Hybrid Quantum Molecular Simulation via Fragment Decomposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current classical computing methods for identifying and predicting the electronic structure and most stable conformers of large molecules are computationally intractable due to exponential resource demands, and quantum computing approaches face challenges with high costs, noise sensitivity, and limited qubit scalability, limiting accurate quantum chemistry calculations.
Innovation Solution
Utilize problem decomposition techniques on hybrid quantum-classical computing systems to decompose molecular systems into smaller fragments, perform quantum mechanical energy and electronic structure calculations on each fragment, and recombine results to achieve accurate computations for complex molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If highly accurate quantum chemistry methods (CC or Full CI) are performed on classical computers, then the electronic structure and stable conformers can be accurately identified, but the computational cost increases exponentially with molecule size
Solution Approach 1:
The patent applies segmentation by dividing large molecules into smaller molecular fragments or subsystems. Each fragment is treated independently with quantum chemistry methods, and the results are recombined to obtain the total molecular energy. This reduces the exponential scaling problem of classical computing while maintaining accuracy through proper treatment of fragment interactions.
Solution Approach 2:
The patent uses quantum computers as an intermediary computational platform between classical preprocessing and postprocessing steps. The quantum computer specifically handles the electronically excited state calculations and energy computations for molecular fragments, leveraging quantum parallelism to achieve exponential speedup for these critical computational tasks.
2Productivity
If quantum computing is used to compute molecular energy and electronic structure, then exponentially less computational resources are needed, but QC resources are expensive, rare, and limited in qubit count
Solution Approach 1:
The patent segments the overall quantum chemistry calculation into smaller subproblems corresponding to individual molecular fragments. Each fragment requires fewer qubits to simulate, making the calculation feasible on current NISQ devices with limited qubit counts. This segmentation allows the use of available quantum resources while maintaining the ability to handle larger molecular systems through systematic decomposition.
Solution Approach 2:
The patent applies partial action by computing quantum mechanical properties for only the most critical molecular fragments or subsystems that require quantum computational resources, while other parts are handled classically. This selective approach optimizes the use of expensive quantum resources by applying them where they provide the most significant computational advantage.
3Adaptability or versatility
If the number of qubits in quantum computers is increased to handle larger molecules, then more complex quantum chemistry problems can be solved, but qubit sensitivity to noise and environmental effects increases
Solution Approach 1:
The patent divides large molecular systems into smaller fragments, each requiring fewer qubits for accurate simulation. This segmentation reduces the total qubit requirement and consequently reduces the cumulative noise and decoherence effects that scale with system size. Each fragment can be simulated with higher reliability on current noisy quantum hardware.
Solution Approach 2:
The patent employs error mitigation techniques and uses quantum error correction codes to cushion against noise and decoherence effects before they accumulate. By preparing error-corrected logical qubits from multiple physical qubits and using error detection schemes, the system maintains reliability even when scaling to larger molecular problems that require more qubits.
Data Source
AI summary
The present disclosure provides methods and systems for using a hybrid architecture of classical and non-classical (e.g., quantum) computing to compute the quantum mechanical energy and/or electronic structure of a chemical system, as well as to identify stable conformations of a chemical system (e.g., a molecule) and/or to perform an ab initio molecular dynamics calculation or simulation on the chemical system.