Uploaded December 2025 | Updated September 2026, 1 week ago
Mario Motta and Kevin Sung present a two part session on Hamiltonian simulation and new Qiskit tools.
Mario Motta gives an overview of Hamiltonian simulation for chemistry, materials, and high energy physics, showing partner results on binding energies, reaction pathways, and spin models. He focuses on the sample based quantum diagonalization (SQD) algorithm, which uses a quantum processor to sample important electronic configurations and a classical solver to approximate ground states, highlighting how recent SQD improvements are closing the gap to high accuracy classical methods.
Kevin Sung then gives a hands on tutorial using the open source Qiskit add ons. He shows how to build molecular Hamiltonians, construct and optimize LUCJ ansatz circuits for SQD, and run the diagonalize_fermionic_hamiltonian routine. He also introduces AQC Tensor, which uses tensor networks to compress deep trotterized time evolution circuits into much shallower approximate circuits, reducing depth and noise while keeping good fidelity on current quantum hardware.
Mario Motta and Kevin Sung present a two part session on Hamiltonian simulation and new Qiskit tools.
Mario Motta gives an overview of Hamiltonian simulation for chemistry, materials, and high energy physics, showing partner results on binding energies, reaction pathways, and spin models. He focuses on the sample based quantum diagonalization (SQD) algorithm, which uses a quantum processor to sample important electronic configurations and a classical solver to approximate ground states, highlighting how recent SQD improvements are closing the gap to high accuracy classical methods.
Kevin Sung then gives a hands on tutorial using the open source Qiskit add ons. He shows how to build molecular Hamiltonians, construct and optimize LUCJ ansatz circuits for SQD, and run the diagonalize_fermionic_hamiltonian routine. He also introduces AQC Tensor, which uses tensor networks to compress deep trotterized time evolution circuits into much shallower approximate circuits, reducing depth and noise while keeping good fidelity on current quantum hardware.
