| name | quantum-pave-chemistry |
| version | v1.0.0 |
| last_updated | 2026-05-30T00:00:00.000Z |
| description | QuantumPave — hybrid quantum-classical workflow for computing additive binding energies using quantum-centric supercomputing. Demonstrates practical quantum chemistry application on real quantum processors by sampling dominant electronic configurations on a QPU and performing classical diagonalization on HPC resources. |
QuantumPave: Quantum Chemistry on Quantum Processors
Description
QuantumPave is a hybrid quantum-classical workflow for computing additive binding energies in materials using quantum-centric supercomputing. The approach samples dominant electronic configurations on a quantum processor and leverages classical HPC resources for diagonalization, providing a practical route to correlated electronic-structure calculations on NISQ devices.
arXiv: 2605.27640
Title: Additive binding energies in asphalt on a quantum processor via quantum-selected configuration interaction (QSCI)
Categories: quant-ph, cond-mat
Core Methodology
Quantum-Selected Configuration Interaction (QSCI)
QSCI is a hybrid algorithm that separates the exponential complexity of electronic structure into two parts:
- Quantum Sampling — The quantum processor samples the dominant electronic configurations from a correlated wavefunction
- Classical Diagonalization — Classical HPC resources diagonalize the Hamiltonian in the sampled subspace
This separation enables practical quantum chemistry calculations on noisy intermediate-scale quantum (NISQ) devices by offloading the most computationally intensive part (diagonalization) to classical systems.
Workflow
Step 1: Problem Formulation
- Define the molecular/material system and basis set
- Map the electronic structure problem to a qubit Hamiltonian using standard transformations (Jordan-Wigner, Bravyi-Kitaev)
- Determine the active space for configuration interaction
Step 2: Quantum State Preparation
- Prepare an initial state on the quantum processor
- Apply variational or Trotterized evolution to explore configuration space
- Use error mitigation techniques to improve state fidelity on NISQ hardware
Step 3: Configuration Sampling
- Measure the quantum state to sample dominant electronic configurations
- Each measurement collapses to a specific configuration (Slater determinant)
- Accumulate statistics over many shots to identify high-weight configurations
Step 4: Classical Diagonalization
- Construct the Hamiltonian matrix in the subspace of sampled configurations
- Perform exact diagonalization on classical HPC resources
- Extract ground state energy and excited state properties
Step 5: Binding Energy Computation
- Compute additive binding energies from the correlated ground state
- Compare with classical benchmarks to assess quantum advantage
- Iterate with refined active spaces or improved state preparation
Key Advantages
NISQ-Compatible
- Shallow circuit depth compared to full quantum phase estimation
- Error mitigation sufficient for useful results on current hardware
- No need for fault-tolerant quantum computing
Hybrid Efficiency
- Quantum part scales polynomially with system size
- Classical part leverages existing HPC infrastructure
- Communication overhead minimized between quantum and classical stages
Practical Applications
- Materials science: Binding energy calculations for complex materials
- Catalysis: Reaction energetics for industrial catalysts
- Energy materials: Battery materials, fuel cells, photovoltaics
- Asphalt chemistry: Additive binding energies for pavement materials (demonstrated application)
Error Mitigation
- Readout error correction: Calibrate measurement errors using known states
- Zero-noise extrapolation: Run circuits at different noise levels and extrapolate
- Symmetry verification: Enforce particle number and spin conservation
- Configuration filtering: Remove low-weight configurations from the sampled subspace
Integration with Quantum-Centric Supercomputing
QuantumPave exemplifies the quantum-centric supercomputing paradigm:
- Quantum processor: Samples configurations (exponential space exploration)
- Classical HPC: Diagonalization and post-processing (polynomial scaling)
- Tight coupling: Iterative refinement between quantum and classical stages
Activation Keywords
- quantum chemistry
- binding energy calculation
- quantum configuration interaction
- QSCI algorithm
- quantum-centric supercomputing
- materials science quantum
- electronic structure quantum
- quantum processor chemistry
- 量子化学
- 结合能计算
Resources
