Strong empiricalVQE ansatz benchmark
RY-CX hardware-efficient ansatz · 3 qubits
A reproducible hardware-efficient VQE layer with parameterized RY rotations and a linear CNOT entangler.
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A reproducible hardware-efficient VQE layer with parameterized RY rotations and a linear CNOT entangler.
A two-axis variational layer followed by a nearest-neighbor CZ entangling pattern.
A Hartree–Fock-like computational-basis seed followed by tunable rotations and a CNOT chain.
A problem-inspired layer combining ZZ interactions and transverse X rotations for a ring Ising model.
Determine the ground state of weakly-interacting, non-covalently bonded molecules — the weakly-bound intermolecular regime that variational quantum algorithms applied to strongly-bound, covalently-bonded systems with full molecular-orbital bases had left largely unexplored — using a coarse-grained representation of the electronic response suited to a VQA.
Find quantum circuits that diagonalize a given input Hamiltonian, that is, approximate its eigenstates, without resorting to brute-force optimization of an unstructured variational circuit, which runs into barren plateaus.
The minimal-basis (STO-3G) H₂ electronic Hamiltonian after Jordan-Wigner/parity mapping and two-qubit tapering: the canonical small-molecule target for variational quantum eigensolver (VQE) demonstrations.
Simulate strongly correlated chemical systems on near-term quantum hardware, whose noise and limited size otherwise confine such simulations to small chemical systems, by embedding a quantum treatment of a strongly correlated fragment within a larger classical calculation.
Predict the three-dimensional structure a protein takes from its primary sequence of amino acids, posed here on the model Hamiltonian the paper defines for a chain of N monomers placed on a tetrahedral lattice.
A quantum algorithm that produces approximate solutions for combinatorial optimization problems, tunable by a positive integer p.
A ground-state preparation method that projects non-unitary imaginary-time evolution onto a parameterized quantum circuit.
Given a target position for a robot manipulator's end effector, find joint angles that reach it — the inverse kinematics problem, which has no analytical solution for a general 6-degree-of-freedom arm and admits many joint configurations at once for a redundant one.
Frozen-core and active-orbital choices define the Hamiltonian size before variational optimization.
An adaptive ansatz grows one operator at a time using measured energy gradients from a predefined pool.
Several high-gradient operators are appended per adaptive iteration to reduce optimization and measurement rounds.
Randomized measurements are reused to estimate many observables from a shared data set.
Conditional value-at-risk averages only a selected low-energy tail of samples for combinatorial objectives.
Minimizing the squared shifted Hamiltonian targets eigenstates near a chosen energy shift.
Generalized singles and doubles relax occupied-to-virtual restrictions to enlarge the variational manifold.
Parameter-shift or analytic derivative measurements supply gradients to a classical optimizer.
A hybrid chemistry workflow that compares a quantum expectation loop with classical eigensolvers.
Alternating native one-qubit rotations and entanglers reduce compilation overhead but can change trainability.
McLachlan-style projected imaginary-time dynamics update parameters toward low-energy states.
Iterative QCC repeatedly dresses the Hamiltonian and selects new entanglers instead of fixing one deep circuit.
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