DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-2, 5, 12, 16, and 18-20 have been amended. Claims 1-20 have been examined.
Response to Arguments
The 4/17/2026 claim amendments have overcome the prior rejections under 35 USC § 101 and 35 USC § 112, which have been withdrawn accordingly.
Applicant’s arguments, see pp. 7-8, filed 4/17/2026, with respect to the rejection(s) of claim(s) 1, 16 and 20 under 35 USC § 103, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of art of record U.S. Patent Application Publication 11017321 by Mishra et al.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, 8, 10-12, 14-15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 20210334689 by Klimov et al. ("Klimov") in view of U.S. Patent Application Publication 20190042973 by Zou et al. ("Zou") and U.S. Patent Application Publication 11017321 by Mishra et al. ("Mishra").
In regard to claim 1, Klimov discloses:
1. A method of operating a quantum computing system, the method comprising: See Klimov, at least Fig. 3, broadly depicting a method.
obtaining … characterization data associated with an operating parameter of a qubit in a quantum computing system, …; Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, …”
implementing an … operation to extract one or more anomaly … from the characterization data, …; and Klimov ¶ 0058, “The system defines a first cost function that maps qubit operation frequency values (e.g., all qubit idling frequencies, as described below) to a cost (e.g., a real number) corresponding to an operating state of the quantum device (step 302). e.g., an operating state that executes an arbitrary quantum algorithm with lower error rates compared to other operating states.”
Klimov does not expressly disclose the following limitations taught by Zou:
time-dependent characterization data … the time-dependent characterization data characterizing one or more properties of the qubit at each of a plurality of times … Zou, ¶ 0085, “After the initial static update, the corrective sequence management circuitry/logic 1000 dynamically updates the spin-echo sequence table 1005 over time, as new errors are associated with the various qubits of the quantum processor 207.”
unsupervised learning to extract anomaly clusters … each anomaly cluster comprising one or more first anomalies at a first time of the plurality of times …, Zou, ¶ 0087, “Thus, in one embodiment, the machine-learning logic/circuit 1008 performs unsupervised learning of new errors as they occur. Unsupervised learning is particularly beneficial for working with a quantum processor 207 because the physical responses of the individual qbits may change over time and may also vary from one quantum processor to another.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s input data in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
Klimov and Zou does not expressly disclose: and one or more second anomalies at a second time of the plurality of times. This is taught by Mishra. See col. 10, lines 3-5, “The categorization engine 124 may group the events 110 (or the operating characteristics that indicate the events 110) into clusters of different categories. … For example, a first cluster may represent “priority events” (also referred to as “meaningful events”) that have a high likelihood of being indicative of a status that is a precursor to a fault (or some other status associated with one or more recommended actions) …” Also col. 16, lines 13-23, “… events identified based on the historical operating data 156, quantities of the events, times of the events, inferences determined based on the events, such as statuses (e.g., fault precursor states) of the equipment asset 150, …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Mishra’s event clustering with the qbit data of Klimov and Zou in order to predict a potential fault event as suggested by Mishra (see col. 10 lines 3-27).
Klimov also discloses:
operating the qubit in the quantum computing system based at least in part on the one or more anomaly clusters. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.” Also see Zou, ¶ 0085, “In one embodiment, the error detection and machine-learning logic/circuit 1008 may continuously analyze results generated by the quantum processor 207 during runtime and specify corrective actions to be taken by the corrective sequence management circuitry/logic 1000, …”
In regard to claim 2, Klimov also discloses:
2. The method of claim 1, further comprising predicting, based on the one or more anomaly clusters, one or more future predicted anomalies. Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, such as … predicted and/or measured relaxation and/or coherence times of the qubits included in the quantum computing device.” Also see Fig. 2 and ¶ 0058, e.g. “optimizer module 210 … computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.” Klimov’s optimizer identifies frequencies that or less/more error-prone which applies to a broad interpretation of prediction. Note that Klimov combines with Zou to teach prediction of anomaly clusters as cited above. See Zou, ¶ 0085, “In one embodiment, the error detection and machine-learning logic/circuit 1008 may continuously analyze results generated by the quantum processor 207 during runtime and specify corrective actions to be taken by the corrective sequence management circuitry/logic 1000, …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s prediction in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
In regard to claim 3, Klimov also discloses:
3. The method of claim 1, wherein the qubit is a frequency tunable qubit, and the operating parameter comprises an operating frequency of the frequency tunable qubit. Klimov, ¶ 0043, “Each qubit may be operated using respective operating frequencies, … The operating frequencies may be chosen before a computation is performed by the quantum computing device.”
In regard to claim 4, Klimov also discloses:
4. The method of claim 1, wherein the characterization data comprises qubit energy relaxation time versus time. Klimov, ¶ 0044, “One proxy for assessing how good a particular operating frequency is for a particular qubit is that qubit's relaxation time (T1) at that frequency. … However, as shown in plot 100, in reality T1 varies sporadically and unpredictably in qubit frequency (and, although not shown in FIG. 1, in time and from qubit to qubit.) due to uncontrollable defects, as shown by the downward spikes 106.”
In regard to claim 5, Klimov, Zou and Mishra also teach:
5. The method of claim 1, wherein each of the one or more anomaly clusters comprises data indicative of a plurality of states, at the plurality of times, of a corresponding two-level-system defect of one or more two-level-system defects of the quantum computing system. Klimov, ¶ 0102, “It is understood that the term “qubit” encompasses all quantum systems that may be suitably approximated as a two-level system in the corresponding context.” Also Zou, ¶ 0071, “an error decoder (within the QEC unit 808) decodes a multi-qubit measurement from the quantum processor 207 to determine whether an error has occurred … In response, the QEC unit 808 generates error syndrome data from which it may identify the errors that have occurred and implement corrective operations.”
In regard to claim 8, Klimov does not expressly disclose:
8. The method of claim 1, wherein the method comprises pre-processing the characterization data prior to implementation of the unsupervised learning operation. This is taught by Mishra, col. 18, lines 4-7, “In some implementations, the monitoring device 102 may perform pre-processing on the operating characteristics data 136 and the events 110 prior to extracting features from the operating characteristics data 136 and the events 110.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Mishra’s pre-processing with Klimov’s input data in order to reduce complexity of feature extraction, reduce the memory footprint associated with the operating characteristics data and events, clean up the operating characteristics data and events, format the operating characteristics data and events, or a combination thereof, as suggested by Mishra (col. 18, lines 7-13).
In regard to claim 10, Klimov and Mishra further teach:
10. The method of claim 8, wherein pre-processing the characterization data comprises extracting data points that exceed a defined threshold. Mishra, col. 18, lines 13-23, “For example, the pre-processing may include … removing an entry from the operating characteristics data 136 that is associated with a variance that fails to satisfy a variance threshold, …”
In regard to claim 11, Klimov also discloses:
11. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises calibrating the qubit based at least in part on the one or more anomalies. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.”
In regard to claim 12, Klimov also discloses:
12. The method of claim 11, wherein calibrating the qubit comprises modifying an operating parameter associated with the qubit. Klimov, ¶ 0052 as cited above, e.g. “adjust qubit operation frequency values …”
In regard to claim 14, Klimov also discloses:
14. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises modifying one or more quantum hardware parameters based at least in part on the one or more anomalies. Klimov, ¶ 0050, “The operating state of the quantum device may be defined as the set of qubit operation frequencies, e.g., idling and interaction frequencies, that are used by the quantum device.” Also ¶ 0052 as cited above, e.g. “adjust qubit operation frequency values …”
In regard to claim 15, Klimov does not expressly disclose:
15. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises modifying one or more environmental parameters based at least in part on the one or more anomalies. However, this is taught by Zou, e.g. ¶ 0092, “As quantum devices continue to mature, there is an emerging need to efficiently organize and orchestrate all elements of the control electronics stack so that the quantum physical chip can be manipulated (electrical controls, microwaves, flux) and measured with acceptable precision, allowing quantum experiments and programs to be conducted in a reliable and repeatable manner.” (Note interpretation in view of Applicant’s as-filed specification ¶ 0026). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s environmental parameters with Klimov’s system in order to operate a quantum system in a reliable and repeatable manner as suggested by Zou.
In regard to claim 20, Klimov discloses:
20. A non-transitory computer-readable storage medium comprising instructions that are executable by a classical or quantum processing device and upon such execution cause the classical or quantum processing device to perform operations comprising: Klimov ¶ 0101, “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.”
obtaining … characterization data associated with an operating parameter of a qubit in a quantum computing system, the … characterization data characterizing one or more properties of the qubit …; Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, …” Also ¶ 0066, e.g. “T1,qXY−1(t) represents relaxation time during the frequency sweep qXY, which depends on fqXY(t).”
implementing an … operation to extract one or more predicted anomaly … from the characterization data,…; and Klimov ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, such as … predicted and/or measured relaxation and/or coherence times of the qubits included in the quantum computing device.” Also ¶ 0058, “The system defines a first cost function that maps qubit operation frequency values (e.g., all qubit idling frequencies, as described below) to a cost (e.g., a real number) corresponding to an operating state of the quantum device (step 302). e.g., an operating state that executes an arbitrary quantum algorithm with lower error rates compared to other operating states.”
Klimov does not expressly disclose the following limitations taught by Zou:
time-dependent characterization data at each of a plurality of times … Zou, ¶ 0085, “After the initial static update, the corrective sequence management circuitry/logic 1000 dynamically updates the spin-echo sequence table 1005 over time, as new errors are associated with the various qubits of the quantum processor 207.”
unsupervised learning to extract anomaly clusters … each anomaly cluster comprising one or more first anomalies at a first time of the plurality of times and … Zou, ¶ 0087, “Thus, in one embodiment, the machine-learning logic/circuit 1008 performs unsupervised learning of new errors as they occur. Unsupervised learning is particularly beneficial for working with a quantum processor 207 because the physical responses of the individual qbits may change over time and may also vary from one quantum processor to another.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s input data in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
Klimov and Zou does not expressly disclose: one or more second anomalies at a second time of the plurality of times. This is taught by Mishra. See col. 10, lines 3-5, “The categorization engine 124 may group the events 110 (or the operating characteristics that indicate the events 110) into clusters of different categories. … For example, a first cluster may represent “priority events” (also referred to as “meaningful events”) that have a high likelihood of being indicative of a status that is a precursor to a fault (or some other status associated with one or more recommended actions) …” Also col. 16, lines 13-23, “… events identified based on the historical operating data 156, quantities of the events, times of the events, inferences determined based on the events, such as statuses (e.g., fault precursor states) of the equipment asset 150, …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Mishra’s event clustering with the qbit data of Klimov and Zou in order to predict a potential fault event as suggested by Mishra (see col. 10 lines 3-27).
Klimov also discloses:
modifying an operating parameter of the qubit in the quantum computing system based at least in part on the one or more predicted anomaly clusters. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.”
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou and Mishra as applied above, and further in view of U.S. Patent Application Publication 20140372346 by Phillipps et al. ("Phillipps").
In regard to claim 6, Klimov does not expressly disclose:
6. The method of claim 1, wherein the unsupervised learning operation comprises a clustering operation. This is taught by Phillipps, ¶ 0079, “As shown, in one embodiment, the unsupervised learning module 208 includes a clustering module 230.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Phillipps clustering with the learning operations of Zou and Klimov in order to summarize and explain key features of a data set as suggested by Phillipps (see ¶ 0072).
In regard to claim 7, Phillipps also teaches:
7. The method of claim 6, wherein the clustering operation comprises a density-based clustering operation or a spectral-based clustering operation. Phillipps, ¶ 0079, “Non-limiting examples of clustering algorithms include … density-based clustering algorithms, spectral clustering algorithms.”
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou and Mishra as applied above, and further in view of U.S. Patent Application Publication 20190057301 by Pantazi et al. ("Pantazi").
In regard to claim 9, Klimov, Zou and Mishra do not expressly teach:
9. The method of claim 8, wherein pre-processing the characterization data comprises inverting the characterization data. However, this is taught by Pantazi, e.g. ¶ 0029, “Data inverter 10 receives the input image data and produces complementary pixel data for each pixel of the image. The complementary pixel data defines a value which is complementary to that of the pixel data for the corresponding pixel.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Pantazi’s data inversion with the pre-processing of Klimov and Mishra in order to provide advantages such as increasing sensibility for correlation detection, leading to improved neural network performance as suggested by Pantazi (see ¶ 0008).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou and Mishra as applied above, and further in view of “Fluctuations of Energy-Relaxation Times in Superconducting Qubits” by Klimov et al. (“Klimov-NPL”).
In regard to claim 13, Klimov does not expressly disclose:
13. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises determining one or more of a density, diffusivity, velocity, or an acceleration associated with the one or more anomalies. However, this is taught by Klimov-NPL, p. 4, par. 3, e.g. “To estimate the density of TF defects and to understand the relationship between the telegraphic and diffusive regimes, we run a Markov-Chain Monte Carlo simulation of interacting defect dynamics in a thin film representative of the interfacial dielectrics in our qubit circuit. Since the diffusivity of TLS transitions is expected to depend strongly on TF density, we use it to connect simulation to experiment.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the density and diffusivity of Klimov-NPL with Klimov’s anomalies in order to utilize an estimation of system state as essentially suggested by Klimov-NPL.
Claim(s) 16 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou, Mishra and U.S. Patent Application Publication 20190165244 by Hertzberg et al. ("Hertzberg").
In regard to claim 16, Klimov discloses:
16. A quantum computing system comprising: a plurality of superconducting qubits, each qubit configured to be operated using an operating frequency, each operating frequency associated with an energy relaxation time; Klimov, ¶ 0043, “Quantum computing devices often include multiple qubits arranged in a two-dimensional grid, where neighboring qubits allowed to interact. Each qubit may be operated using respective operating frequencies, e.g., respective idling and interaction frequencies.” Also ¶ 0044, “One proxy for assessing how good a particular operating frequency is for a particular qubit is that qubit's relaxation time (T1) at that frequency.” Also ¶ 0056, “… the quantum computing device includes a two dimensional grid of interacting superconducting qubits.”
one or more processors configured to execute computer-readable instructions stored in one or more memory devices to perform operations, the operations comprising: Klimov, ¶ 0005, “One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.”
obtaining … characterization data associated with the energy relaxation time for each of the plurality of superconducting qubits at a plurality of possible operating frequencies …; Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, …” Also ¶ 0066, e.g. “T1,qXY−1(t) represents relaxation time during the frequency sweep qXY, which depends on fqXY(t).”
implementing an … operation to extract, from the characterization data, one or more anomaly … comprising data indicative of a first state of a respective two-level-system … Klimov ¶ 0058, “The system defines a first cost function that maps qubit operation frequency values (e.g., all qubit idling frequencies, as described below) to a cost (e.g., a real number) corresponding to an operating state of the quantum device (step 302). e.g., an operating state that executes an arbitrary quantum algorithm with lower error rates compared to other operating states.” Also ¶ 0102, “It is understood that the term “qubit” encompasses all quantum systems that may be suitably approximated as a two-level system in the corresponding context.”
Klimov does not expressly disclose the following limitations taught by Zou:
time-dependent data … at each of a plurality of times; Zou, ¶ 0085, “After the initial static update, the corrective sequence management circuitry/logic 1000 dynamically updates the spin-echo sequence table 1005 over time, as new errors are associated with the various qubits of the quantum processor 207.”
… unsupervised learning to extract anomaly clusters each indicative of state of… defect at a first time of the plurality of times and Zou, ¶ 0087, “Thus, in one embodiment, the machine-learning logic/circuit 1008 performs unsupervised learning of new errors as they occur. Unsupervised learning is particularly beneficial for working with a quantum processor 207 because the physical responses of the individual qbits may change over time and may also vary from one quantum processor to another.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s input data in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
Klimov and Zou do not expressly disclose:
a second state of the respective two-level-system defect at a second time of the plurality of times; This is taught by Mishra. See col. 10, lines 3-5, “The categorization engine 124 may group the events 110 (or the operating characteristics that indicate the events 110) into clusters of different categories. … For example, a first cluster may represent “priority events” (also referred to as “meaningful events”) that have a high likelihood of being indicative of a status that is a precursor to a fault (or some other status associated with one or more recommended actions) …” Also col. 16, lines 13-23, “… events identified based on the historical operating data 156, quantities of the events, times of the events, inferences determined based on the events, such as statuses (e.g., fault precursor states) of the equipment asset 150, …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Mishra’s event clustering with the two-level defect analysis of Klimov and Zou in order to predict a potential fault event as suggested by Mishra (see col. 10 lines 3-27).
determining, based on the one or more anomaly clusters, one or more … defects for at least one superconducting qubit of the plurality of superconducting qubits; and Klimov ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, such as … predicted and/or measured relaxation and/or coherence times of the qubits included in the quantum computing device.” Also ¶ 0058, “The system defines a first cost function that maps qubit operation frequency values (e.g., all qubit idling frequencies, as described below) to a cost (e.g., a real number) corresponding to an operating state of the quantum device (step 302). e.g., an operating state that executes an arbitrary quantum algorithm with lower error rates compared to other operating states.” ¶ 0102, “It is understood that the term “qubit” encompasses all quantum systems that may be suitably approximated as a two-level system in the corresponding context.”
Klimov does not expressly disclose:
predicted collisions with one or more two-level-system defects.
However, Zou teaches unsupervised learning to determine anomalies. See Zou, ¶ 0087, “Thus, in one embodiment, the machine-learning logic/circuit 1008 performs unsupervised learning of new errors as they occur. Unsupervised learning is particularly beneficial for working with a quantum processor 207 because the physical responses of the individual qbits may change over time and may also vary from one quantum processor to another.” Note that Klimov teaches two-level system defects as cited above. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s two-level system input data in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
Also, Hertzberg teaches qubit collision defects. See Hertzberg ¶ 0042, “a qubit may have quantum interactions with other proximate qubits, based on their resonance frequency. Such behavior constitutes a failure mode known as a “frequency collision.” Frequency collisions can be predicted by modeling of the quantum-mechanical system.” Also ¶ 0083, “The set-point for each qubit may be determined based on frequency collision model 718, by modeling a plurality of frequencies for the qubits to determine collision probabilities of the system, and optimizing the relative values of the frequencies of the various qubits in the system, to minimize measured frequency collisions and consequently improve the multi-qubit gate fidelities.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Hertzberg’s collision prediction with the unsupervised learning/prediction of two-level system defects of Klimov and Zou in order to minimize frequency collision defects and consequently improve the multi-qubit gate fidelities and achieve high performance as suggested by Hertzberg (¶ 0042, 0061 and 0083).
Klimov also discloses:
modifying an operating frequency for the at least one superconducting qubit based at least in part on the one or more predicted collisions with the two-level-system defect. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.”
In regard to claim 18, Klimov and Zou also teach:
18. The quantum computing system of claim 16, wherein determining the one or more predicted collisions for at least one superconducting qubit of the plurality of superconducting qubits comprises determining, for each respective superconducting qubit of the plurality of superconducting qubits in parallel. See Klimov, ¶ 0046, “In addition, the methods may break the optimization problem into multiple independent sub-problems that may be solved quickly and in parallel using standard optimization techniques.”
In regard to claim 19, Klimov and Hertzberg also teach:
19. The quantum computing system of claim 16, wherein the quantum computing system is configured to implement a quantum gate on the at least one superconducting qubit based at least in part on the one or more predicted collisions with two-level-system defects. Klimov, ¶ 0064, “To perform a two-qubit computational gate, the participating qubits are brought into resonance.” Also, see Hertzberg ¶ 0083, “The set-point for each qubit may be determined based on frequency collision model 718 … and consequently improve the multi-qubit gate fidelities.”
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou, Mishra and Hertzberg as applied above, and further in view of Phillipps.
In regard to claim 17, parent claim 16 is addressed above.
All further limitations of claim 17 have been addressed in the above rejection of claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent 11895931 to Hertzberg et al. teaches prediction of qubit frequency collisions. See col. 4, lines 60-31.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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