Quantum Circuit Cutting Using Recurrent Pattern Recognition

The patent applies preliminary action by using a recurrent neural network model to predict optimal cutting points in quantum circuits before the actual cutting operation is performed. The model analyzes the quantum circuit graph structure and pre-identifies which edges should be cut, thereby preparing the circuit division strategy in advance. This reduces the complexity of the cutting operation by providing a guided approach rather than exhaustive search, and eliminates the need for knitting operations since the cutting decisions are optimized beforehand to ensure executability on target hardware with limited qubits.

The patent applies segmentation by dividing large quantum circuits into smaller subcircuits based on predicted optimal cutting points identified by the recurrent neural network model. This segmentation allows the quantum circuit to be executed on hardware with fewer qubits by breaking down the computational task into manageable pieces that can be processed sequentially or in parallel. The segmentation strategy is optimized to minimize the overhead of cutting operations while maintaining the overall computational capability of the original circuit, thereby reducing the exponential resource requirements in simulated quantum systems.

The patent applies parameter changes by dynamically adjusting the cutting strategy based on the target hardware's qubit capacity and accuracy characteristics. The recurrent neural network model predicts optimal cutting points that are tailored to specific hardware constraints, allowing the same quantum circuit to be adapted for execution on different hardware platforms with varying qubit counts and accuracy levels. This parameter-based adaptation enables the system to optimize for accuracy on smaller, more reliable quantum hardware rather than forcing execution on larger, less accurate systems.