Quantum Circuit Cutting with Transpilation Error Prediction
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Solution Overview
Problem
Executing large quantum circuits in quantum computing systems is hindered by limitations in qubit count and accuracy, as well as the exponential increase in resources required for simulated quantum systems, making efficient orchestration of quantum workloads challenging.
Innovation Solution
The proposed solution involves cutting quantum circuits into smaller subcircuits while accounting for transpilation errors, using a method that considers the cost of transpilation and employs machine learning models to predict transpilation errors, thereby pruning unsatisfactory solutions from the cutting problem tree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If quantum circuits are executed on real quantum hardware, then quantum computation can be performed, but the number of qubits is limiting and accuracy decreases with larger hardware
Solution Approach 1:
The patent divides large quantum circuits into smaller subcircuits that can be executed on limited quantum hardware. By segmenting the circuit into manageable portions, the system can execute circuits larger than what a single quantum device can accommodate while maintaining accuracy through careful decomposition and reconstruction of quantum states
2Productivity
If quantum circuits are simulated, then execution can be performed, but resources required increase exponentially with complexity
Solution Approach 1:
The patent segments quantum circuits into subcircuits that can be executed on quantum hardware, avoiding the need for full simulation of large circuits. This segmentation approach reduces the exponential resource requirement by leveraging actual quantum hardware capabilities for the segmented portions rather than simulating everything classically
Solution Approach 2:
The patent introduces a classical computer as an intermediary to manage circuit decomposition, coordination, and result aggregation. This intermediary handles the complex coordination between quantum hardware executions, reducing the computational burden that would otherwise require exponential classical resources
3Use of energy by moving object
If circuit cutting is performed to execute large circuits, then resource consumption decreases, but transpilation errors increase
Solution Approach 1:
The patent implements feedback mechanisms where the system monitors and evaluates transpilation errors during circuit cutting. By feeding back error information, the system can adjust the cutting strategy, select optimal cut points, and modify transpilation parameters to minimize error propagation while maintaining resource efficiency
Solution Approach 2:
The patent dynamically adjusts transpilation parameters such as error thresholds, optimization levels, and cutting strategies based on the specific quantum circuit and hardware characteristics. By changing these parameters adaptively, the system optimizes the trade-off between resource consumption and transpilation accuracy for each specific execution context
4Adaptability or versatility
If cutting operation is performed to divide quantum circuits, then execution feasibility increases, but time consumption increases due to knitting operation
Solution Approach 1:
The patent performs preliminary circuit analysis and decomposition strategies before execution to optimize the cutting plan. By pre-calculating optimal cut points and preparing subcircuit execution schedules in advance, the system reduces the time required during actual execution and minimizes the knitting operation overhead
Data Source
AI summary
Cutting quantum circuits is disclosed. Solutions to a cutting problem of cutting a quantum circuit into quantum subcircuits are represented in a tree structure. Selected nodes are queried using a machine learning model to generate predicted transpilation metrics such as estimated transpilation error. If the prediction associated with a node fails such that the predicted transpilation error in a simulated quantum computing system is greater than an error threshold or constraint, the corresponding solutions represented by the node and the node's children are pruned from the tree structure.