ffsshhttiikk What I do
- Apply quantum computing to chemical problems
- Simulate quantum systems on quantum hardware
- Develop quantum algorithms for chemistry
- Calculate strongly correlated systems
- Research quantum machine learning
- Explore quantum advantage in chemistry
When to use me
- When simulating quantum chemistry problems
- When exploring quantum computing applications
- When calculating strongly correlated systems
- When developing quantum algorithms
- When studying quantum advantage
- When modeling molecular quantum dynamics
Key Concepts
Quantum Computing Basics
Qubits: |0⟩, |1⟩, or superposition Quantum Gates: Hadamard, CNOT, Pauli, rotation Quantum Circuits: Sequence of gates
Variational Quantum Eigensolver (VQE)
# Example: VQE framework for quantum chemistry
def vqe(ansatz, hamiltonian, optimizer):
"""
Find ground state energy using quantum computer.
ansatz: Parameterized quantum circuit
hamiltonian: Molecular Hamiltonian
optimizer: Classical optimizer
"""
def objective(params):
# Prepare quantum state
state = ansatz.apply(params)
# Measure expectation value
energy = hamiltonian.expectation(state)
return energy
return optimizer.minimize(objective)
# Ansatz circuits
ansatz_types = {
'UCCSD': 'Unitary coupled cluster',
'Hardware Efficient': 'Hardware-adapted layers',
'ADAPT': 'Iterative ansatz construction'
}
Quantum Chemistry on Quantum Computers
- Hamiltonian mapping: Jordan-Wigner, Bravyi-Kitaev
- State preparation: Adiabatic, variational
- Measurement reduction: Grouping techniques
- Error mitigation: ZS, PEC, TMR
Applications
- Ground state energy
- Excited states
- Molecular dynamics
- Reaction rates
- Excited states
- Catalyst design
Available Frameworks
- Qiskit Nature
- Cirq
- PennyLane
- Amazon Braket
- IBM Quantum