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 .
Status of Claims
The present application is being examined under the claims filed on January 30, 2024.
Claims 1-18 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on August 20, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length.
The abstract of the disclosure is objected to because AI and GPU are acronyms that should be spelled out upon their first use. Although AI and GPU are currently widely understood to be referring to Artificial Intelligence and Graphics Processing Unit, they should be spelled out upon first use for clarity of the record. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 1-6 are objected to because of the following informalities:
In claims 1 and 13, ‘(“qubits”)’ and ‘(“GPU”)’ should read, “(qubits)” and “(GPU)”, respectively.
Claims 2-6 and 14-18 are objected to for inheriting the deficiencies of 1 and 13, respectively.
In claims 2, 8, and 14, “wherein the quantum computing rate that is at least two-times higher than the reference rate” should read, “wherein the quantum computing rate is at least two-times higher than the reference rate”.
In claims 3, 9, and 15, “wherein the quantum computing rate that is at least five-times higher than the reference rate” should read, “wherein the quantum computing rate is at least five-times higher than the reference rate”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding Claims 1-20:
The following are claimed subject matter for which the specification is not enabling:
Claims 1, 7, and 13 recite: “a digital computer running the GPU trains the AI algorithm to create the AI model at a reference rate; and the quantum computer running the GPU trains the AI algorithm to create the AI model at a quantum computing rate that is higher than the reference rate”.
Claims 2, 8, and 14 recite: “wherein the quantum computing rate that is at least two-times higher than the reference rate”
Claims 3, 9, and 15 recite: “wherein the quantum computing rate that is at least five-times higher than the reference rate”
The claimed subject matter is claiming that a quantum computer using a GPU and an AI algorithm to train an AI model will complete the training faster than a digital computer using the same GPU and AI algorithm. Although this is expressed throughout the current literature as theoretically possible, it has not been shown as something that can be currently achieved.
Wands Factors [see MPEP 2164.01]
The breadth of the claims:
The claims broadly recite a computational speed up, but do not recite how this speed up is done.
The nature of the invention:
The claims recite subject matter that is shown to not be achievable in practice. Specifically, that a quantum computer using a GPU and AL algorithm will complete training faster than a digital computer using the same GPU and algorithm.
The state of the prior art:
Havenstein et al. (“Comparisons of Performance between Quantum and Classical Machine Learning”, 2018) discloses on page 2, “[t]o determine if there was a quantum speedup over the relevant classical machine learning algorithm, we utilized wall time, which measures the program execution time on the classical or quantum hardware in microseconds…However, we observed no quantum speedups when comparing wall times for algorithm executions.”
Zahorodko et al. (“Comparisons of Performance Between Quantum-enhanced and Classical Machine Learning Algorithms on IBM Quantum Experience”, 2021) discloses on page 8, “[a]nalysis of Table 3 allows us to conclude that at the current stage of quantum technologies development traditionally machine learning provides greater performance than quantum-enhanced.”
Bowles et al. (“Better Than Classical? The Subtle Art of Benchmarking Quantum Machine Learning Models”, 2024), discloses on page 14, “[a] very clear finding across our experiments was that the out-of-the-box classical models systematically outperform the quantum models.”
Anas et al. (“Quantum Machine Learning vs. Classical Machine Learning: A Case Study on Predicting University Performance using Scientometric Indicators”, 2025) discloses on page 3317, “This study evaluates the performance of quantum support vector machine (QSVM) compared to classical support vector machine (SVM) in predicting university performance by using scientometric indicators…Both models were assessed using standard classification metrics, with QSVM showing modest gains in accuracy (92.3%) and F1-score (91.2%) over SVM (88.7% and 87.2%, respectively), albeit with significantly longer processing time—approximately six times slower.”
Yu et al. (“Quantum vs. Classical Machine Learning: A Unified Empirical Comparison”, 2026) discloses on page 12, “[t]he key findings of this study include the following: Classical machine learning (CML) models currently outperform the evaluated QML [Quantum machine learning] models in terms of overall accuracy, policy stability, and training efficiency across both classification and sequential decision-making tasks.”
The level of one of ordinary skill:
A high level of skill in the art at the time the application was filed is required for quantum computing.
The level of predictability in the art:
The claimed subject matter has a lack of predictability in the art because how to achieve the claimed speedup in practice is not easily predictable in the state of the art.
The amount of direction provided by the inventor:
The inventor has not provided any direction on how to achieve the speed up in the claimed subject matter. The specification merely repeats the claimed language in paragraphs [0012]-[0015] and [0038]-[0041].
The existence of working examples:
There are no examples provided on how to achieve the speed up in the claimed subject matter. The specification merely repeats the claimed language in paragraphs [0012]-[0015] and [0038]-[0041].
The quantity of experimentation needed to make or use the invention based on the content of the disclosure:
The disclosure merely recites the claimed language, and Applicant has not provided any disclosure on how to achieve the claimed subject matter in practice. Therefore, it would require an undue amount of experimentation to make or use the invention based on the content of the disclosure.
There is a lack of enablement based on the evidence regarding each of the above factors because at the time the application was filed, the specification would not have taught one skilled in the art how to make and/or use the full scope of the claimed invention without undue experimentation. Therefore, claims 1-3, 7-9, and 13-15 are rejected for failing to comply with the enablement requirement.
Furthermore, Claims 2-6, 8-12, and 14-18 are rejected for inheriting the deficiencies of claims 1, 7, and 13, respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Vargas Calderon et al. (US 20250355962), hereinafter Calderon, in view of Wissel et al. (US 20220321333), hereinafter Wissel, and further in view of Preskill (“Quantum Computing in the NISQ Era and Beyond”), hereinafter Preskill.
Regarding Claim 1:
Calderon discloses:
A system for training an artificial intelligence (AI) model using a quantum computer, the system comprising:
Calderon, [0002], “A method for solving a continuous optimization problem includes: training a generative model using a training data set”
[0026], “The system 100 includes a quantum computer 102.”
Calderon discloses training a model, and their system includes a quantum computer [system for training an artificial intelligence (AI) model using a quantum computer].
the quantum computer
As cited above in para. 26, Calderon discloses a quantum computer.
an AI algorithm
Calderon, [0054], “In what follows, we explain in detail the cGEO [Continuous Generative Enhanced Optimization] algorithm.”
Calderon discloses cGEO [an AI algorithm].
the AI model
As cited above in para. 2, Calderon discloses a generative model [the AI model].
a dataset that includes data
Calderon, [0083]-[0084], “Memory 404 stores an input configuration pool 411, a cost function 414, intermediate data 440…Intermediate data 440 includes a training dataset 441…”
Calderon discloses a training data set [a dataset that includes data].
wherein: the quantum computer is configured to: receive, at the quantum computer, the data
Calderon, [0021], “Quantum annealing starts with the classical computer 254 generating an initial Hamiltonian 260 and a final Hamiltonian 262 based on a computational problem 258 to be solved, and providing the initial Hamiltonian 260, the final Hamiltonian 262 and an annealing schedule 270 as input to the quantum computer 252. The quantum computer 252 prepares a well-known initial state 266 (FIG. 2B, operation 264), such as a quantum-mechanical superposition of all possible states (candidate states) with equal weights, based on the initial Hamiltonian 260.”
store:
the data as quantum bits ("qubits")
Calderon, [0010], “In general, the fundamental data storage unit in quantum computing is the quantum bit, or qubit.”
Calderon discloses the storage [store] unit in quantum computing is a qubit [the data as quantum bits (“qubits”)].
the dataset as a set of qubits in superposition states
Calderon, [0011], “a single qubit can represent a one, a zero, or any quantum superposition of those two qubit states; a pair of qubits can be in any quantum superposition of 4 orthogonal basis states; and three qubits can be in any superposition of 8 orthogonal basis states”
Calderon discloses qubits in quantum superposition states [the dataset as a set of qubits in superposition states].
initiate training of the AI algorithm using the dataset to create the AI model
Calderon, [0072], “2. Train a generative model using the training data set Dr”.
Calderon discloses their model using the dataset to create the trained model [initiate training of the AI algorithm using the dataset to create the AI model].
transition the set of qubits from the superposition states into a binary state, thereby transitioning the dataset into a binary state
Calderon, [0051], “The measurement unit 110 in the quantum computer 102 (which may be implemented as described above in connection with FIGS. 1 and 2A-2B) may measure the states of the qubits 104 and produce measurement output 338 representing the collapse of the states of the qubits 104 into one of their eigenstates. As a result, the measurement output 338 includes or consists of bits and therefore represents a classical state.”
provide the dataset to a graphics processing unit (“GPU”) to train the AI algorithm to create the AI model, said GPU configured to run on the quantum computer
Calderon, [0093], “One or both of steps 510 and step 520 may be partly or entirely executed by quantum computer 102, as generative models produced and/or trained by quantum computers, as well as configuration pools, can be superior to those produced and/or trained by classical computers for certain optimization problems.”
[0079], “FIG. 4 is a schematic of a solver 400, which includes a processor 486 and a memory 404…Solver 400 may include at least one of quantum computer 102”
[0081], “For example, processor 486 may include one or more of…a graphics processing unit (GPU)”
In para. 93, Calderon discloses training using the quantum computer [train the AI algorithm to create the AI model]. Para. 79 discloses a solver, using a quantum computer, that has a processor and memory. Para. 81 specifies the processor can be a GPU [provide the dataset to a graphics processing unit (“GPU”)…said GPU configured to run on the quantum computer].
Calderon does not explicitly disclose:
a cryptographic key
protect the dataset in the binary state with the cryptographic key, the cryptographic key configured to apply quantum-resistant cryptography or quantum cryptography
wherein: a digital computer running the GPU trains the AI algorithm to create the AI model at a reference rate; and the quantum computer running the GPU trains the AI algorithm to create the AI model at a quantum computing rate that is higher than the reference rate
However, in the same field, analogous art Wissel teaches:
a cryptographic key
protect the dataset in the binary state with the cryptographic key, the cryptographic key configured to apply quantum-resistant cryptography or quantum cryptography
Wissel, [0062], “According to the invention two different key exchange procedures are combined, preferably QKD and PQC. A QKD platform has the task to provide a quantum secured key, KQKD, between two locations separated from each other. This quantum secured key, KQKD, is secure against attacks of a quantum computer.”
Wissel discloses a combination of two different key exchange [a cryptographic key] procedures. In particular, Wissel discloses using QKD [the cryptographic key configured to apply…quantum cryptograph]] and PQC [the cryptographic key configured to apply quantum-resistant cryptography]. Wissel further states the cryptographic keys are for security [protect the dataset in the binary state].
Calderon, Wissel, and the instant application are analogous art because they are all directed to quantum computing.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Calderon with Wissel to use QKD and/or PQC in order to secure data robustly. “It is an advantage that the created encryption key does not require that users installed at the first location and the second location, respectively, and using the created encryption key, must trust the provider(s) of the trusted node(s). According to the invention two different key exchange procedures are combined, preferably QKD and PQC. A QKD platform has the task to provide a quantum secured key, KQKD, between two locations separated from each other. This quantum secured key, KQKD, is secure against attacks of a quantum computer” (Wissel, [0062]).
However, in the same field, analogous art Preskill teaches:
wherein: a digital computer running the GPU trains the AI algorithm to create the AI model at a reference rate; and the quantum computer running the GPU trains the AI algorithm to create the AI model at a quantum computing rate that is higher than the reference rate
Preskill, pp. 7, “When will quantum computers be able to solve problems we care about faster than classical computers, and for what problems? At least in the near term quantum computers are likely to be special purpose devices, which most users will access via the cloud. When we speak of a quantum speedup, we typically mean that the quantum computer solves the problem faster than competing classical computers using the best available hardware and running the best algorithm which performs the same task.”
Preskill discloses the concept of quantum speedup, the idea that quantum computers can theoretically solve problems faster than classical computers. This is interpreted as theoretically possible, but not achievable currently due to Preskill explicitly disclosing that quantum computers in the near future are still special purpose devices.
p. 10, “Machine learning is transforming technology and having a big impact on science as well, so it is natural to wonder about the potential of combining machine learning with quantum technology. There are a variety of different notions of ‘quantum machine learning.’ Much of the literature on the subject builds on quantum algorithms that speed up linear algebra and related tasks [37, 38], and I’ll address such applications in the ensuing subsections.”
p. 11, “QRAM has further implications for quantum algorithms. In particular, the task of matrix inversion admits an exponential quantum speedup, which could have many applications. The algorithm we call HHL…runs in time O(logN), an exponential speedup relative to classical matrix inversion…I expect HHL to have high-impact applications eventually, but it is not likely to be feasible in the NISQ era. The algorithm may just be too expensive to be executed successfully by a quantum computer which doesn’t use error correction.”
On page 10, Preskill teaches ML and Quantum has potential and majority of the literature is built on quantum algorithms that theoretically speeds up linear algebra. Page 11, an example is disclosed where the quantum algorithm for matrix inversion has a theoretical exponential quantum speedup. As discussed above in section 112(a), the prior art (including Preskill) disclose that quantum computers are currently not feasible and faster than classical computers when it comes to machine learning, but the theory of quantum computing shows that it is possible for quantum computers to be potentially faster in the future. Therefore, Preskill teaches the claimed language because Preskill shows theoretically that quantum computing in ML may eventually achieve quantum speedup.
Calderon, Wissel, Preskill, and the instant application are analogous art because they are all directed to quantum computing.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Calderon and Wissel with Preskill to use quantum computing because it has been theoretically shown to be faster than classical computing which has potential for various reasons as stated by Preskill. “A few years ago I spoke enthusiastically about quantum supremacy as an impending milestone for human civilization [22]. I suggested this term as a way to characterize computational tasks performable by quantum devices, where one could argue persuasively that no existing (or easily foreseeable) classical device could perform the same task, disregarding whether the task is useful in any other respect. I was trying to emphasize that now is a very privileged time in the coarse-grained history of technology on our planet, and I don’t regret doing so. But from a commercial perspective, obviously we should pay attention to whether the task is useful! Quantum supremacy is a worthy goal, notable for entrepreneurs and investors not so much because of its intrinsic importance but rather as a sign of progress toward more valuable applications further down the road” (Preskill, p. 7).
Regarding Claim 2:
As discussed above Calderon in view of Wissel, further in view of Preskill teach [the] system of claim 1, and Preskill further discloses:
wherein the quantum computing rate that is at least two-times higher than the reference rate
Preskill, p. 11, “QRAM has further implications for quantum algorithms. In particular, the task of matrix inversion admits an exponential quantum speedup, which could have many applications. The algorithm we call HHL…runs in time O(logN), an exponential speedup relative to classical matrix inversion…I expect HHL to have high-impact applications eventually, but it is not likely to be feasible in the NISQ era. The algorithm may just be too expensive to be executed successfully by a quantum computer which doesn’t use error correction.”
On page 11, an example is disclosed where the quantum algorithm for matrix inversion has a theoretical exponential quantum speedup [the quantum computing rate that is at least two-times higher than the reference rate].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Calderon, Wissel, and Preskill, further with Preskill to use quantum computing because it has been theoretically shown to be faster than classical computing which has potential for various reasons as stated by Preskill. “A few years ago I spoke enthusiastically about quantum supremacy as an impending milestone for human civilization [22]. I suggested this term as a way to characterize computational tasks performable by quantum devices, where one could argue persuasively that no existing (or easily foreseeable) classical device could perform the same task, disregarding whether the task is useful in any other respect. I was trying to emphasize that now is a very privileged time in the coarse-grained history of technology on our planet, and I don’t regret doing so. But from a commercial perspective, obviously we should pay attention to whether the task is useful! Quantum supremacy is a worthy goal, notable for entrepreneurs and investors not so much because of its intrinsic importance but rather as a sign of progress toward more valuable applications further down the road” (Preskill, p. 7).
Regarding Claim 3:
As discussed above Calderon in view of Wissel, further in view of Preskill teach [the] system of claim 1, and Preskill further discloses:
wherein the quantum computing rate that is at least five-times higher than the reference rate
Preskill, p. 11, “QRAM has further implications for quantum algorithms. In particular, the task of matrix inversion admits an exponential quantum speedup, which could have many applications. The algorithm we call HHL…runs in time O(logN), an exponential speedup relative to classical matrix inversion…I expect HHL to have high-impact applications eventually, but it is not likely to be feasible in the NISQ era. The algorithm may just be too expensive to be executed successfully by a quantum computer which doesn’t use error correction.”
On page 11, an example is disclosed where the quantum algorithm for matrix inversion has a theoretical exponential quantum speedup [the quantum computing rate that is at least five-times higher than the reference rate].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Calderon, Wissel, and Preskill, further with Preskill to use quantum computing because it has been theoretically shown to be faster than classical computing which has potential for various reasons as stated by Preskill. “A few years ago I spoke enthusiastically about quantum supremacy as an impending milestone for human civilization [22]. I suggested this term as a way to characterize computational tasks performable by quantum devices, where one could argue persuasively that no existing (or easily foreseeable) classical device could perform the same task, disregarding whether the task is useful in any other respect. I was trying to emphasize that now is a very privileged time in the coarse-grained history of technology on our planet, and I don’t regret doing so. But from a commercial perspective, obviously we should pay attention to whether the task is useful! Quantum supremacy is a worthy goal, notable for entrepreneurs and investors not so much because of its intrinsic importance but rather as a sign of progress toward more valuable applications further down the road” (Preskill, p. 7).
Regarding Claim 4:
As discussed above Calderon in view of Wissel, further in view of Preskill teach [the] system of claim 1, and Wissel further discloses:
the cryptographic key is configured to use quantum-resistant cryptography; and said quantum-resistant cryptograph comprising post-quantum cryptography (PQC)
Wissel, [0062], “According to the invention two different key exchange procedures are combined, preferably QKD and PQC. A QKD platform has the task to provide a quantum secured key, KQKD, between two locations separated from each other. This quantum secured key, KQKD, is secure against attacks of a quantum computer.”
Wissel discloses a combination of two different key exchange [the cryptographic key] procedures. In particular, Wissel discloses using both QKD and PQC [the cryptographic key is configured to use quantum-resistant cryptography; and said quantum-resistant cryptograph comprising post-quantum cryptography (PQC)].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Calderon with Wissel to use QKD and/or PQC in order to secure data robustly. “It is an advantage that the created encryption key does not require that users installed at the first location and the second location, respectively, and using the created encryption key, must trust the provider(s) of the trusted node(s). According to the invention two different key exchange procedures are combined, preferably QKD and PQC. A QKD platform has the task to provide a quantum secured key, KQKD, between two locations separated from each other. This quantum secured key, KQKD, is secure against attacks of a quantum computer” (Wissel, [0062]).
Regarding Claim 5:
As discussed above Calderon in view of Wissel, further in view of Preskill teach [the] system of claim 1, and Wissel further discloses:
the cryptographic key is configured to use quantum cryptography; and said quantum cryptography comprising quantum key distribution (QKD)
Wissel, [0062], “According to the invention two different key exchange procedures are combined, preferably QKD and PQC. A QKD platform has the task to provide a quantum secured key, KQKD, between two locations separated from each other. This quantum secured key, KQKD, is secure against attacks of a quantum computer.”
Wissel discloses a combination of two different key exchange [the cryptographic key] procedures. In particular, Wissel discloses using both QKD [the cryptographic key is configured to use quantum cryptography; and said quantum cryptography comprising quantum key distribution (QKD)] and PQC.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Calderon with Wissel to use QKD and/or PQC in order to secure data robustly. “It is an advantage that the created encryption key does not require that users installed at the first location and the second location, respectively, and using the created encryption key, must trust the provider(s) of the trusted node(s). According to the invention two different key exchange procedures are combined, preferably QKD and PQC. A QKD platform has the task to provide a quantum secured key, KQKD, between two locations separated from each other. This quantum secured key, KQKD, is secure against attacks of a quantum computer” (Wissel, [0062]).
Regarding Claim 6:
As discussed above Calderon in view of Wissel, further in view of Preskill teach [the] system of claim 1, and Calderon further discloses:
wherein the quantum computer is configured to run the AI model
Calderon, [0093], “One or both of steps 510 and step 520 may be partly or entirely executed by quantum computer 102, as generative models produced and/or trained by quantum computers, as well as configuration pools, can be superior to those produced and/or trained by classical computers for certain optimization problems.”
Calderon discloses training the model using the quantum computer [the quantum computer is configured to run the AI model].
Regarding Claim 7:
Claim 7 corresponds to claim 1 and is rejected for at least the same reasons as given in the rejection of claim 1.
Regarding Claims 8-12:
Claims 8-12 correspond to claims 2-6 and are rejected for at least the same reasons as given in the rejections of claims 2-6. In particular, 8:2, 9:3, 10:4, 11:5, 12:6.
Regarding Claim 13:
Claim 13 corresponds to claim 1 and is rejected for at least the same reasons as given in the rejection of claim 1.
Regarding Claims 14-18:
Claims 14-18 correspond to claims 2-6 and are rejected for at least the same reasons as given in the rejections of claims 2-6. In particular, 14:2, 15:3, 16:4, 17:5, 19:6.
Conclusion
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/STEVEN PHUNG/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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