Algorithms
Ground state, excited states, stochastic search, periodic systems — every algorithm here inherits from the same VariationalDriver and reaches the user through the same QuantumCalculator. The basis and the integrals that feed them are on the Basis Sets page.
VQE optimizes a fixed UCCSD ansatz; ADAPT-VQE grows the ansatz operator by operator, screening the gradient of a pool — fermionic, qubit, qubit-excitation-based (QEB) or compact-excitation (CEO) — and adding only the one that lowers the energy most.
DeflationMixin runs variational deflation (VQD): each new state is optimized orthogonal to the ones already found. SubspaceMixin runs SSVQE instead, optimizing a weighted set of orthogonal states together. Both plug into any driver without duplicating its setup.
The Variational Adaptive Stochastic Quantum Eigensolver samples its next operator from a softmax over the pool's gradients rather than always taking the steepest one, with a temperature schedule that anneals from exploration to the greedy ADAPT-VQE limit.
The same drivers run on a periodic basis through BlochCalculator, producing Bloch band structure and Born–von Kármán total energies for a crystal, not just an isolated molecule.
Cutting the qubit count, tracking how much of Hilbert space an ansatz actually reaches, and getting a result that survives a restart.
Drop the chemically inert core orbitals from the qubit count before mapping, without touching the basis or the integral engine.
Restricted and unrestricted Hartree–Fock references, including spin-polarized systems like triplet O₂.
Above twelve qubits, ADAPT-VQE switches to an automatically sparsified operator pool rather than the full one, keeping gradient screening tractable.
KL-divergence against the Haar-random distribution measures how much of Hilbert space an ansatz can actually reach, tracked as ADAPT-VQE grows it.
Parameters are periodically re-optimized from scratch mid-run, escaping local minima that gradient descent alone gets stuck in.
A built qubit Hamiltonian serializes to Parquet or JSON with automatic format detection — replay a solver against it without recomputing integrals or geometry.
One QuantumCalculator surface — the choice of backend, basis and mapping composes freely with the choice of algorithm above.
Every solver reaches the user through one entry point — QuantumCalculator(method=...) — an ASE calculator, so a run looks the same whether it optimizes a UCCSD ansatz on a state vector or screens ADAPT-VQE gradients against a real QPU.
Localized orbitals and pseudopotentials generated from scratch, and the C-accelerated integral engine underneath both.
Cross-backend validation to 1.3×10⁻⁷ Ha, real QPU execution through Amazon Braket, and what still doesn't work.