CTNF 18/237,331 CTNF 94181 DETAILED ACTION This action is in response to the Applicant Response filed 23 August 2023 for application 18/237,331 filed 23 August 2023. Claim(s) 1-20 is/are pending. Claim(s) 1-5, 7-20 is/are rejected. Claim(s) 6 is/are objected to. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-04-01 AIA 07-04 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. Claim(s) 13, 16-18 is/are rejected under 35 U.S.C. 101, because the claim(s) is/are directed to an abstract idea, and because the claim elements, whether considered individually or in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. V. CLS Bank International et al. , 573 US 208 (2014). Regarding claim 13 , the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The claim recites a(n) method for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits. The limitation of generating a final prediction from each of the respective predictions from each of the machine learning decoder models in the ensemble of machine learning decoder models , as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea. Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. The claim recites additional element(s) – quantum computer, plurality of data qubits, stabilizer qubits . The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)). The claim recites additional element(s) – one or more machine learning decoder models, ensemble of machine learning decoder models . The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)). The claim recites obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising one or more analog measurements and one or more stabilizer events for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step , which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)). The claim recites processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models to generate a prediction of whether an error occurred in the computation which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)). The claim recites wherein each updating time step corresponds to one or more of the time steps and wherein the respective input for each of the plurality of updating time steps is generated from the error correction data for the corresponding one or more time steps; wherein the one or more machine learning decoder models are part of an ensemble of machine learning decoder models that each generate a respective prediction of whether an error occurred in the computation which is simply additional information regarding the timesteps, the data and the model, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of: quantum computer, plurality of data qubits, stabilizer qubits amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)) applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f)) acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d)) one or more machine learning decoder models, ensemble of machine learning decoder models amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)) additional information regarding the timesteps, the data and the model do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)) The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible. Regarding claim 16 , the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 16 is directed to a method, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The claim recites a(n) method for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits. The limitation of determining the probabilistic output satisfies a threshold probabilistic output , as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper. The limitation of restarting the routine at a first computation in the sequence of computations , as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea. Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. The claim recites additional element(s) – quantum computer, plurality of data qubits, stabilizer qubits . The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)). The claim recites additional element(s) – one or more machine learning decoder models . The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)). The claim recites obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising one or more analog measurements and one or more stabilizer events for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step , which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)). The claim recites processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models to generate a prediction of whether an error occurred in the computation which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)). The claim recites wherein each updating time step corresponds to one or more of the time steps and wherein the respective input for each of the plurality of updating time steps is generated from the error correction data for the corresponding one or more time steps; wherein the computation is part of a routine of a sequence of computations; wherein the prediction of whether an error occurred in the computation is a probabilistic output which is simply additional information regarding the timesteps, the data and the computations, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of: quantum computer, plurality of data qubits, stabilizer qubits amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)) applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f)) acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d)) one or more machine learning decoder models amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)) additional information regarding the timesteps, the data and the computations do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)) The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible. Regarding claim 17 , the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 17 is directed to a method, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The claim recites a(n) method for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits. The limitation of identifying a prediction of the respective predictions for the sets of data qubits with the highest confidence , as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper. The limitation of performing a following computation in the sequence of computations using the set of data qubits that correspond to the identified prediction , as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea. Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. The claim recites additional element(s) – quantum computer, plurality of data qubits, stabilizer qubits, plurality of sets of data qubits . The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)). The claim recites additional element(s) – one or more machine learning decoder models . The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)). The claim recites obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising one or more analog measurements and one or more stabilizer events for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step; wherein obtaining error correction data for each of a plurality of time steps during the computation comprises obtaining error correction data for each set of data qubits , which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)). The claim recites processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models to generate a prediction of whether an error occurred in the computation; wherein processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models comprises processing the respective input for each of the plurality of updating time steps for each set of data qubits using one or more machine learning decoder models corresponding to the set of data qubits to generate a respective prediction of whether an error occurred in the computation for the set of data qubits which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)). The claim recites wherein each updating time step corresponds to one or more of the time steps and wherein the respective input for each of the plurality of updating time steps is generated from the error correction data for the corresponding one or more time steps; wherein the computation is part of a routine of a sequence of computations; wherein the prediction of whether an error occurred in the computation is a probabilistic output; wherein the plurality of data qubits is one set of a plurality of sets of data qubits which is simply additional information regarding the timesteps, the data, the computations and the qubits, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of: quantum computer, plurality of data qubits, stabilizer qubits, plurality of sets of data qubits amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)) applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f)) acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d)) one or more machine learning decoder models amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)) additional information regarding the timesteps, the data, the computations and the qubits do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)) The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible. Regarding claim 18 , the claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 Analysis: Claim 18 is directed to a method, which is directed to a process, one of the statutory categories. Step 2A Prong One Analysis: The claim recites a(n) method for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits. The limitation of identifying a set of data qubits for which the corresponding prediction has a highest confidence , as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process. The limitation is directed to observation, evaluation, judgment and opinion and is a process capable of being performed by a human mentally or using pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the "Mental Processes" grouping. Accordingly, the claim recites an abstract idea. Step 2A Prong Two Analysis: With respect to the abstract idea, the judicial exception is not integrated into a practical application. The claim recites additional element(s) – quantum computer, plurality of data qubits, stabilizer qubits, plurality of sets of data qubits . The additional element(s) is/are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of executing instructions on the computers) such that it amounts to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)). The claim recites additional element(s) – one or more machine learning decoder models . The additional element(s) is/are recited at a high-level of generality such that it amounts to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)). The claim recites obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising one or more analog measurements and one or more stabilizer events for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step; wherein obtaining error correction data for each of a plurality of time steps during the computation comprises obtaining error correction data for each set of data qubits , which is simply acquiring data recited at a high level of generality. This is nothing more than insignificant extra-solution activity (MPEP 2106.05(g)). The claim recites processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models to generate a prediction of whether an error occurred in the computation; wherein processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models comprises processing the respective input for each of the plurality of updating time steps for each set of data qubits using one or more machine learning decoder models corresponding to the set of data qubits to generate a respective prediction of whether an error occurred in the computation for the set of data qubits which is simply applying a model recited at a high level of generality and amounts to the recitation of the words “apply it” (or an equivalent) or amounts to no more than mere instructions to implement an abstract idea or other exception on a computer (MPEP 2106.05(f)). The claim recites wherein each updating time step corresponds to one or more of the time steps and wherein the respective input for each of the plurality of updating time steps is generated from the error correction data for the corresponding one or more time steps; wherein the plurality of data qubits is one set of a plurality of sets of data qubits; wherein each respective prediction is a probabilistic output which is simply additional information regarding the timesteps, the data and the qubits, and the element(s) do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)). Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because the additional element(s) do(es) not impose any meaningful limits on practicing the abstract idea, and, therefore, the claim is directed to an abstract idea. Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element(s) of: quantum computer, plurality of data qubits, stabilizer qubits, plurality of sets of data qubits amount(s) to no more than mere instructions to apply the exception using generic computer components (MPEP 2106.05(b)) applying a model amount(s) to no more than mere instructions to apply the exception (MPEP 2106.05(f)) acquiring data amount(s) to no more than insignificant extra-solution activity (MPEP 2106.05(g)), wherein the insignificant extra-solution activity is the well-understood routine and conventional activit(y/ies) of receiving or transmitting data over a network and/or storing and retrieving information in memory (MPEP 2016.05(d)) one or more machine learning decoder models amount(s) to no more than indicating a field of use or technological environment in which to apply the judicial exception (MPEP 2106.05(h)) additional information regarding the timesteps, the data and the qubits do(es) not apply the exception in a meaningful way (MPEP 2106.05(e)) The additional element(s) do(es) not provide an inventive concept, and, therefore, the claim is not patent eligible. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim (s) 1, 9, 11, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya et al. (Suppressing Quantum Errors by Scaling a Surface Code Logical Qubit, hereinafter referred to as “Acharya”) in view of Battistel et al. (Hardware-Efficient Leakage Reduction Scheme for Quantum Error Correction with Superconducting Transmon Qubits, hereinafter referred to as “Battistel”) . Regarding claim 1 , Acharya teaches a method for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits (Acharya, section II – teaches error detection of a quantum computer surface code) , the method comprising: obtaining error correction data for each of a plurality of time steps during the computation (Acharya, section II – teaches collecting error correction data at each cycle [time step] during the computation) , the error correction data for each time step comprising one or more analog measurements (Acharya, section V – teaches incorporating more fine-grained measurement data; Acharya, sections IV-V, Figs. 2, 4 - teaches leakage data; Acharya, section XI.F. - teaches leakage and crosstalk; see also Acharya, section XIV-XVII) and one or more stabilizer events for each of a plurality of stabilizer qubits (Acharya, section II – teaches collecting parities [stabilizer events] using stabilizer qubits) that each correspond to a respective subset of the data qubits for the time step (Acharya, section II – teaches that each stabilizer qubit interacts with neighboring data qubits) ; and processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models to generate a prediction of whether an error occurred in the computation, wherein each updating time step corresponds to one or more of the time steps and wherein the respective input for each of the plurality of updating time steps is generated from the error correction data for the corresponding one or more time steps (Acharya, sections II-V - teaches processing the error correction data for each cycle to determine if an error occurred using machine learning). While Acharya teaches acquiring more measurement data beyond stabilizer events and teaches leakage data, Acharya does not explicitly teach obtaining analog measurements at each time step. Battistel teaches obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising one or more analog measurements and one or more stabilizer events for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step (Battistel, section II – teaches obtaining parity checks for each cycle from ancilla qubits and analog measurements of leakage from ancilla qubits). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya with the teachings of Battistel in order to mitigate leakage in the field of quantum error correction (Battistel, Abstract – “Leakage outside of the qubit computational subspace poses a threatening challenge to quantum error correction (QEC). We propose a scheme using two leakage-reduction units (LRUs) that mitigate these issues for a transmon-based surface code, without requiring an overhead in terms of hardware or QEC cycle time as in previous proposals. For data qubits, we consider a microwave drive to transfer leakage to the readout resonator, where it quickly decays, ensuring that this negligibly disturbs the computational states for realistic system parameters. For ancilla qubits, we apply a |1> ↔ |2> π pulse conditioned on the measurement outcome. Using density-matrix simulations of the distance-3 surface code, we show that the average leakage lifetime is reduced to almost one QEC cycle, even when the LRUs are implemented with limited fidelity. Furthermore, we show that this leads to a significant reduction of the logical error rate. This LRU scheme opens the prospect for near-term scalable QEC demonstrations.”). Regarding claim 9 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. Battistel further teaches wherein the one or more analog measurements comprise leakage data characterizing leakage of the corresponding subset of data qubits at the time step (Battistel, section II – teaches obtaining parity checks for each cycle from ancilla qubits and analog measurements of leakage from ancilla qubits). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Acharya and Battistel in order to measure leakage to mitigate leakage (Battistel, Abstract). Regarding claim 11 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. Battistel further teaches wherein the error correction data comprises a time series of analog measurements of the corresponding subset of data qubits for a period of time ending at the time step (Battistel, section II – teaches obtaining analog measurements of leakage from ancilla qubits at the end of the time step). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Acharya and Battistel in order to measure leakage to mitigate leakage (Battistel, Abstract). Regarding claim 19 , Acharya teaches a system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations Acharya, section XIV.B.2 - teaches performing simulations on classical computers (Acharya, section XIV.B.2 - teaches performing simulations on classical computers) comprising: obtaining error correction data for each of a plurality of time steps during a computation performed by a quantum computer (Acharya, section II – teaches collecting error correction data of a quantum computer surface code at each cycle [time step] during the computation) comprising a plurality of data qubits (Acharya, section II – teaches a plurality of data qubits) , the error correction data for each time step comprising one or more analog measurements (Acharya, section V – teaches incorporating more fine-grained measurement data; Acharya, sections IV-V, Figs. 2, 4 - teaches leakage data; Acharya, section XI.F. - teaches leakage and crosstalk; see also Acharya, section XIV-XVII) and one or more stabilizer events for each of a plurality of stabilizer qubits (Acharya, section II – teaches collecting parities [stabilizer events] using stabilizer qubits) that each correspond to a respective subset of the data qubits for the time step (Acharya, section II – teaches that each stabilizer qubit interacts with neighboring data qubits) ; and processing a respective input for each of a plurality of updating time steps using one or more machine learning decoder models to generate a prediction of whether an error occurred in the computation, wherein each updating time step corresponds to one or more of the time steps and wherein the respective input for each of the plurality of updating time steps is generated from the error correction data for the corresponding one or more time steps (Acharya, sections II-V - teaches processing the error correction data for each cycle to determine if an error occurred using machine learning). While Acharya teaches acquiring more measurement data beyond stabilizer events and teaches leakage data, Acharya does not explicitly teach obtaining analog measurements at each time step. Battistel teaches obtaining error correction data for each of a plurality of time steps during a computation performed by a quantum computer comprising a plurality of data qubits, the error correction data for each time step comprising one or more analog measurements and one or more stabilizer events for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step (Battistel, section II – teaches obtaining parity checks for each cycle from ancilla qubits and analog measurements of leakage from ancilla qubits) . It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya with the teachings of Battistel in order to mitigate leakage in the field of quantum error correction (Battistel, Abstract – “Leakage outside of the qubit computational subspace poses a threatening challenge to quantum error correction (QEC). We propose a scheme using two leakage-reduction units (LRUs) that mitigate these issues for a transmon-based surface code, without requiring an overhead in terms of hardware or QEC cycle time as in previous proposals. For data qubits, we consider a microwave drive to transfer leakage to the readout resonator, where it quickly decays, ensuring that this negligibly disturbs the computational states for realistic system parameters. For ancilla qubits, we apply a |1> ↔ |2> π pulse conditioned on the measurement outcome. Using density-matrix simulations of the distance-3 surface code, we show that the average leakage lifetime is reduced to almost one QEC cycle, even when the LRUs are implemented with limited fidelity. Furthermore, we show that this leads to a significant reduction of the logical error rate. This LRU scheme opens the prospect for near-term scalable QEC demonstrations.”). Regarding claim 20 , it is the computer-readable storage media embodiment of claim 19 with similar limitations to claim 19 and is rejected using the same reasoning found in claim 19. Acharya further teaches one or more non-transitory computer-readable storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations (Acharya, section XIV.B.2 - teaches performing simulations on classical computers) comprising … It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Acharya and Battistel for the same reasons as disclosed in claim 19 above . 07-21-aia AIA Claim (s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya in view of Battistel and further in view of Choukroun et al. (WO 2024/157242 A1 – Decoding Quantum Error Correction Codes Using Transformer Neural Networks, hereinafter referred to as “Choukroun”) . Regarding claim 2 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the one or more machine learning decoder models comprise a Transformer neural network. Choukroun teaches wherein the one or more machine learning decoder models comprise a Transformer neural network (Choukroun, p. 7, line 32-p. 8, line 3 – teaches a transformer neural network decoder). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Choukroun in order to perform efficient and high performance QECC decoding in the field of quantum error correction (Choukroun, p. 8, lines 4-7 – “The transformer neural network based QECC decoders, interchangeably designated QECCT herein after, takes advantage of the state of the art transformer neural network based ECC decoders (ECCT) technology, with some modification adapting it for efficient and high performance QECC decoding.”) . 07-21-aia AIA Claim (s) 3, 5, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya in view of Battistel and further in view of Zheng et al. (US 2021/0391873 A1 – Neural Network-Based Quantum Error Correction Decoding Method and Apparatus and Chip, hereinafter referred to as “Zheng”) . Regarding claim 3 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the one or more machine learning decoder models comprise a recurrent neural network. Zheng teaches wherein the one or more machine learning decoder models comprise a recurrent neural network (Zheng, [0089] – teaches a recurrent neural network decoder). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Zheng in order to improve the speed of the fault tolerant error correction decoding in the field of quantum error correction (Zheng, [0169] – “Base on the foregoing, according to the technical solution provided in the embodiments of this application, real error syndrome information of a quantum circuit is decoded, to obtain a corresponding logic error class and perfect error syndrome information, and then a data qubit in which an error occurs in the quantum circuit and a corresponding error type are determined according to the logic error class and the perfect error syndrome information, so that fault tolerant error correction decoding is performed on the error syndrome information by using a neural network algorithm in a case that the error syndrome information of the quantum circuit is not perfect. Moreover, according to the solution, the fault tolerant error correction decoding is equivalent to a classification problem, so that it is suitable for performing the fault tolerant error correction decoding on error syndrome information by using a high-efficiency neural network classifier, thereby improving the speed of the fault tolerant error correction decoding. If an appropriate neural network classifier is selected, the speed of a decoding algorithm can be greatly accelerated, and a road is paved for implementing the real-time fault-tolerant error correction decoding.”). Regarding claim 5 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the one or more machine learning decoder models comprise a convolutional neural network. Zheng teaches wherein the one or more machine learning decoder models comprise a convolutional neural network (Zheng, [0089] – teaches a convolutional neural network decoder). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Zheng in order to improve the speed of the fault tolerant error correction decoding in the field of quantum error correction (Zheng, [0169] – “Base on the foregoing, according to the technical solution provided in the embodiments of this application, real error syndrome information of a quantum circuit is decoded, to obtain a corresponding logic error class and perfect error syndrome information, and then a data qubit in which an error occurs in the quantum circuit and a corresponding error type are determined according to the logic error class and the perfect error syndrome information, so that fault tolerant error correction decoding is performed on the error syndrome information by using a neural network algorithm in a case that the error syndrome information of the quantum circuit is not perfect. Moreover, according to the solution, the fault tolerant error correction decoding is equivalent to a classification problem, so that it is suitable for performing the fault tolerant error correction decoding on error syndrome information by using a high-efficiency neural network classifier, thereby improving the speed of the fault tolerant error correction decoding. If an appropriate neural network classifier is selected, the speed of a decoding algorithm can be greatly accelerated, and a road is paved for implementing the real-time fault-tolerant error correction decoding.”). Regarding claim 8 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the one or more machine learning decoder models comprise a multilayer perceptron. Zheng teaches wherein the one or more machine learning decoder models comprise a multilayer perceptron (Zheng, [0089] – teaches a fully connected neural network decoder). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Zheng in order to improve the speed of the fault tolerant error correction decoding in the field of quantum error correction (Zheng, [0169] – “Base on the foregoing, according to the technical solution provided in the embodiments of this application, real error syndrome information of a quantum circuit is decoded, to obtain a corresponding logic error class and perfect error syndrome information, and then a data qubit in which an error occurs in the quantum circuit and a corresponding error type are determined according to the logic error class and the perfect error syndrome information, so that fault tolerant error correction decoding is performed on the error syndrome information by using a neural network algorithm in a case that the error syndrome information of the quantum circuit is not perfect. Moreover, according to the solution, the fault tolerant error correction decoding is equivalent to a classification problem, so that it is suitable for performing the fault tolerant error correction decoding on error syndrome information by using a high-efficiency neural network classifier, thereby improving the speed of the fault tolerant error correction decoding. If an appropriate neural network classifier is selected, the speed of a decoding algorithm can be greatly accelerated, and a road is paved for implementing the real-time fault-tolerant error correction decoding.”) . 07-21-aia AIA Claim (s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya in view of Battistel and further in view of Lange et al. (Data-Driven Decoding of Quantum Error Correcting Codes Using Graph Neural Networks, hereinafter referred to as “Lange”) . Regarding claim 4 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the one or more machine learning decoder models comprise a graph network. Lange teaches wherein the one or more machine learning decoder models comprise a graph network (Lange, section III – teaches a GNN decoder). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Lange in order to create an efficient and accurate decoder in the field of quantum error correction (Lange, Abstract – “To leverage the full potential of quantum error-correcting stabilizer codes it is crucial to have an efficient and accurate decoder. Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. In this work, instead, we explore a model-free, data-driven, approach to decoding, using a graph neural network (GNN). The decoding problem is formulated as a graph classification task in which a set of stabilizer measurements is mapped to an annotated detector graph for which the neural network predicts the most likely logical error class. We show that the GNN-based decoder can outperform a matching decoder for circuit level noise on the surface code given only simulated experimental data, even if the matching decoder is given full information of the underlying error model. Although training is computationally demanding, inference is fast and scales approximately linearly with the space-time volume of the code. We also find that we can use large, but more limited, datasets of real experimental data ... for the repetition code, giving decoding accuracies that are on par with minimum weight perfect matching. The results show that a purely data-driven approach to decoding may be a viable future option for practical quantum error correction, which is competitive in terms of speed, accuracy, and versatility.”) . 07-21-aia AIA Claim (s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya in view of Battistel and further in view of Varsamopoulos et al. (Comparing Neural Network Based Decoders for the Surface Code, hereinafter referred to as “Varsamopoulos”) . Regarding claim 7 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the one or more machine learning decoder models comprise a long short-term memory network. Varsamopoulos teaches wherein the one or more machine learning decoder models comprise a long short-term memory network (Varsamopoulos, section II – teaches using a LSTM RNN decoder). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Varsamopoulos in order to enhance the decoding performance and the decoding time in the field of quantum error correction (Varsamopoulos, Abstract – “Matching algorithms can be used for identifying errors in quantum systems, being the most famous the Blossom algorithm. Recent works have shown that small distance quantum error correction codes can be efficiently decoded by employing machine learning techniques based on neural networks (NN). Various NN-based decoders have been proposed to enhance the decoding performance and the decoding time. Their implementation differs in how the decoding is performed, at logical or physical level, as well as in several neural network related parameters. In this work, we implement and compare two NN-based decoders, a low level decoder and a high level decoder, and study how different NN parameters affect their decoding performance and execution time. Crucial parameters such as the size of the training dataset, the structure and the type of the neural network, and the learning rate used during training are discussed. After performing this comparison, we conclude that the high level decoder based on a Recurrent NN shows a better balance between decoding performance and execution time and it is much easier to train. We then test its decoding performance for different code distances, probability datasets and under the depolarizing and circuit error models.”) . 07-21-aia AIA Claim (s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya in view of Battistel and further in view of Higgott et al. (Improved Decoding of Circuit Noise and Fragile Boundaries of Tailored Surface Codes, hereinafter referred to as “Higgott”) . Regarding claim 10 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the error correction data comprises posterior probabilities of a stabilizer measurement given analog measurements of the corresponding subset of data qubits at the time step. Higgott teaches wherein the error correction data comprises posterior probabilities of a stabilizer measurement given analog measurements of the corresponding subset of data qubits at the time step (Higgott, section III.A – teaches using posterior probabilities of noisy stabilizer measurements). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Higgott in order to improve accuracy in the field of quantum error correction (Higgott, Abstract – “Realizing the full potential of quantum computation requires quantum error correction (QEC), with most recent breakthrough demonstrations of QEC using the surface code. QEC codes use multiple noisy physical qubits to encode information in fewer logical qubits, enabling the identification of errors through a decoding process. This process increases the logical fidelity (or accuracy) making the computation more reliable. However, most fast (efficient run-time) decoders neglect important noise characteristics, thereby reducing their accuracy. In this work, we introduce decoders that are both fast and accurate, and can be used with a wide class of QEC codes including the surface code. Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC. Using the surface code threshold as a performance metric, we observe a threshold at 0.94% error probability for our decoders, outperforming the 0.82% threshold for a standard minimum-weight perfect matching decoder. We also test our belief-matching decoders in a theoretical case study of codes tailored to a biased noise model. We find that the decoders lead to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code. Surprisingly, in the well-below-threshold regime, the rectangular surface code becomes more resource efficient than the tailored surface code due to a previously unnoticed phenomenon that we call “fragile boundaries.” Our decoders outperform all other fast decoders in terms of threshold and accuracy, enabling better results in current quantum-error-correction experiments and opening up new areas for theoretical case studies.”) . 07-21-aia AIA Claim (s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya in view of Battistel and further in view of Sheth et al. (Neural Ensemble Decoding for Topological Quantum Error-Correcting Codes, hereinafter referred to as “Sheth”) . Regarding claim 12 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the one or more machine learning decoder models are part of an ensemble of machine learning decoder models that each generate a respective prediction of whether an error occurred in the computation. Sheth teaches wherein the one or more machine learning decoder models are part of an ensemble of machine learning decoder models that each generate a respective prediction of whether an error occurred in the computation (Sheth, section III – teaches an ensemble of individual encoders). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Sheth in order to create a very fast ensemble decoder with high error-correcting capability in the field of quantum error correction (Sheth, Abstract – “Topological quantum error-correcting codes are a promising candidate for building fault-tolerant quantum computers. Decoding topological codes optimally, however, is known to be a computationally hard problem. Various decoders have been proposed that achieve approximately optimal error thresholds. Due to practical constraints, it is not known if there exists an obvious choice for a decoder. In this paper, we introduce a framework which can combine arbitrary decoders for any given code to significantly reduce the logical error rates. We rely on the crucial observation that two different decoding techniques, while possibly having similar logical error rates, can perform differently on the same error syndrome. We classify each error syndrome to the decoder which is more likely to decode it correctly using machine learning techniques. We apply our framework to an ensemble of Minimum-Weight Perfect Matching (MWPM) and Hard-Decision Re-normalization Group (HDRG) decoders for the surface code in the depolarizing noise model. Our simulations show an improvement of 38.4%, 14.6%, and 7.1% over the pseudo-threshold of MWPM in the instance of distance 5, 7, and 9 codes, respectively. Lastly, we discuss the advantages and limitations of our framework and applicability to other error-correcting codes. Our framework can provide a significant boost to error correction by combining the strengths of various decoders. In particular, it may allow for combining very fast decoders with moderate error-correcting capability to create a very fast ensemble decoder with high error-correcting capability.”). Regarding claim 13 , Acharya in view of Battistel and further in view of Sheth teaches all of the limitations of the method of claim 12 as noted above. Sheth further teaches generating a final prediction from each of the respective predictions from each of the machine learning decoder models in the ensemble of machine learning decoder models (Sheth, section III – teaches that all models will generate a prediction, but the prediction from the model most likely to decode the error syndrome is selected). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to combine the teachings of Acharya, Battistel and Sheth in order to use and ensemble decoder to create a very fast ensemble decoder with high error-correcting capability (Sheth, Abstract) . 07-21-aia AIA Claim (s) 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Acharya in view of Battistel and further in view of Ryan-Anderson et al. (Realization of Real-Time Fault-Tolerant Quantum Error Correction, hereinafter referred to as “Ryan-Anderson”) . Regarding claim 14 , Acharya in view of Battistel teaches all of the limitations of the method of claim 1 as noted above. However, Acharya in view of Battistel does not explicitly teach wherein the computation is part of a routine of a sequence of computations. Ryan-Anderson teaches wherein the computation is part of a routine of a sequence of computations (Ryan-Anderson, Introduction section – teaches multiple steps of a computation). It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to modify Acharya in view of Battistel with the teachings of Ryan-Anderson in order to correct errors in real-time for reliable large-scale computations in the field of quantum error correction (Ryan-Anderson, Abstract – “Correcting errors in real time is essential for reliable large-scale quantum computations. Realizing this high-level function requires a system capable of several low-level primitives, including single-qubit and two-qubit operations, mid-circuit measurements of subsets of qubits, real-time processing of measurement outcomes, and the ability to condition subsequent gate operations on those measurements. In this work, we use a ten qubit QCCD trapped-ion quantum computer to encode a single logical qubit using the [[7; 1; 3]] color code ... The logical qubit is initialized into the eigenstates of three mutually unbiased bases using an encoding circuit, and we measure an average logical SPAM error of 1.7(2) x 10^-3, compared to the average physical SPAM error 2.4(8) x 10^-3 of our qubits. We then perform multiple syndrome measurements on the encoded qubit, using a real-time decoder to determine any necessary corrections that are done either as software updates to the Pauli frame or as physically applied gates. Moreover, these procedures are done repeatedly while maintaining coherence, demonstrating a dynamically protected logical qubit memory. Additionally, we demonstrate non-Clifford qubit operations by encoding a T j+iL magic state with an error rate below the threshold required for magic state distillation. Finally, we present system-level simulations that allow us to identify key hardware upgrades that may enable the system to reach the pseudo-threshold.”). Regarding claim 15 , Acharya in view of Battistel and further in view of Ryan-Anderson teaches all of the limitations of the method of claim 14 as noted above. Ryan-Anderson further teaches wherein the prediction of whether an error occurred in the computation is a probabilistic output (Acharya, section III – teaches the error occurrence is determined as a probability). It would have been obvious to one of ordinary skill in the art before the filing data of the claimed invention to combine the teachings of Acharya, Battistel and Ryan-Anderson for the same reasons as disclosed in claim 14 above . Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Regarding Claim 6 , the closest pertinent prior art (Zheng) teaches a convolutional neural network decoder, but does not specifically teach that the CNN is a U-Net. Conclusion Any inquiry concerning this communication or earlier communication from the examiner should be directed to MARSHALL WERNER whose telephone number is (469) 295-9143. The examiner can normally be reached on Monday – Thursday 7:30 AM – 4:30 PM ET. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar, can be reached at (571) 272-7796. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MARSHALL L WERNER/ Primary Examiner, Art Unit 2125 Application/Control Number: 18/237,331 Page 2 Art Unit: 2125 Application/Control Number: 18/237,331 Page 3 Art Unit: 2125 Application/Control Number: 18/237,331 Page 4 Art Unit: 2125 Application/Control Number: 18/237,331 Page 5 Art Unit: 2125 Application/Control Number: 18/237,331 Page 6 Art Unit: 2125 Application/Control Number: 18/237,331 Page 7 Art Unit: 2125 Application/Control Number: 18/237,331 Page 8 Art Unit: 2125 Application/Control Number: 18/237,331 Page 9 Art Unit: 2125 Application/Control Number: 18/237,331 Page 10 Art Unit: 2125 Application/Control Number: 18/237,331 Page 11 Art Unit: 2125 Application/Control Number: 18/237,331 Page 12 Art Unit: 2125 Application/Control Number: 18/237,331 Page 13 Art Unit: 2125 Application/Control Number: 18/237,331 Page 14 Art Unit: 2125 Application/Control Number: 18/237,331 Page 15 Art Unit: 2125 Application/Control Number: 18/237,331 Page 16 Art Unit: 2125 Application/Control Number: 18/237,331 Page 17 Art Unit: 2125 Application/Control Number: 18/237,331 Page 18 Art Unit: 2125 Application/Control Number: 18/237,331 Page 19 Art Unit: 2125 Application/Control Number: 18/237,331 Page 20 Art Unit: 2125 Application/Control Number: 18/237,331 Page 21 Art Unit: 2125 Application/Control Number: 18/237,331 Page 22 Art Unit: 2125 Application/Control Number: 18/237,331 Page 23 Art Unit: 2125 Application/Control Number: 18/237,331 Page 24 Art Unit: 2125 Application/Control Number: 18/237,331 Page 25 Art Unit: 2125 Application/Control Number: 18/237,331 Page 26 Art Unit: 2125 Application/Control Number: 18/237,331 Page 27 Art Unit: 2125 Application/Control Number: 18/237,331 Page 29 Art Unit: 2125 Application/Control Number: 18/237,331 Page 32 Art Unit: 2125
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