# US 12,223,395 B2 · Circuit cutting taking into account transpilation error > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,223,395 B2 - Document ID: US12223395B2 - Status: Granted - Kind: B2 - Application number: 18/345,173 - Filed: 2023-06-30 - Published: 2024-05-16 - Granted: 2025-02-11 - Earliest claimed: 2022-11-11 - Assignees: Dell Products L.P. - Applicants: Dell Products L.P. - Inventors: Miguel Paredes Quiñones; Rômulo Teixeira de Abreu Pinho - Web page: https://patentgrep.com/patent/US12223395B2 ## CPC - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 10/70: Quantum error correction, detection or prevention, e.g. surface codes or magic state distillation - G06N 10/00: Quantum computing, i.e. information processing based on quantum-mechanical phenomena - G06N 10/20: Models of quantum computing, e.g. quantum circuits or universal quantum computers - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract 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. ## Claims ### Claim 1 A method comprising: representing solutions to a cutting problem of cutting a quantum circuit into quantum subcircuits in a tree structure, wherein nodes of the tree structure correspond to solutions of the cutting problem; evaluating a solution associated with a selected node; performing a query on the solution to determine a predicted transpilation error for transpiling the quantum subcircuits represented by the solution; pruning the node from the tree structure when the predicted transpilation error is greater than a threshold transpilation error; and leaving the node in the tree structure when the query passes. ### Claim 2 (depends on 1) The method of claim 1, further comprising receiving a quantum circuit from a client. ### Claim 3 (depends on 1) The method of claim 1, further comprising inputting the solution, which includes a set of quantum subcircuits, into a machine learning model that is configured to predict the transpilation error for each of the quantum subcircuits in the set of quantum subcircuits. ### Claim 4 (depends on 3) The method of claim 3, wherein the predicted transpilation error is compared to a threshold transpilation error, wherein the solution is rejected when the comparison fails, wherein the comparison fails when the predicted transpilation error is greater than the threshold transpilation error. ### Claim 5 (depends on 4) The method of claim 4, further comprising deleting the node associated with the solution and children nodes of the node associated with the solution from the tree structure. ### Claim 6 (depends on 5) The method of claim 5, further comprising extracting features from the set of quantum subcircuits and inputting the features into the machine learning model. ### Claim 7 (depends on 3) The method of claim 3, wherein the query includes an error constraint relating to transpiling the set of quantum subcircuits. ### Claim 8 (depends on 1) The method of claim 1, wherein less than all of the nodes in the tree structure are subject to the query. ### Claim 9 (depends on 1) The method of claim 1, further comprising performing a backward verification on a parent node of the selected node by performing a query on the parent node. ### Claim 10 (depends on 1) The method of claim 1, further comprising, after an operation of pruning the tree is completed, selecting a solution and cutting the quantum circuit according to the solution to generate the quantum subcircuits, wherein finding a solution to the cutting problem is separate and independent of performing a knitting operation after the quantum circuits have been cut and executed at one or more quantum computing systems and wherein pruning is performed separate and independent of other operations to avoid conflicting optimizations. ### Claim 11 A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: representing solutions to a cutting problem of cutting a quantum circuit into quantum subcircuits in a tree structure, wherein nodes of the tree structure correspond to solutions of the cutting problem; evaluating a solution associated with a selected node; performing a query on the solution to determine a predicted transpilation error for transpiling the quantum subcircuits represented by the solution; pruning the node from the tree structure when the predicted transpilation error is greater than a threshold transpilation error; and leaving the node in the tree structure when the query passes. ### Claim 12 (depends on 11) The non-transitory storage medium of claim 11, further comprising receiving a quantum circuit from a client. ### Claim 13 (depends on 11) The non-transitory storage medium of claim 11, further comprising inputting the solution, which includes a set of quantum subcircuits, into a machine learning model that is configured to predict the transpilation error for each of the quantum subcircuits in the set of quantum subcircuits. ### Claim 14 (depends on 13) The non-transitory storage medium of claim 13, wherein the predicted transpilation error is compared to a threshold transpilation error, wherein the solution is rejected when the comparison fails, wherein the comparison fails when the predicted transpilation error is greater than the threshold transpilation error. ### Claim 15 (depends on 14) The non-transitory storage medium of claim 14, further comprising deleting the node associated with the solution and children nodes of the node associated with the solution from the tree structure. ### Claim 16 (depends on 15) The non-transitory storage medium of claim 15, further comprising extracting features from the set of quantum subcircuits and inputting the features into the machine learning model. ### Claim 17 (depends on 13) The non-transitory storage medium of claim 13, wherein the query includes an error constraint relating to transpiling the set of quantum subcircuits. ### Claim 18 (depends on 11) The non-transitory storage medium of claim 11, wherein less than all of the nodes in the tree structure are subject to the query. ### Claim 19 (depends on 11) The non-transitory storage medium of claim 11, further comprising performing a backward verification on a parent node of the selected node by performing a query on the parent node. ### Claim 20 (depends on 11) The non-transitory storage medium of claim 11, further comprising, after an operation of pruning the tree is completed, selecting a solution and cutting the quantum circuit according to the solution to generate the quantum subcircuits, wherein finding a solution to the cutting problem is separate and independent of performing a knitting operation after the quantum circuits have been cut and executed at one or more quantum computing systems and wherein pruning is performed separate and independent of other operations to avoid conflicting optimizations.