Researchers have developed a novel first-quantization method for quantum computing that achieves significant improvements in efficiency for molecular energy calculations across arbitrary basis sets, demonstrating both asymptotic speedup in molecular orbital calculations and orders-of-magnitude resource reductions when using dual plane waves compared to second-quantization approaches.
This research demonstrates a Contextual Subspace Variational Quantum Eigensolver on superconducting hardware that successfully models N₂ molecular bond-breaking with superior accuracy to single-reference wavefunction techniques, achieved through comprehensive error mitigation strategies and circuit optimization while requiring fewer quantum resources than comparable classical approaches.
A research team has developed an innovative hybrid quantum-classical computing approach that combines quantum optimization, machine learning, and classical computations to efficiently screen thousands of diarylethene derivatives, successfully identifying five promising photochromic compounds with optimal properties for light-controlled drug delivery applications.
Researchers have successfully extended the quantum phase difference estimation algorithm, a general quantum algorithm for the direct calculations of energy gaps, to enable the direct calculation of energy differences between two different molecular geometries. This allows for the computation, based on the finite difference method, of energy derivatives with respect to nuclear coordinates in a single calculation.
Quantum computers promise significantly shorter computing times for complex problems. But there are still only a few quantum computers worldwide with a limited number of so-called qubits. However, quantum computer algorithms can already run on conventional servers that simulate a quantum computer. A team has succeeded in calculating the electron orbitals and their dynamic development using an example of a small molecule after a laser pulse excitation.
The quantum simulation of quantum chemistry shows promise with a novel two-step low-rank factorization method that substantially reduces gate complexity of Hamiltonian and unitary Coupled Cluster Trotter steps, enabling practical implementation on near-term quantum devices with linear connectivity, as demonstrated by a 50-qubit molecular simulation requiring only 4,000 layers of two-qubit gates.
Some interesting article in Medium written by an interdisciplinary team of IBM researchers with partners from Daimler AG and Virginia Tech.
Japanese startup QunaSys announces the launch of private beta release of QunaSys Qamuy for quantum chemistry calculations.
Quantum Chemistry Breakthrough: DeepMind Uses Neural Networks to Tackle Schrödinger Equation.