Notice of Pre-AIA or AIA Status
This Non-Final communication is in response to application no. 18/497,929 filed on 10/30/2023. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
101 Subject Matter Eligibility analysis
Step 1: Claims 1-20 are within the four statutory categories (a process, machine, manufacture or
composition of matter). Claims 1-10 describe a process and claims 11-20 describe a machine.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
selecting a plurality of data subsets from a data set; (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
generating a solution for the plurality of data subsets (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
comparing the solution to a threshold solution; (This is an abstract idea of a "Mental Process." The "comparing" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The comparison could be made manually by an individual.)
Step 2a Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
training, using a quantum computer over a plurality of iterations, a plurality of parameters of a quantum computing system model using the plurality of data subsets and a quantum circuit depth; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
using the quantum computing system model and the quantum computer; (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
in response to the solution of the quantum computing system model not satisfying the threshold solution, adjusting the quantum circuit depth; and (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
retraining, using the quantum computer, the plurality of parameters of the quantum computing system model using the plurality of data subsets and adjusted quantum circuit depth. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception.
The additional elements are recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 1 is ineligible.
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, recites an additional abstract idea:
selecting a plurality of second data subsets, wherein each of the plurality of second data subsets is a larger size than each of the plurality of data subsets; and (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
training, using the quantum computer over the plurality of iterations, the plurality of parameters of the quantum computing system model using the plurality of second data subsets and the quantum circuit depth. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 2, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the plurality of second data subsets are selected in response to a size of one of the plurality of data subsets being less than a size of the data set and all available qubits of the quantum computer not being utilized to train the plurality of parameters of the quantum computing system model. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 3, recites an additional abstract idea:
generating a solution to a second data set (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
using the quantum computing system model that was trained (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
the size of one of the plurality of data subsets being equal to the size of the data set; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
all available qubits of the quantum computer being utilized to train the plurality of parameters of the quantum computing system model (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element “using the quantum computing system model that was trained” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional elements “the size of one…” and “all available qubits…”add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 1, recites an additional abstract idea:
the data set includes a plurality of interconnected nodes and selecting the plurality of data subsets from the data set includes: for each of the plurality of data subsets: selecting a node; and (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
applying a search algorithm to select one or more neighboring nodes of the node.(This is an abstract idea of a "Mental Process." The "search algorithm" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The search algorithm could be performed manually by an individual.)
Step 2A Prong 2: claim 5 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 5 does not recite an additional element.
Therefore, claim 5 is ineligible.With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
adjusting the quantum circuit depth comprises placing one or more additional quantum logic gates within a quantum circuit of the quantum computer. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: claim 7, which incorporates the rejection of claim 6, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the quantum circuit comprises a plurality of quantum logic gates and the one or more additional quantum logic gates are placed between a first quantum logic gate and a second quantum logic gate of the plurality of quantum logic gates. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 7 is ineligible.
With respect to claim 8:
Step 2A Prong 1: claim 8, which incorporates the rejection of claim 1, recites an additional abstract idea:
selecting one of the plurality of data subsets based on a weight of each of the plurality of data subsets; (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
training the plurality of parameters using the one of the plurality of data subsets and the quantum circuit depth; and (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
adjusting a weight applied to the one of the plurality of data subsets based on the training performed using the one of the plurality of data subsets. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element “training…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional element “adjusting…” adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 8 is ineligible.
With respect to claim 9:
Step 2A Prong 1: claim 9, which incorporates the rejection of claim 1, recites an additional abstract idea:
adjusting the threshold solution based on the solution of the quantum computing system model. (This is an abstract idea of a "Mental Process." The "adjusting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The adjustment could be made manually by an individual.)
Step 2A Prong 2: claim 9 does not recite any additional elements and thus cannot be integrated into a practical application.
Step 2B: claim 9 does not recite an additional element.
Therefore, claim 9 is ineligible.
With respect to claim 10:
Step 2A Prong 1: claim 10, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
using the quantum computing system model that was trained using the plurality of parameters and the adjusted quantum circuit depth to generate a solution based on a second data set. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 10 is ineligible.
With respect to claim 11:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 11. Therefore, claim 11 is ineligible.
With respect to claim 12:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible.
With respect to claim 13:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible.
With respect to claim 14:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible.
With respect to claim 15:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible.
With respect to claim 16:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible.
With respect to claim 17:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible.
With respect to claim 18:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible.
With respect to claim 19:
The claim recites similar limitations as corresponding to claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 19. Therefore, claim 19 is ineligible.
With respect to claim 20:
The claim recites similar limitations as corresponding to claim 10. Therefore, the same subject matter analysis that was utilized for claim 10, as described above, is equally applicable to claim 20. Therefore, claim 20 is ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1, 6-7, 10-12, 17-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable by Babbush (US 2021/0334691 A1) in view of Flöther (US 2023/0177372 A1).
Regarding claim 1, Babbush teaches:
A method, comprising: ([0004] “In general, one innovative aspect of the subject matter described in this specification can be implemented in a method for approximating a target quantum state”)
training, using a quantum computer over a plurality of iterations, a plurality of parameters of a quantum computing system model using the plurality of data subsets and a quantum circuit depth; ([0064] “The system may then perform a variational algorithm using the variational ansatz wavefunction to determine adjusted values of the one or more circuit parameters that define the adjusted quantum circuit that, when applied to the initial quantum state, approximates the ground state of the quantum system.”)
generating a solution for the plurality of data subsets using the quantum computing system model and the quantum computer; ([0073] “The system generates one or more updated quantum circuits for the iteration using the determined number of T gates for the iteration (step 304). As described above, a fixed number of T gates defines one (or more) sets of discrete rotations that can be implemented exactly, the sets of discrete rotations approximating the quantum circuit. Each updated quantum circuit therefore corresponds to a different assignment of the determined number of T gates within the updated quantum circuit, i.e., a different set of discrete rotation operations that approximates the quantum circuit.” And [0074] “The system determines, for each updated quantum circuit, an energy expectation value of the quantum system for the iteration using the updated quantum circuit (step 306).”)
comparing the solution to a threshold solution; ([0076] “The system determines whether the difference between the lowest determined energy expectation value for the iteration and a lowest determined energy expectation value for the previous iteration exceeds a predetermined threshold”)
in response to the solution of the quantum computing system model not satisfying the threshold solution, adjusting the quantum circuit depth; and ([0077] “In response to determining that the difference exceeds the predetermined threshold, the system performs a subsequent iteration” performing another iteration includes assigning more T gates to the quantum circuit (adjusting the depth)).
retraining, using the quantum computer, the plurality of parameters of the quantum computing system model using the plurality of data subsets and adjusted quantum circuit depth. (([0077] “In response to determining that the difference exceeds the predetermined threshold, the system performs a subsequent iteration” performing another iteration includes retraining the computing system.)
Babbush does not teach:
selecting a plurality of data subsets from a data set;
However, Flöther does:
selecting a plurality of data subsets from a data set; ([0030] “Data selection component 214 can select, for each iteration of a data selection routine, a subset of the compressed and clustered data to be submitted to an available quantum device for processing. As will be discussed in more detail below, the subset of the can be selected such that each of the clusters are represented in the subset.”)
Babbush and Flöther are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther. One would want to do this to not use large amount of data to start.
Regarding claim 6, Babbush in view of Flöther teaches claim 1 as outlined above. Babbush further teaches:
adjusting the quantum circuit depth comprises placing one or more additional quantum logic gates within a quantum circuit of the quantum computer. ([0044] “To adaptively adjust the number of available T gates, the classical processors 104 may define an initial number of T gates, e.g., a predetermined minimum number of T gates that is less than the available number of T gates, and iteratively generate quantum circuits that include an increasing number of T gates. This may include, at each iteration, determining a number of T gates for the iteration and determining a quantum circuit that includes a particular assignment of the determined number of T gates for the iteration, e.g., using quantum circuit design techniques and algorithms.”)
Regarding claim 7, Babbush in view of Flöther teaches claim 6 as outlined above. Babbush further teaches:
the quantum circuit comprises a plurality of quantum logic gates and the one or more additional quantum logic gates are placed between a first quantum logic gate and a second quantum logic gate of the plurality of quantum logic gates. ([0044] “To adaptively adjust the number of available T gates, the classical processors 104 may define an initial number of T gates, e.g., a predetermined minimum number of T gates that is less than the available number of T gates, and iteratively generate quantum circuits that include an increasing number of T gates. This may include, at each iteration, determining a number of T gates for the iteration and determining a quantum circuit that includes a particular assignment of the determined number of T gates for the iteration, e.g., using quantum circuit design techniques and algorithms.”)
Regarding claim 10, Babbush in view of Flöther teaches claim 1 as outlined above. Babbush further teaches:
using the quantum computing system model that was trained using the plurality of parameters and the adjusted quantum circuit depth to generate a solution based on a second data set. ([0034] “The predefined target precision defines sequences of gates necessary to implement the quantum circuit rotation operations at the target precision and produce a target quantum state, e.g., that encodes a solution to a computational task.”)
Regarding claim 11, Babbush in view of Flöther teaches claim 1 as outlined above. Babbush further teaches:
One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations of claim 1. ([0099] “Implementations of the digital and/or quantum subject matter described in this specification can be implemented as one or more digital and/or quantum computer programs, i.e., one or more modules of digital and/or quantum computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus.”)
Regarding claim 12, Babbush teaches:
A system comprising: a quantum computing device configured to train a plurality of parameters of a quantum computing system model over a plurality of iterations using a plurality of data subsets and a quantum circuit depth; and ([0036] “The example system 100 is an example of a system implemented as classical or quantum computer programs on one or more classical computers or quantum computing devices in one or more locations, in which the systems, components, and techniques described below can be implemented.”)
a classical computing device communicatively coupled to the quantum computing device, the classical computing device configured to: ([0036] “The example system 100 is an example of a system implemented as classical or quantum computer programs on one or more classical computers or quantum computing devices in one or more locations, in which the systems, components, and techniques described below can be implemented.”)
generate a solution for the plurality of data subsets using the quantum computing system model and the quantum computing device; ([0073] “The system generates one or more updated quantum circuits for the iteration using the determined number of T gates for the iteration (step 304). As described above, a fixed number of T gates defines one (or more) sets of discrete rotations that can be implemented exactly, the sets of discrete rotations approximating the quantum circuit. Each updated quantum circuit therefore corresponds to a different assignment of the determined number of T gates within the updated quantum circuit, i.e., a different set of discrete rotation operations that approximates the quantum circuit.” And [0074] “The system determines, for each updated quantum circuit, an energy expectation value of the quantum system for the iteration using the updated quantum circuit (step 306).”)
comparing the solution to a threshold solution; ([0076] “The system determines whether the difference between the lowest determined energy expectation value for the iteration and a lowest determined energy expectation value for the previous iteration exceeds a predetermined threshold”)
in response to the solution of the quantum computing system model not satisfying the threshold solution, adjust the quantum circuit depth, wherein after adjusting the quantum circuit depth, the quantum computing device retrains the plurality of parameters of the quantum computing system model using the plurality of data subsets and adjusted quantum circuit depth. ([0077] “In response to determining that the difference exceeds the predetermined threshold, the system performs a subsequent iteration” performing another iteration includes assigning more T gates to the quantum circuit (adjusting the depth). Also performing another iteration includes retraining the computing system).
Babbush does not teach:
select the plurality of data subsets from a data set;
However, Flöther does:
select the plurality of data subsets from a data set; ([0030] “Data selection component 214 can select, for each iteration of a data selection routine, a subset of the compressed and clustered data to be submitted to an available quantum device for processing. As will be discussed in more detail below, the subset of the can be selected such that each of the clusters are represented in the subset.”)
Babbush and Flöther are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther. One would want to do this to not use large amount of data to start.
Regarding claim 17, Babbush in view of Flöther teaches claim 12 as outlined above. Babbush further teaches:
adjusting the quantum circuit depth comprises placing one or more additional quantum logic gates within a quantum circuit of the quantum computing device. ([0044] “To adaptively adjust the number of available T gates, the classical processors 104 may define an initial number of T gates, e.g., a predetermined minimum number of T gates that is less than the available number of T gates, and iteratively generate quantum circuits that include an increasing number of T gates. This may include, at each iteration, determining a number of T gates for the iteration and determining a quantum circuit that includes a particular assignment of the determined number of T gates for the iteration, e.g., using quantum circuit design techniques and algorithms.”)
Regarding claim 18, Babbush in view of Flöther teaches claim 17 as outlined above. Babbush further teaches:
the quantum circuit comprises a plurality of quantum logic gates and the one or more additional quantum logic gates are placed between a first quantum logic gate and a second quantum logic gate of the plurality of quantum logic gates. ([0044] “To adaptively adjust the number of available T gates, the classical processors 104 may define an initial number of T gates, e.g., a predetermined minimum number of T gates that is less than the available number of T gates, and iteratively generate quantum circuits that include an increasing number of T gates. This may include, at each iteration, determining a number of T gates for the iteration and determining a quantum circuit that includes a particular assignment of the determined number of T gates for the iteration, e.g., using quantum circuit design techniques and algorithms.”)
Regarding claim 20, Babbush in view of Flöther teaches claim 12 as outlined above. Babbush further teaches:
the classical computing device is further configured to use the quantum computing system model that was trained using the plurality of parameters and the adjusted quantum circuit depth to generate a solution based on a second data set. ([0034] “The predefined target precision defines sequences of gates necessary to implement the quantum circuit rotation operations at the target precision and produce a target quantum state, e.g., that encodes a solution to a computational task.”)
Claims 2-4 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable by Babbush in view of Flöther and Benedetti (GB 2636551 A).
Regarding claim 2, Babbush in view of Flöther teaches claim 1 as outlined above. Neither of them teach:
selecting a plurality of second data subsets, wherein each of the plurality of second data subsets is a larger size than each of the plurality of data subsets; and
training, using the quantum computer over the plurality of iterations, the plurality of parameters of the quantum computing system model using the plurality of second data subsets and the quantum circuit depth.
However, Benedetti does:
selecting a plurality of second data subsets, wherein each of the plurality of second data subsets is a larger size than each of the plurality of data subsets; and ([0083] “Operation 330 performs a second stage of training the model against data from the target using the full set of operators to obtain optimised values for a larger subset of the set of parameters for the model. The second stage of performing is performed on quantum computer hardware to provide a further trained model. The optimized parameter values saved from the first stage may be used to initialise the corresponding parameters for the second stage of training”)
training, using the quantum computer over the plurality of iterations, the plurality of parameters of the quantum computing system model using the plurality of second data subsets and the quantum circuit depth. ([0083] “Operation 330 performs a second stage of training the model against data from the target using the full set of operators to obtain optimised values for a larger subset of the set of parameters for the model. The second stage of performing is performed on quantum computer hardware to provide a further trained model. The optimized parameter values saved from the first stage may be used to initialise the corresponding parameters for the second stage of training”)
Babbush, Flöther and Benedetti are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther with the second larger subset of Benedetti. One would want to do this to enhance overall performance (Benedetti [0023])
Regarding claim 3, Babbush in view of Flöther and Benedetti teaches claim 2 as outlined above. Flöther further teaches:
the plurality of second data subsets are selected in response to a size of one of the plurality of data subsets being less than a size of the data set and all available qubits of the quantum computer not being utilized to train the plurality of parameters of the quantum computing system model. ([0061] “At 812, a subset of the compressed and clustered data obtained via the first part of the methodology 800a can be selected (e.g., by data selection component 214), where the amount of data selected does not exceed the upper limit for the selected quantum device (as determined at step 804).” The first subset is not all of the data)
Regarding claim 4, Babbush in view of Flöther and Benedetti teaches claim 3 as outlined above. Babbush further teaches:
generating a solution to a second data set using the quantum computing system model that was trained, wherein the generating the solution to the second data set is in response to at least one of: ([0073] “The system generates one or more updated quantum circuits for the iteration using the determined number of T gates for the iteration (step 304). As described above, a fixed number of T gates defines one (or more) sets of discrete rotations that can be implemented exactly, the sets of discrete rotations approximating the quantum circuit. Each updated quantum circuit therefore corresponds to a different assignment of the determined number of T gates within the updated quantum circuit, i.e., a different set of discrete rotation operations that approximates the quantum circuit.” And [0074] “The system determines, for each updated quantum circuit, an energy expectation value of the quantum system for the iteration using the updated quantum circuit (step 306).”)
Benedetti further teaches:
the size of one of the plurality of data subsets being equal to the size of the data set; and all available qubits of the quantum computer being utilized to train the plurality of parameters of the quantum computing system model. ([0084] “The larger subset of the set of parameters for the model may, in some implementations, comprise the full set of parameters for the model. Accordingly, the second phase of training may encompass all the set of parameters for the model.” The full model is being used the second time.)
Regarding claim 13, Babbush in view of Flöther teaches claim 12 as outlined above. Neither of them teach:
the classical computing device is further configured to select a plurality of second data subsets, wherein each of the plurality of second data subsets is a larger size than the plurality of data subsets; and
the quantum computing device is further configured to train, over the plurality of iterations, the plurality of parameters of the quantum computing system model using the plurality of second data subsets and the quantum circuit depth.
However, Benedetti does:
the classical computing device is further configured to select a plurality of second data subsets, wherein each of the plurality of second data subsets is a larger size than the plurality of data subsets; and ([0083] “Operation 330 performs a second stage of training the model against data from the target using the full set of operators to obtain optimised values for a larger subset of the set of parameters for the model. The second stage of performing is performed on quantum computer hardware to provide a further trained model. The optimized parameter values saved from the first stage may be used to initialise the corresponding parameters for the second stage of training”)
the quantum computing device is further configured to train, over the plurality of iterations, the plurality of parameters of the quantum computing system model using the plurality of second data subsets and the quantum circuit depth. ([0083] “Operation 330 performs a second stage of training the model against data from the target using the full set of operators to obtain optimised values for a larger subset of the set of parameters for the model. The second stage of performing is performed on quantum computer hardware to provide a further trained model. The optimized parameter values saved from the first stage may be used to initialise the corresponding parameters for the second stage of training”)
Babbush, Flöther and Benedetti are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther with the second larger subset of Benedetti. One would want to do this to enhance overall performance (Benedetti [0023])
Regarding claim 14, Babbush in view of Flöther and Benedetti teaches claim 13 as outlined above. Flöther further teaches:
the plurality of second data subsets are selected in response to a size of one of the plurality of data subsets being less than a size of the data set and all available qubits of the quantum computing device not being utilized to train the plurality of parameters of the quantum computing system model. ([0061] “At 812, a subset of the compressed and clustered data obtained via the first part of the methodology 800a can be selected (e.g., by data selection component 214), where the amount of data selected does not exceed the upper limit for the selected quantum device (as determined at step 804).” The first subset is not all of the data)
Regarding claim 15, Babbush in view of Flöther and Benedetti teaches claim 14 as outlined above. Babbush further teaches:
the classical computing device is further configured to generate a solution to a second data set, using the quantum computing system model that was trained, in response to at least one of: ([0073] “The system generates one or more updated quantum circuits for the iteration using the determined number of T gates for the iteration (step 304). As described above, a fixed number of T gates defines one (or more) sets of discrete rotations that can be implemented exactly, the sets of discrete rotations approximating the quantum circuit. Each updated quantum circuit therefore corresponds to a different assignment of the determined number of T gates within the updated quantum circuit, i.e., a different set of discrete rotation operations that approximates the quantum circuit.” And [0074] “The system determines, for each updated quantum circuit, an energy expectation value of the quantum system for the iteration using the updated quantum circuit (step 306).”)
Benedetti further teaches:
the size of one of the plurality of data subsets being equal to the size of the data set; and all available qubits of the quantum computing device being utilized to train the plurality of parameters of the quantum computing system model. ([0084] “The larger subset of the set of parameters for the model may, in some implementations, comprise the full set of parameters for the model. Accordingly, the second phase of training may encompass all the set of parameters for the model.” The full model is being used the second time.)
Claims 5, 16 are rejected under 35 U.S.C. 103 as being unpatentable by Babbush in view of Flöther and Ai (NPL ‘Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach (2023)).
Regarding claim 5, Babbush in view of Flöther teaches claim 1 as outlined above. Neither of them teach the limitations of claim 5. However, Ai does:
the data set includes a plurality of interconnected nodes and selecting the plurality of data subsets from the data set includes: (Page 5 top of left column “In this paper, we develop a novel quantum-classical hybrid machine learning algorithm for graph-structured data.”)
for each of the plurality of data subsets: selecting a node; and applying a search algorithm to select one or more neighboring nodes of the node. (page 4 right column section 3.2 “Graph Neural Networks (GNNs) are effective machine learning tools for structured data and rely on a neighborhood aggregation strategy. Specifically, for each node in a graph, the GNN recursively aggregates the current representation with those for its neighbors, thus giving a new representation for use at the next iteration.”)
Babbush, Flöther and Ai are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther with the graph sampling of Ai. One would want to do this to use graph datasets as their input and reduce information loss (Ai conclusion).
Regarding claim 16, Babbush in view of Flöther teaches claim 12 as outlined above. Neither of them teach the limitations of claim 16. However, Ai does:
the data set includes a plurality of interconnected nodes and the classical computing device is configured to select the plurality of data subsets from the data set for each of the plurality of data subsets by: (Page 5 top of left column “In this paper, we develop a novel quantum-classical hybrid machine learning algorithm for graph-structured data.”)
selecting a node; and applying a search algorithm to select one or more neighboring nodes of the node. (page 4 right column section 3.2 “Graph Neural Networks (GNNs) are effective machine learning tools for structured data and rely on a neighborhood aggregation strategy. Specifically, for each node in a graph, the GNN recursively aggregates the current representation with those for its neighbors, thus giving a new representation for use at the next iteration.”)
Babbush, Flöther and Ai are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther with the graph sampling of Ai. One would want to do this to use graph datasets as their input and reduce information loss (Ai conclusion).
Claims 8, 19 are rejected under 35 U.S.C. 103 as being unpatentable by Babbush in view of Flöther and Lubensky (US 2020/0320422 A1).
Regarding claim 8, Babbush in view of Flöther teaches claim 1 as outlined above. Neither of them teach the limitations of claim 8. However Lubensky does:
each of the plurality of data subsets are weighted and the training the plurality of parameters includes: ([0076] “In some embodiments, for example, as illustrated by system 500 described below and depicted in FIG. 5, ensemble component 108 can assign a lower weight value to correctly classified outcomes and/or a higher weight value to miss-classified outcomes.”)
selecting one of the plurality of data subsets based on a weight of each of the plurality of data subsets; ([0086] “In some embodiments, in training a boosted AI model and/or an ensemble AI model, a weak performing AI model (e.g., learner, classifier, etc.) may use the weights or data can be subsampled according to the distribution of weights. In some embodiments, the weight of a component classifier can be≥0 if error≤½, the smaller the error the higher the weight.”)
training the plurality of parameters using the one of the plurality of data subsets and the quantum circuit depth; and ([0086] “In some embodiments, in training a boosted AI model and/or an ensemble AI model, a weak performing AI model (e.g., learner, classifier, etc.) may use the weights or data can be subsampled according to the distribution of weights. In some embodiments, the weight of a component classifier can be≥0 if error≤½, the smaller the error the higher the weight.”)
adjusting a weight applied to the one of the plurality of data subsets based on the training performed using the one of the plurality of data subsets. ([0080] “In some embodiments, algorithm 300 can comprise an adaptive boosting algorithm that can change sample distribution by modifying one or more weights corresponding to training data. For example, algorithm 300 can comprise an adaptive boosting algorithm that, when implemented (e.g., by ensemble component 108, score component 110, etc.), can change sample distribution by modifying one or more weights 502a, 502b, 502n (where n can represent a total quantity of weights) corresponding to training data represented by plus symbols (+) and minus symbols (−) in FIGS. 4 and 5. In some embodiments, ensemble component 108 can employ algorithm 300 to increase the weight of mis-classified data and decrease the weight of correctly classified data (e.g., as illustrated in FIGS. 4 and 5).”)
Babbush, Flöther and Lubensky are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther with the weight boosting of Lubensky. One would want to do this to put importance of incorrectly classified data to further improve accuracy (Lubensky [0080]).
Regarding claim 19, Babbush in view of Flöther teaches claim 12 as outlined above. Neither of them teach the limitations of claim 19. However Lubensky does:
each of the plurality of data subsets are weighted and the training the plurality of parameters includes: ([0076] “In some embodiments, for example, as illustrated by system 500 described below and depicted in FIG. 5, ensemble component 108 can assign a lower weight value to correctly classified outcomes and/or a higher weight value to miss-classified outcomes.”)
selecting one of the plurality of data subsets based on a weight of each of the plurality of data subsets; ([0086] “In some embodiments, in training a boosted AI model and/or an ensemble AI model, a weak performing AI model (e.g., learner, classifier, etc.) may use the weights or data can be subsampled according to the distribution of weights. In some embodiments, the weight of a component classifier can be≥0 if error≤½, the smaller the error the higher the weight.”)
training the plurality of parameters using the one of the plurality of data subsets and the quantum circuit depth; and ([0086] “In some embodiments, in training a boosted AI model and/or an ensemble AI model, a weak performing AI model (e.g., learner, classifier, etc.) may use the weights or data can be subsampled according to the distribution of weights. In some embodiments, the weight of a component classifier can be≥0 if error≤½, the smaller the error the higher the weight.”)
adjusting a weight applied to the one of the plurality of data subsets based on the training performed using the one of the plurality of data subsets. ([0080] “In some embodiments, algorithm 300 can comprise an adaptive boosting algorithm that can change sample distribution by modifying one or more weights corresponding to training data. For example, algorithm 300 can comprise an adaptive boosting algorithm that, when implemented (e.g., by ensemble component 108, score component 110, etc.), can change sample distribution by modifying one or more weights 502a, 502b, 502n (where n can represent a total quantity of weights) corresponding to training data represented by plus symbols (+) and minus symbols (−) in FIGS. 4 and 5. In some embodiments, ensemble component 108 can employ algorithm 300 to increase the weight of mis-classified data and decrease the weight of correctly classified data (e.g., as illustrated in FIGS. 4 and 5).”)
Babbush, Flöther and Lubensky are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther with the weight boosting of Lubensky. One would want to do this to put importance of incorrectly classified data to further improve accuracy (Lubensky [0080]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable by Babbush in view of Flöther and Dridi (US 2024/0169231 A1).
Regarding claim 9, Babbush in view of Flöther teaches claim 1 as outlined above. Neither of them teach the limitations of claim 9. However Dridi does:
adjusting the threshold solution based on the solution of the quantum computing system model. ([0048] “Some embodiments may determine the threshold used to determine whether to update a learning rate parameter based on a computed gradient. Determining the threshold may include determining a geometric threshold based on the computed gradient. For example, some embodiments may implement a version of Armijo's principle to determine a threshold Thresh based on Equation E.7 below, where α may be a proportionality constant, and where λ.sub.i may remain an undetermined step size until a later operation:”)
Babbush, Flöther and Dridi are considered analogous art to the claimed invention because they are in the same field of endeavor being quantum computing. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the training and gate adjusting of Babbush with the data subset usage of Flöther with the threshold adjusting of Dridi. One would want to do this to change the threshold as the data/model changes (Dridi [0048]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL P GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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