations"""
def __init__(self):
"""Initialize the quantum symbolic interface"""
self.symbolic_processor = SymbolicProcessor()
self.computation_history: List[ComputationResult] = []
def create_expression(self,
expr_type: SymbolicType,
value: Any,
children: Optional[List[SymbolicExpression]] = None,
metadata: Optional[Dict[str, Any]] = None) -> SymbolicExpression:
"""Create a symbolic expression"""
return SymbolicExpression(expr_type, value, children, metadata)
def create_logical_expression(self,
operator: LogicalOperator,
operands: List[SymbolicExpression]) -> SymbolicExpression:
"""Create a logical expression"""
return self.symbolic_processor.create_operator(operator, operands)
def evaluate_expression(self,
expr: SymbolicExpression,
computation_type: ComputationType,
parameters: Optional[Dict[str, Any]] = None) -> ComputationResult:
"""Evaluate a symbolic expression using quantum computation"""
try:
# Convert to quantum circuit
circuit = self.symbolic_processor.to_quantum_circuit(expr)
# Apply computation-specific optimizations
self._optimize_circuit(circuit, computation_type, parameters)
# Run quantum computation
quantum_state = circuit.run()
# Process results based on computation type
result, probability = self._process_results(
quantum_state,
computation_type,
parameters
)
# Create and store computation result
computation_result = ComputationResult(
success=True,
result=result,
probability=probability,
quantum_state=quantum_state,
metadata={
'computation_type': computation_type,
'circuit_depth': len(circuit.gates),
'num_qubits': circuit.num_qubits
}
)
self.computation_history.append(computation_result)
return computation_result
except Exception as e:
# Handle computation errors
return ComputationResult(
success=False,
result=str(e),
probability=0.0,
metadata={'error': str(e)}
)
def _optimize_circuit(self,
circuit: QuantumCircuit,
computation_type: ComputationType,
parameters: Optional[Dict[str, Any]] = None) -> None:
"""Optimize quantum circuit based on computation type"""
if computation_type == ComputationType.LOGICAL_INFERENCE:
self._optimize_for_inference(circuit, parameters)
elif computation_type == ComputationType.CONSTRAINT_SOLVING:
self._optimize_for_constraints(circuit, parameters)
elif computation_type == ComputationType.OPTIMIZATION:
self._optimize_for_optimization(circuit, parameters)
# Add more optimization strategies as needed
def _optimize_for_inference(self,
circuit: QuantumCircuit,
parameters: Optional[Dict[str, Any]] = None) -> None:
"""Optimize circuit for logical inference"""
# Implement inference-specific optimizations
# For example: gate cancellation, circuit depth reduction
pass
def _optimize_for_constraints(self,
circuit: QuantumCircuit,
parameters: Optional[Dict[str, Any]] = None) -> None:
"""Optimize circuit for constraint solving"""
# Implement constraint-specific optimizations
# For example: ancilla qubit optimization, measurement reduction
pass
def _optimize_for_optimization(self,
circuit: QuantumCircuit,
parameters: Optional[Dict[str, Any]] = None) -> None:
"""Optimize circuit for optimization problems"""
# Implement optimization-specific circuit improvements
# For example: quantum annealing patterns, adiabatic optimization
pass
def _process_results(self,
quantum_state: QuantumState,
computation_type: ComputationType,
parameters: Optional[Dict[str, Any]] = None) -> Tuple[Any, float]:
"""Process quantum computation results"""
if computation_type == ComputationType.LOGICAL_INFERENCE:
return self._process_inference_results(quantum_state, parameters)
elif computation_type == ComputationType.CONSTRAINT_SOLVING:
return self._process_constraint_results(quantum_state, parameters)
elif computation_type == ComputationType.OPTIMIZATION:
return self._process_optimization_results(quantum_state, parameters)
# Add more result processing strategies as needed
return None, 0.0
def _process_inference_results(self,
quantum_state: QuantumState,
parameters: Optional[Dict[str, Any]] = None) -> Tuple[Any, float]:
"""Process results for logical inference"""
# Implement inference-specific result processing
# For example: extract logical conclusions, calculate confidence
return None, 0.0
def _process_constraint_results(self,
quantum_state: QuantumState,
parameters: Optional[Dict[str, Any]] = None) -> Tuple[Any, float]:
"""Process results for constraint solving"""
# Implement constraint-specific result processing
# For example: extract solution assignments, verify constraints
return None, 0.0
def _process_optimization_results(self,
quantum_state: QuantumState,
parameters: Optional[Dict[str, Any]] = None) -> Tuple[Any, float]:
"""Process results for optimization problems"""
# Implement optimization-specific result processing
# For example: extract optimal values, calculate objective function
return None, 0.0
def get_computation_history(self) -> List[ComputationResult]:
"""Get history of computations"""
return self.computation_history
def clear_history(self) -> None:
"""Clear computation history"""
self.computation_history.clear()
def get_circuit_statistics(self, circuit: QuantumCircuit) -> Dict[str, Any]:
"""Get statistics about a quantum circuit"""
return {
'num_qubits': circuit.num_qubits,
'circuit_depth': len(circuit.gates),
'gate_counts': self._count_gates(circuit),
'estimated_runtime': self._estimate_runtime(circuit)
}
def _count_gates(self, circuit: QuantumCircuit) -> Dict[str, int]:
"""Count gates by type in the circuit"""
gate_counts: Dict[str, int] = {}
for gate in circuit.gates:
gate_type = str(gate.gate_type)
gate_counts[gate_type] = gate_counts.get(gate_type, 0) + 1
return gate_counts
def _estimate_runtime(self, circuit: QuantumCircuit) -> float:
"""Estimate runtime for the circuit in microseconds"""
# Simple estimation based on circuit depth and gate types
# In practice, this would be more sophisticated
base_time = 1.0 # Base time per gate in microseconds
total_time = 0.0
for gate in circuit.gates:
if gate.gate_type in [GateType.H, GateType.X, GateType.Y, GateType.Z]:
total_time += base_time
elif gate.gate_type in [GateType.CNOT, GateType.SWAP]:
total_time += 2 * base_time
else:
total_time += 3 * base_time
return total_time
