Bridging quantum computing with artificial intelligence for next-generation decision systems.
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3-qubit variational quantum eigen solver for energy state calculations
Quantum computing enables efficient modeling of molecular interactions with 80% faster convergence compared to classical methods.
Portfolio optimization using quantum annealing provides 87% better risk returns in complex market simulations.
Our hybrid quantum-classical models achieve state-of-the-art results on quantum pattern recognition tasks.
from qiskit import QuantumCircuit
def quantum_optimization(ancillary_qubits):
qc = QuantumCircuit()
# Initialize qubits in superposition state
for i in range(ancillary_qubits):
qc.h(i)
# Entangle control qubits
for i in range(ancillary_qubits-1):
qc.cx(i, i+1)
return qc
# Optimization routine
def quantum_solver():
q = quantum_optimization(4)
result = execute(q, 'qasm_simulator').result()
return result.get_counts()
Quantum optimization algorithm for NP-hard combinatorial problems using 4 qubit ancillary states.
Collaborate on groundbreaking quantum machine learning projects and help shape the future of quantum computing
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