Sparse Noise Tomography for Qubit Mapping
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Solution Overview
Problem
Existing qubit mapping methods face challenges in efficiently mapping logical qubits onto physical qubits due to limited connectivity and noise susceptibility, leading to suboptimal algorithmic performance and increased error rates.
Innovation Solution
The implementation of sparse noise tomography-based qubit mapping, which employs a learning component to build a sparse noise model of the quantum computing device and a selection component to remove selected qubits based on this model, thereby optimizing qubit placement and reducing noise effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sparse noise tomography is employed to learn noise and build a sparse noise model, then noise characterization accuracy is improved, but computational complexity and measurement overhead increase
Solution Approach 1:
The patent segments the full noise tomography process into sparse sampling measurements followed by classical post-processing. By dividing the quantum measurements from the computational reconstruction, the system achieves accurate noise characterization while managing computational complexity through efficient classical algorithms that process the segmented measurement data.
Solution Approach 2:
The patent applies partial action by performing sparse sampling rather than complete tomography. Instead of measuring all possible quantum states, the system performs a reduced set of measurements that capture the essential noise characteristics, achieving sufficient accuracy with less computational overhead than full tomography would require.
2Reliability
If qubits are removed from the quantum computing device based on the sparse noise model, then circuit execution quality is improved, but the number of available qubits decreases
Solution Approach 1:
The patent applies local quality by selectively removing only those qubits that exhibit high noise characteristics according to the sparse noise model. Instead of uniformly reducing the qubit array, the system identifies and removes specific problematic qubits while preserving the majority of functional qubits, thus maintaining circuit execution quality with minimal impact on the total number of available qubits.
3Productivity
If sparse noise tomography is used to optimize qubit mapping, then algorithmic performance is enhanced, but measurement and processing time increases
Solution Approach 1:
The patent applies preliminary action by performing sparse noise tomography and building the noise model before actual quantum circuit execution. This advance characterization of device noise allows the system to optimize qubit mapping in advance, so that when circuits are executed, the optimal mapping is already established, reducing the time penalty to a one-time preprocessing cost rather than a recurring overhead.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to sparse noise tomography-based qubit mapping. The computer-implemented system can comprise a memory that can store computer executable components. The computer-implemented system can further comprise a processor that can execute the computer executable components stored in the memory, wherein the computer executable components can comprise a learning component that can employ sparse tomography to learn noise of a quantum computing device to build a sparse noise model of the quantum computing device, and a selection component that can select, based on the sparse noise model and a quantum circuit, nodes and edges of a graph topology of the quantum computing device by removing selected qubits. Furthermore, sets of embedding layouts of the quantum circuit on remaining graph fragments can be scored based on the sparse noise model to select an optimal virtual-to-physical qubit mapping.