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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-28 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-28 of copending Application No. 17/935,284 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because The co-pending claim discloses all the limitation of the claims 1-28 of in instant application. The only different between 1 of the instant application and 1 of the co-pending application is that 1 of the instant application discloses the training samples for training samples for the pretraining task involve unitary matrices, circuits, or microwave pulses for a family of quantum devices. However, this discloses in claim 14 of the co-pending application which cites “The method of claim 1, wherein the pretraining task involves generating unitary matrices, circuits, or microwave pulses.”. Thus claim 1 of instant application is anticipated by claims 1 and 14 of the co-pending application. Additionally, the co-pending application discloses the remaining claim 2-28 of the instant application. See the chart below for claim mapping between the two applications.
Instant Application – 17/952,935
Co-pending Application – 17/935,284
1. A computer-implement method comprising:
1. A computer-implemented method comprising:
obtaining training samples by automatically generating said samples for a pretraining task involving unitary matrices, circuits, or microwave pulses for a family of quantum devices;
automatically generating training samples efficiently for a general computational task and a family of quantum devices; and
14. (Original) The method of claim 1, wherein the pretraining task involves generating unitary matrices, circuits, or microwave pulses.
pretraining a quantum foundation model with training samples to embed information about the family of quantum devices and pretraining task;
pretraining a quantum foundation model to embed information about physical characteristics of a family of quantum devices and a general computational task
fine-tuning the model with a specialized dataset to perform a specific computational task on a specific quantum computational device; and
fine-tuning the model with a specialized dataset to perform a specific computational task on a specific quantum computational device; and
using the fine-tuned model to generate inputs to the quantum device, wherein the generated inputs reduce gate execution time, reduce gate errors, reduce cross-talk, reduce leakage, or reduce the number of gates needed to execute an algorithm on the quantum device.
using the fine-tuned model to generate inputs configured to reduce a metric selected from the group consisting of gate execution times, cross-talk, leakage, gate errors on the quantum device, and number of gates needed to execute an algorithm on the quantum device.
2. (Currently Amended) The method of claim 1, wherein the quantum foundation model further comprises:
Pretraining with a process that uses a generative adversarial model, the generative adversarial model comprising a generator and a discriminator, to evaluate the quality of states.
2. (Currently Amended) The method of claim 1, wherein the quantum foundation model further comprises:
pretraining with a process that uses a generative adversarial model to evaluate the quality of states, wherein the evaluation is based on a generator output produced by a generator network.
The instant applicant cites that the generative adversarial model comprising a generator and a discriminator which is not cited in the co-pending claim 2. However, all generative adversarial networks (GANs) consist of a generator, which creates data samples and discriminator that evaluates them. Thus, it is inherent to GANs.
3. (Original) The method of claim 2, further comprising: pretraining the foundation model with a structure that uses classical simulators to transform the generator output into a quantum state.
3. (Currently Amended) The method of claim 2, further comprising: pretraining the foundation model with a structure that uses classical simulators to transform the generator output of the generative adversarial model into a quantum state.
4. (Original) The method of claim 3, further comprising: simulating quantum systems classically with different noise parameters, including but not limited to gate errors and thermal relaxation times.
4. (Currently Amended) The method of claim 3, further comprising: simulating quantum systems classically with different noise parameters, including
5. (Currently Amended) The method of claim 1, wherein an output generated by the quantum foundation model after pretraining is used without fine-tuning.
5. (Currently Amended) The method of claim 1, wherein an output of the quantum foundation model is used without fine-tuning.
6. (Original) The method of claim 1, wherein the quantum foundation model is not fine-tuned and its output is directly input into a specialized model.
6. (Original) The method of claim 1, wherein the quantum foundation model is not fine-tuned and its output is directly input into a specialized model.
7. (Original) The method of claim 1, wherein the generated quantum device inputs are a sequence of gates, a sequence of microwave pulses, or a sequence of unitary operations.
7. (Original) The method of claim 1, wherein the generated quantum device inputs are a sequence of gates, a sequence of microwave pulses, or a sequence of unitary operations.
8. (Original) The method of claim 1, wherein the quantum foundation model has a neural network architecture.
8. (Original) The method of claim 1, wherein the quantum foundation model has a neural network architecture.
9. (Original) The method of claim 1, wherein the quantum foundation model uses a transformer model architecture.
9. (Original) The method of claim 1, wherein the quantum foundation model uses a transformer model architecture.
10. (Original) The method of claim 1, wherein the pretraining sample is prepared by generating random unitary matrices or random gate sequences.
10. (Original) The method of claim 1, wherein the pretraining sample is prepared by generating random unitary matrices or random gate sequences.
11. (Original) The method of claim 1, wherein the pretraining sample is prepared by generating random microwave pulses and simulating them classically.
11. (Original) The method of claim 1, wherein the pretraining sample is prepared by generating random microwave pulses and simulating them classically.
12. (Original) The method of claim 1, wherein the target family of quantum devices are superconducting circuits, ion traps, quantum annealers, or Boson samplers.
12. (Original) The method of claim 1, wherein the target family of quantum devices are superconducting circuits, ion traps, quantum annealers, or Boson samplers.
13. (Original) The method of claim 1, wherein the target family of quantum devices are universal quantum computers.
13. (Original) The method of claim 1, wherein the target family of quantum devices are universal quantum computers.
14. (Original) The method of claim 1, wherein the pretraining task involves generating unitary matrices, circuits, or microwave pulses.
14. (Original) The method of claim 1, wherein the pretraining task involves generating unitary matrices, circuits, or microwave pulses.
15. A system comprising:
15. A system comprising:
a classical processor; and
a processor; and
a non-transitory computer-readable medium storing instructions that, when executed by the classical processor, cause the system to perform operations comprising:
a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the system to perform operations comprising:
Obtaining training samples by automatically generating said samples for a pretraining task involving unitary matrices, circuits, or microwave pulses for a family of quantum devices;
automatically generating training samples efficiently for a general computational task and a family of quantum devices: and
pretraining a quantum foundation model with the training samples to embed information about the family of quantum devices and the pretraining task;
pretraining a quantum foundation model to embed information about physical characteristics of a family of quantum devices and a general computational task
fine-tuning the model with a specialized dataset to perform a specific computational task on a specific computational device; and
fine-tuning the model with a specialized dataset to perform a specific computational task on a specific computational device; and
using the fine-tuned model to generate higher quality inputs to the quantum device, wherein the generated inputs reduce gate execution time, reduce gate errors, reduce cross-talk, reduce leakage, or reduce the number of gates needed to execute an algorithm on the quantum device.
using the fine-tuned model to generate inputs configured to reduce a metric selected from the group consisting of gate execution times, cross-talk, leakage, gate errors, and number of gates needed to execute an algorithm on the quantum device.
16. The system of claim 15, wherein the quantum foundation model further comprises:
Pretraining with a process that uses a generative adversarial model, the generative adversarial model comprising a generator and a discriminator, to evaluate the quality of states.
16. (Currently Amended) The system of claim 15, wherein the quantum foundation model further comprises: pretraining with a process that uses a generative adversarial model to evaluate the quality of states, wherein the evaluation is based on a generator output produced by a generator network.
17. (Original) The system of claim 16, further comprising: pretraining the foundation model with a structure that uses classical simulators to transform the generator output into a quantum state.
17. (Currently Amended) The system of claim 16, further comprising: pretraining the foundation model with a structure that uses classical simulators to transform the generator output of the generative adversarial model- into a quantum state.
18. (Original) The system of claim 17, further comprising: simulating quantum systems classically with different noise parameters, including but not limited to gate errors and thermal relaxation times.
18. (Currently Amended) The system of claim 17, further comprising: simulating quantum systems classically with different noise parameters, including gate errors and thermal relaxation times.
19. The system of claim 15, wherein an output generated by the quantum foundation model after pretraining is used without fine-tuning.
19. (Currently Amended) The system of claim 15, wherein an output of the quantum foundation model is used without fine-tuning.
20. (Original) The system of claim 15, wherein the quantum foundation model is not fine-tuned and its output is directly input into a specialized model.
20. (Original) The system of claim 15, wherein the quantum foundation model is not fine-tuned and its output is directly input into a specialized model.
21. (Original) The system of claim 15, wherein the generated quantum device inputs are a sequence of gates, a sequence of microwave pulses, or a sequence of unitary operations.
21. (Original) The system of claim 15, wherein the generated quantum device inputs are a sequence of gates, a sequence of microwave pulses, or a sequence of unitary operations.
22. (Original) The system of claim 15, wherein the quantum foundation model has a neural network architecture.
22. (Original) The system of claim 15, wherein the quantum foundation model has a neural network architecture.
23. (Original) The system of claim 15, wherein the quantum foundation model uses a transformer model architecture.
23. (Original) The system of claim 15, wherein the quantum foundation model uses a transformer model architecture.
24. (Original) The system of claim 15, wherein the pretraining sample is prepared by generating random unitary matrices or random gate sequences.
24. (Original) The system of claim 15, wherein the pretraining sample is prepared by generating random unitary matrices or random gate sequences.
25. (Original) The system of claim 15, wherein the pretraining sample is prepared by generating random microwave pulses and simulating them classically.
25. (Original) The system of claim 15, wherein the pretraining sample is prepared by generating random microwave pulses and simulating them classically.
26. (Original) The system of claim 15, wherein the target family of quantum devices are superconducting circuits, ion traps, quantum annealers, or Boson samplers.
26. (Original) The system of claim 15, wherein the target family of quantum devices are superconducting circuits, ion traps, quantum annealers, or Boson samplers.
27. (Original) The system of claim 15, wherein the target family of quantum devices are universal quantum computers.
27. (Original) The system of claim 15, wherein the target family of quantum devices are universal quantum computers.
28. (Original) The system of claim 15, wherein the pretraining task involves generating unitary matrices, circuits, or microwave pulses.
28. (Original) The system of claim 15, wherein the pretraining task involves generating unitary matrices, circuits, or microwave pulses.
Response to Arguments
Applicant's arguments filed 26 November have been fully considered but they are not persuasive. The applicant argues that amendment to the claims overcome the double patenting rejection and as such the rejection should be withdrawn. However, the examiner respectfully traverses the applicant’s argument as the amendment the claims are still anticipated by claims 1-28 of the co-pending application. The only different between 1 of the instant application and 1 of the co-pending application is that 1 of the instant application discloses the training samples for training samples for the pretraining task involve unitary matrices, circuits, or microwave pulses for a family of quantum devices. However, this discloses in claim 14 of the co-pending application which cites “The method of claim 1, wherein the pretraining task involves generating unitary matrices, circuits, or microwave pulses.”. Thus claim 1 of instant application is anticipated by claims 1 and 14 of the co-pending application. Additionally, the co-pending application discloses the remaining claim 2-28 of the instant application. As such the rejection for non-statutory provisional double patenting is maintained, see the chart above for claim mapping between the two applications.
Applicant’s arguments with respect to the objection to the specification, rejection under 35 USC 101 for the computer-readable medium being directed to non-statutory subject matter, and the rejection under 35 USC 102 have been fully considered and are persuasive; therefore, the rejections have been withdrawn.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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/PAULINHO E SMITH/Primary Examiner, Art Unit 2127