aiqbench takeQuantum Computing

VGSQD is a method for reusing quantum samples across molecular geometries, so it can trace energy surfaces and reaction paths without a full circuit run for each configuration. The reported agreement with experiment on beryllium dimer and a hydrogen exchange barrier is promising, but the work runs on IBM hardware at up to 124 qubits, and the authors themselves flag scalability to larger systems as open. Readers should treat it as a methodological step on quantum chemistry benchmarks, not yet evidence of practical advantage over classical methods.

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IBM Quantum Watson Team Simulates Molecular Reactions Using 124 Qubits

Quantum ZeitgeistOctober 31, 20264 min read

Be2’s electronic structure proved more accurately modelled by this new algorithm than by current gold standard calculations, achieving closer agreement with experimental values than CCSD(T). Variable-geometry sample-based quantum diagonalization (VGSQD) extends existing techniques to calculate molecular dynamics, not just static energy levels. Application to a hydrogen exchange reaction yielded barrier heights within 0.

Determining molecular properties like geometry and reaction pathways using quantum computers previously required extensive computational resources; each new configuration demanded a full circuit execution. Variable-geometry sample-based quantum diagonalization (VGSQD) reuses data from initial calculations across different molecule shapes, sharply reducing processing demands while maintaining accuracy, demonstrated by circuits utilising up to 124 qubits on an ibm-quebec processor. The algorithm efficiently calculates not only molecular energy but also their three-dimensional shape and how they evolve during chemical reactions. Variable-geometry sample-based quantum diagonalization…

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