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 .
This action is in response to the original filing of 5/26/2023. Claims 1-20 are pending and have been considered below.
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.
Claim(s) 1-5, 8-13, 15, 17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prati et al. (US 2024/0169237 A1) in view of McKiernan et al. (US 2023/0143652 A1).
Claim 1. Prati discloses a method, performed on a … quantum … computer system, for training a quantum-enhanced learning agent,
the … quantum … computer system comprising … a quantum computer,
[a] classical computer including a processor, a non-transitory computer readable medium, and computer instructions stored in the non-transitory computer readable medium, classical hardware (P 0171);
the quantum computer including a quantum component having a plurality of qubits encoded in quantum states of a physical system, a quantum computer operating on a single qubit or multiple qubits (P 0026);
the quantum-enhanced learning agent having an initial state S1, the agent learns a policy to maximize reward over time (P 0081) that defines the behavior of the agent (P 0117) agents are trained depending on the reward function (P 0105) the policies and training represent states of an agent,
an input X1, an observation matrix is input to an agent to determine the addition of a selected one of the base quantum gates to the current quantum circuit (P 0065) as a deep neural network and receives an input vector of an observation (P 0090), and
a set of quantum gates with a set of parameters T1, an agent solves a problem with a number of gates (P 0152) by mapping an operator into a sequence of elementary gates (P 0174) an agent approximates gates (P 0189),
wherein the initial state S1 is encoded in one or more of the plurality of qubits, agents approximate gates (P 0083, 0138, 0145) agents generate qubit logic circuits (P 0181);
wherein the computer instructions, when executed by the processor, perform the method, the method comprising: generating an output Y1 by applying the set of quantum gates with the set of parameters T1 to the initial state S1 and input X1,
computing a reward value R1 based on the output Y1, a reinforcement learning model is applied to iteratively adjust a current approximating quantum circuit by actions decided by an agent (P 0063) providing by the agent, as output, a vector of parameter values representing an action (P 0071) and agent builds a suitable approximating sequence of gates (P 0087) agents generate qubit logic circuits (P 0181);
updating the quantum-enhanced learning agent based on the reward value R1, the updating comprising: replacing the set of parameters T1 with an updated set of parameters T2; and replacing the initial state S1 with an updated state S2, obtaining an updated quantum circuit for the next iteration based on a reward function, obtaining an updated quantum circuit for the next iteration; calculating, by a training environment, for the next iteration, an updated observation matrix, representing the operation of the updated quantum circuit, and an updated reward function; providing the updated observation matrix and the updated reward function to the agent for the next iteration (P 0065-0068).
Prati does not disclose a hybrid quantum-classical computer system; the hybrid quantum-classical computer system comprising a classical computer and a quantum computer, as disclosed in the claims. However, in the same field of invention, McKiernan discloses a hybrid quantum-classical computer (P 0116). Therefore, considering the teachings of Prati and McKiernan, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine a hybrid quantum-classical computer system; the hybrid quantum-classical computer system comprising a classical computer and a quantum computer with the teachings of Prati with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 2. Prati and McKiernan discloses the method of claim 1, and Prati discloses wherein updating the quantum-enhanced learning agent comprises updating the quantum-enhanced learning agent a plurality of times, the kth update having input Xk, an updated state Sk, an updated set of parameters Tk, and output Yk, a reinforcement learning model is applied to iteratively adjust a current approximating quantum circuit by actions decided by an agent (P 0063) obtaining an updated quantum circuit for the next iteration based on a reward function, obtaining an updated quantum circuit for the next iteration; calculating, by a training environment, for the next iteration, an updated observation matrix, representing the operation of the updated quantum circuit, and an updated reward function; providing the updated observation matrix and the updated reward function to the agent for the next iteration (P 0065-0068).
Claim 3. Prati and McKiernan discloses the method of claim 2, and Prati discloses one or more 2-qubits gate produce entanglement on the two input qubits (P 0059) and McKiernan discloses the logic entangles two or more qubits (P 0035) quantum comprise a set of two-qubit entangling gates for each distinct pair of qubits in the plurality of qubits (P 0160). Therefore, considering the teachings of Prati and McKiernan, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein an agent state Si is entangled with an output Yk output of another state Sk for some I ≠ k with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 4. Prati and McKiernan discloses the method of claim 2, Prati discloses one or more 2-qubits gate produce entanglement on the two input qubits (P 0059) and McKiernan discloses the logic entangles two or more qubits (P 0035) quantum comprise a set of two-qubit entangling gates for each distinct pair of qubits in the plurality of qubits (P 0160). Therefore, considering the teachings of Prati and McKiernan, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein an output Yi is entangled with Sl or another output Yk for some I ≠ k with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 5. Prati and McKiernan discloses the method of claim 2, and McKiernan discloses the QPU includes two, three, four or more quantum processor cells that can operate in parallel based on interactions with the controllers (P 0038) multiple instances of the quantum program can execute in parallel (P 0097) various operations of a quantum program synthesis process are executed in parallel (P 0115) two independently-operated QPUs can be operated independently of each other to execute two instances of a quantum program in parallel (P 0117). Therefore, considering the teachings of Prati and McKiernan, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine unrolling the quantum-enhanced learning agent in time, wherein a plurality of copies of the quantum-enhanced learning agent are simultaneously maintained, with each copy corresponding to an update of the quantum-enhanced learning agent with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 8. Prati and McKiernan discloses the method of claim 5, and McKiernan discloses the QPU includes two, three, four or more quantum processor cells that can operate in parallel based on interactions with the controllers (P 0038) multiple instances of the quantum program can execute in parallel (P 0097) various operations of a quantum program synthesis process are executed in parallel (P 0115) two independently-operated QPUs can be operated independently of each other to execute two instances of a quantum program in parallel (P 0117). Therefore, considering the teachings of Prati and McKiernan, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the unrolling is accomplished with quantum circuits with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 9. Prati and McKiernan discloses the method of claim 1, and Prati discloses using the quantum-enhanced learning agent to solve an optimization problem by constructing a solution that optimizes an objective function having discrete steps, the Deep Reinforced Learning is carried out by means of the Proximal Policy Optimization, PPO, algorithm (P 0074) and exploits deep neural networks to learn optimal policies in order to achieve specific goals in decision-making problems (P 0077).
Claim 10. Prati and McKiernan discloses the method of claim 9, and Prati discloses wherein solving the optimization problem comprises evaluating each step leading to a solution by measuring designated output bits, the Proximal Policy Optimization algorithm is used to approximate single qubit transformations (P 0142, 0143) and multi qubit operations (P 0156).
Claim 11. Prati and McKiernan discloses the method of claim 9, and Prati discloses wherein the solution is evaluated at the end of each iteration, a reinforcement learning model is applied to iteratively adjust a current approximating quantum circuit by actions decided by an agent (P 0063) obtaining an updated quantum circuit for the next iteration based on a reward function, obtaining an updated quantum circuit for the next iteration; calculating, by a training environment, for the next iteration, an updated observation matrix, representing the operation of the updated quantum circuit, and an updated reward function; providing the updated observation matrix and the updated reward function to the agent for the next iteration (P 0065-0068).
Claim 12. Prati and McKiernan discloses the method of claim 1, and Prati discloses using the quantum-enhanced learning agent to solve a combinatorial optimization problem, the Deep Reinforced Learning is carried out by means of the Proximal Policy Optimization, PPO, algorithm (P 0074) and exploits deep neural networks to learn optimal policies in order to achieve specific goals in decision-making problems (P 0077).
Claim 13. Prati and McKiernan discloses the method of claim 9, and Prati discloses wherein using the quantum-enhanced learning agent to solve the optimization problem comprises using the quantum-enhanced learning agent to produce a sequence of outputs as a destination node on a graph, comprising building the solution one component at a time, a reinforcement learning model is applied to iteratively adjust a current approximating quantum circuit by actions decided by an agent (P 0063) obtaining an updated quantum circuit for the next iteration based on a reward function, obtaining an updated quantum circuit for the next iteration; calculating, by a training environment, for the next iteration, an updated observation matrix, representing the operation of the updated quantum circuit, and an updated reward function; providing the updated observation matrix and the updated reward function to the agent for the next iteration (P 0065-0068) the Deep Reinforced Learning is carried out by means of the Proximal Policy Optimization, PPO, algorithm (P 0074) and exploits deep neural networks to learn optimal policies in order to achieve specific goals in decision-making problems (P 0077).
Claim 15. Prati and McKiernan discloses the method of claim 1, and Prati discloses using the quantum-enhanced learning agent for reinforcement learning, wherein the quantum-enhanced learning agent produces a sequence of outputs for each output Y associated with the action of the quantum-enhanced learning agent, a reinforcement learning model is applied to iteratively adjust a current approximating quantum circuit by actions decided by an agent (P 0063) obtaining an updated quantum circuit for the next iteration based on a reward function, obtaining an updated quantum circuit for the next iteration; calculating, by a training environment, for the next iteration, an updated observation matrix, representing the operation of the updated quantum circuit, and an updated reward function; providing the updated observation matrix and the updated reward function to the agent for the next iteration (P 0065-0068) to solve sequences of gates (P 0152).
Claim 17. Prati and McKiernan discloses the method of claim 1, and McKiernan discloses, the model can be used to efficiently train a model for the Traveling Salesperson Problem (P 0056). Therefore, considering the teachings of Prati and McKiernan, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine using the quantum-enhanced learning agent to solve a traveling salesperson problem with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 20 is directed to a hybrid quantum-classical computer system claim similar to the method claim of Claim 1 and is rejected with to the same rationale.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prati et al. (US 2024/0169237 A1) in view of McKiernan et al. (US 2023/0143652 A1) and further in view of Raussendorf et al. (US 2023/0042201 A1).
Claim 6. Prati and McKiernan discloses the method of claim 5, but do not disclose generating quantum correlations by entangling states of the multiple copies of the quantum-enhanced learning agent, as disclosed in the claims. However, in the same field of invention, Raussendorf discloses making correlated entangling measurements (P 0136) quantum states are entangled when the quantum states are quantum-mechanically correlated (P 0146) a two qubit deterministic entangling gate measures a correlated observable for two qubits (P 0259). Therefore, considering the teachings of Prati, McKiernan and Raussendorf, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine generating quantum correlations by entangling states of the multiple copies of the quantum-enhanced learning agent with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 7. Prati, McKiernan and Raussendorf disclose the method of claim 6, and Raussendorf discloses stabilizer are used provide a compact way to reference the corresponding stabilizer state, defined on n qubits by certain linear constraints for a compact definition of stabilizer states in terms of their stabilizer relations, for computationally efficiency (P 0152) for error correction (P 0153). Therefore, considering the teachings of Prati, McKiernan and Raussendorf, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein for n iterations unrolled, the method is applied to restricted quantum states such as n-qubit stabilizer states with the teachings of Prati, McKiernan and Raussendorf with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019) for computational efficiency (Raussendorf: P 0152).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prati et al. (US 2024/0169237 A1) in view of McKiernan et al. (US 2023/0143652 A1) and further in view of Ashrafi (US 2020/0050959 A1).
Claim 14. Prati and McKiernan discloses the method of claim 1, but do not disclose using the quantum-enhanced learning agent for timeseries analysis and forecasting, as disclosed in the claims. However, in the same field of invention, Ashrafi discloses using a quantum network to predict time-series from dynamic systems that uses nonlinear modeling and forecasting to AI that generates a nonlinear attractor reconstructions from the time-series data (P 0085). Therefore, considering the teachings of Prati, McKiernan and Ashrafi, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine using the quantum-enhanced learning agent for timeseries analysis and forecasting with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prati et al. (US 2024/0169237 A1) in view of McKiernan et al. (US 2023/0143652 A1) and further in view of Romanowsky et al. (US 2021/0390457 A1).
Claim 16. Prati and McKiernan discloses the method of claim 1, but Prati does not disclose using the quantum-enhanced learning agent for natural language processing NLP, wherein the quantum-enhanced learning agent receives a series of inputs based on words and produces a sequence of outputs, wherein each output is associated with an embedding, as disclosed in the claims. However, in the same field of invention, Romanowsky discloses using a learning model to process non-image data including natural language text or speech (P 0040) using quantum computing (P 0095). Therefore, considering the teachings of Prati, McKiernan and Romanowsky, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine using the quantum-enhanced learning agent for natural language processing NLP, wherein the quantum-enhanced learning agent receives a series of inputs based on words and produces a sequence of outputs, wherein each output is associated with an embedding with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019) to aid business enterprises in decision making (Romanowsky: P 0003).
Claim(s) 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Prati et al. (US 2024/0169237 A1) in view of McKiernan et al. (US 2023/0143652 A1) and further in view of Gould et al. (US 5,369,595).
Claim 18. Prati and McKiernan discloses the method of claim 9, but Prati does not disclose wherein the optimization problem comprises an optimization problem for optimum placement of chip components on a substrate, as disclosed in the claims. However, in the same field of invention, Gould discloses an automatic placement routine then generates an optimum placement of the books (circuits) on the chip image (C 7 L 57-59). Therefore, considering the teachings of Prati, McKiernan and Gould, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein the optimization problem comprises an optimization problem for optimum placement of chip components on a substrate with the teachings of Prati and McKiernan with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Claim 19. Prati, McKiernan and Gould discloses the method of claim 18, and Prati discloses the Deep Reinforced Learning is carried out by means of the Proximal Policy Optimization, PPO, algorithm (P 0074) and exploits deep neural networks to learn optimal policies in order to achieve specific goals in decision-making problems (P 0077) and Gould discloses an automatic placement routine then generates an optimum placement of the books (circuits) on the chip image (C 7 L 57-59). Therefore, considering the teachings of Prati, McKiernan and Gould, one having ordinary skill in the art before the effective filing date of the invention would have been motivated to combine wherein using the quantum-enhanced learning agent to solve the optimization problem comprises using the quantum-enhanced learning agent to build a chip placement optimization solution one step at a time with the teachings of Prati, McKiernan and Gould with the motivation to improve speed, efficiency and accuracy with which quantum resources are used to solve optimization problems (Prati: P 0019).
Conclusion
Any inquiry concerning this communication should be directed to JOHN M HEFFINGTON at telephone number (571)270-1696.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN M HEFFINGTON whose telephone number is (571)270-1696. The examiner can normally be reached on Monday through Friday from 9:30 am to 5:30 pm Eastern.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar B Paula, can be reached at telephone number 571-272-4128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center to authorized users only. Should you have questions about access to the USPTO patent electronic filing system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
/J.M.H/Examiner, Art Unit 2145 5/30/2026
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
Prosecution Insights