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 . This action is in response to an amendment filed on October 27th, 2025. Claims 1-20 are pending in the current application.
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, 8, 9, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (Herein referred to as Wu) (U.S. Patent Application No. US 20220351064 A1) in view of Abdelhafez et al. (Herein referred to as Abdelhafez) (Gradient-based optimal control of open quantum systems using quantum trajectories and automatic differentiation) in further view of Jiang et al. (Herein referred to as Jiang) (CIMAT: A Transpose SRAM-based Compute-In-Memory Architecture for Deep Neural Network On-Chip Training) and in further view of Qu et al. (Herein referred to as Qu) (A Portable Frequency Domain Electromagnetic System for Shallow Metal Targets Detection)
Regarding claim 1, Wu teaches A non-transitory computer-readable medium storing a set of instructions that is executable by at least one processor of an apparatus to cause the apparatus to perform a method, of parameter optimization for improving quantum chip performance ("According to an aspect of the embodiments of this application, a non-transitory computer-readable storage medium is provided, the computer-readable storage medium storing at least one instruction, and the at least one instruction, when executed by a processor of a computer device, causing the computer device to implement the foregoing quantum gate optimization method.", Paragraph 12) the method comprising: obtaining a quantum gate precision based on the second electromagnetic signal; ("FIG. 1 is a schematic architectural diagram of an experimental platform for a superconducting quantum computer... The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement... optimizations, and optimization is performed by using the actual measurement data as feedback, this application ensures the effect and precision of optimization. With reference to the foregoing two aspects, this application achieves a quantum gate optimization solution that improves both the precision and efficiency.", Paragraphs 39 and 40) (The measurement data is used to obtain a precision.) performing an operation on the quantum gate precision to obtain a gradient of a control parameter (“calculating a gradient corresponding to the control external field based on the actual measurement data and ideal data, the ideal data being used for reflecting an ideal characteristic of the quantum gate”, Paragraphs 9) wherein the chip parameter and the control parameter are configured to control the quantum chip to perform operations, (“The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement...” Paragraph 39) (An ADC is a chip, which implicitly has operational parameters such as frequency.) and updating the control parameter based on the gradient of the control parameter to improve quantum chi performance (“Step 240: Update the control external field according to the gradient to obtain an updated control external field, the updated control external field being applied to the qubit corresponding to the quantum gate to optimize the precision of the quantum gate.” Paragraph 72)
However Wu does not explicitly teach implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal; nor performing specifically a reverse differentiation operation on the quantum gate precision to obtain a gradient of a chip parameter AND a gradient of a control parameter nor the chip parameter is correlated with the structure of the quantum chip nor updating the chip parameter based on the gradient of the chip parameter to improve quantum chip performance.
Abdelhafez teaches performing a reverse-mode automatic differentiation operation (which encompasses reverse differentiation) to obtain a gradient of a chip parameter and a gradient of a control parameter. (“The goal is to determine the optimal control parameters that minimize a certain cost function… For the purpose of qubit readout, the circuit parameters are chosen such that the system is in the dispersive regime… Using Table II as the basic-operation set, we next define the computational graph (see Fig. 12). After the computational graph is run in forward direction (from x1 and x2 toward C), reverse-mode automatic differentiation identifies paths between C and each of x1 and x2. In this example, there is one path (the orange path) relating C to x2 while there are three paths (red paths) between C and x1. Gradients are then automatically calculated in a backward fashion by recursively multiplying the gradients in each path from bottom to top, then summing over all paths contributing to the same variable. Following this scheme, automatic differentiation will calculate the gradients in the reverse mode." Pg. 3, left column, third paragraph; Pg. 10, Last right column, last paragraph under “Problem overview”; Pg. 14, right column, last paragraph (See Table II below))
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of obtaining a precision and controlling a quantum chip, as disclosed in Wu, with the method of obtaining a gradient via reverse-mode automatic differentiation, as disclosed by Abdelhafez. One would be motivated to combine the two teachings, prior to the filing date of the current application, as this allows for automatic differentiation, which has a reverse mode that calculates gradients for any computation graph, (defined in terms of the building blocks of the differentiator) as disclosed in Abdelhafez, (“Gradients are then automatically calculated in a backward fashion by recursively multiplying the gradients in each path from bottom to top, then summing over all paths contributing to the same variable. Following this scheme, automatic differentiation will calculate the gradients in the reverse mode to be which are indeed the correct gradients. In this way, automatic differentiation allows for calculating gradients for any computational graph that is defined in terms of the building blocks of the differentiator.” Pgs. 14-15, last paragraph of 14 and first paragraph in 15.) and using automatic differentiation allows for “flexibility in optimization” and the ability to “suit the different constraints and diverse parameter regimes of real-life experiments” (Abstract of Abdelhafez)
However, Wu nor Abdelhafez teaches implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal nor the chip parameter is correlated with the structure of the quantum chip nor updating the chip parameter based on the gradient of the chip parameter to improve quantum chip performance.
Jiang teaches updating the chip parameter based on the gradient of the chip parameter. (“During the BP [backpropagation] process, the main goal is to calculate the gradient on the weights of each layer... All the weight gradients will be stored off-chip for weight update... Table I shows chip-level parameters including the hardware configuration, precision, area and energy for key circuit modules.", pg. 2, right column, paragraph 2; pg. 5, right column, paragraphs 3, and directly under “4.2 Chip Parameters” (See Jiang’s Table 1 below)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of controlling a quantum chip, as disclosed in Wu and modified by Abdelhafez, and the updating of chip parameters, as disclosed in Jiang. One would be motivated to combine the two teachings, prior to the filing date of the current application, as updating chip parameters with a gradient can improve benchmarks such as FPS [frames per seconds] and energy efficiency, as disclosed by Jiang. (pg. 6, right column, Table 2: Benchmark Results (See Jiang’s Table 2 below)
However, Wu, as modified by Abdelhafez and Jiang, does not explicitly teach implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal, nor the chip parameter is correlated with the structure of the quantum chip.
Qu teaches implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; (“The transmitter generates single frequency or multi-frequencies synthesized primary signal, establishing harmonic primary field around the target.”, pg. 2, under “2. System Design”; See Figure 3 for waveforms) (The transmitter coil, which has chip parameters such at inductance, generates a current which has a waveform associated with it.) measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal, (“The CEM-2 system includes 2 channel receiver systems, collecting reference signal from reference coil and secondary signal from differential connection between receiver coil and reference coil.”, pg. 3, under “2.2 Receiver System”) (The secondary signal corresponds to a second electromagnetic signal emitted by the structure of the quantum chip, responsive to a first signal.) and the chip parameter is correlated with the structure of the quantum chip. (“The physical parameters of transmitter coil are inductance L = 3 mH, resistance R = 1.765 Ω, resonance frequency f c = 55.45 kHz… Data acquisition module is the core of the receiver system. Eventually, 24-Bit, Sigma-Delta analog-digital converter (ADC) chips AD7763 are selected after comparison and screening. AD7763 made in Analog Devices works at a sampling rate of 78125 kHz. The dynamic range is approximately 120 dB. Bluetooth data transmission module using TI’s CC254X chip conveys operating parameters and control instructions from operator and transfers the processed data to PDA for display and storage. PDA is used to achieve human-computer interactions”, pg. 3, second paragraph; pg. 5, second paragraph)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of controlling a quantum chip, as disclosed in Wu, with the modifications as shown above of Abdelhafez and Jiang, and further modify it with the transmitter coils and signals of Qu. One would be motivated to combine the two teachings, prior to the filing date of the current application, as Qu’s method allows for metallic, magnetic, and ferrous objects to be distinguished from background. (“metallic objects with high conductivity and low relative permeability can be distinguished from background by quadrature response while magnetic and ferrous objects can be distinguished by in-phase response.”, pg. 8, bottom paragraph)
Regarding claim 9, Wu teaches an apparatus for parameter optimization for improving quantum chip performance comprising: a memory configured to store a set of instructions, (“...a memory, the memory storing at least one instruction…” Paragraph 11) one or more processors communicatively coupled to the memory and configured to execute the set of instructions, (“…a processor… at least one instruction, when executed by the processor, causing the computer device to implement the foregoing quantum gate optimization method.” Paragraph 11) obtain a quantum gate precision based on the second electromagnetic signal; ("FIG. 1 is a schematic architectural diagram of an experimental platform for a superconducting quantum computer... The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement...optimizations, and optimization is performed by using the actual measurement data as feedback, this application ensures the effect and precision of optimization. With reference to the foregoing two aspects, this application achieves a quantum gate optimization solution that improves both the precision and efficiency.", Paragraphs 39 and 40) (The measurement data is used to obtain a precision.) perform an operation on the quantum gate precision to obtain a gradient of a control parameter (For this claim, Wu’s measurement data or measurement system acts as our chip parameter and Wu’s control external field or control system acts as the control parameter) (“calculating a gradient corresponding to the control external field based on the actual measurement data and ideal data, the ideal data being used for reflecting an ideal characteristic of the quantum gate”, Paragraphs 9) wherein the chip parameter and the control parameter are configured to control the quantum chip to perform operations; (This is done in the following quotation by using the chip parameter (measurement system) and control parameter (control system) to provide control and measurement to a field programmable gate array (FPGA) and analog-to-digital converter (ADC) to produce analog waveforms which in turn control the quantum chip) (“The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement...” Paragraph 39) (An ADC is a chip, which implicitly has operational parameters such as frequency.) and update the control parameter based on the gradient of the control parameter to improve quantum chip performance. (“Step 240: Update the control external field according to the gradient to obtain an updated control external field, the updated control external field being applied to the qubit corresponding to the quantum gate to optimize the precision of the quantum gate.” Paragraph 72)
However Wu does not explicitly teach implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal; nor performing specifically a reverse differentiation operation to obtain a gradient of a chip parameter AND a gradient of a control parameter nor the chip parameter is correlated with the structure of the quantum chip nor updating the chip parameter based on the gradient of the chip parameter to improve quantum chip performance.
Abdelhafez teaches performing a reverse-mode automatic differentiation operation (which encompasses reverse differentiation) to obtain a gradient of a chip parameter and a gradient of a control parameter. (“The goal is to determine the optimal control parameters that minimize a certain cost function… For the purpose of qubit readout, the circuit parameters are chosen such that the system is in the dispersive regime… Using Table II as the basic-operation set, we next define the computational graph (see Fig. 12). After the computational graph is run in forward direction (from x1 and x2 toward C), reverse-mode automatic differentiation identifies paths between C and each of x1 and x2. In this example, there is one path (the orange path) relating C to x2 while there are three paths (red paths) between C and x1. Gradients are then automatically calculated in a backward fashion by recursively multiplying the gradients in each path from bottom to top, then summing over all paths contributing to the same variable. Following this scheme, automatic differentiation will calculate the gradients in the reverse mode." Pg. 3, left column, third paragraph; Pg. 10, Last right column, last paragraph under “Problem overview”; Pg. 14, right column, last paragraph (See Table II below))
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of obtaining a precision and controlling a quantum chip, as disclosed in Wu, with the method of obtaining a gradient via reverse-mode automatic differentiation, as disclosed by Abdelhafez. One would be motivated to combine the two teachings, prior to the filing date of the current application, as this allows for automatic differentiation, which has a reverse mode that calculates gradients for any computation graph, (defined in terms of the building blocks of the differentiator) as disclosed in Abdelhafez, (“Gradients are then automatically calculated in a backward fashion by recursively multiplying the gradients in each path from bottom to top, then summing over all paths contributing to the same variable. Following this scheme, automatic differentiation will calculate the gradients in the reverse mode to be which are indeed the correct gradients. In this way, automatic differentiation allows for calculating gradients for any computational graph that is defined in terms of the building blocks of the differentiator.” Pgs. 14-15, last paragraph of 14 and first paragraph in 15.) and using automatic differentiation allows for “flexibility in optimization” and the ability to “suit the different constraints and diverse parameter regimes of real-life experiments” (Abstract of Abdelhafez)
However, Wu nor Abdelhafez teaches implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal nor the chip parameter is correlated with the structure of the quantum chip nor updating the chip parameter based on the gradient of the chip parameter to improve quantum chip performance.
Jiang teaches updating the chip parameter based on the gradient of the chip parameter. (“During the BP [backpropagation] process, the main goal is to calculate the gradient on the weights of each layer...All the weight gradients will be stored off-chip for weight update...Table I shows chip-level parameters including the hardware configuration, precision, area and energy for key circuit modules.", pg. 2, right column, paragraph 2; pg. 5, right column, paragraphs 3, and directly under “4.2 Chip Parameters” (See Jiang’s Table 1 below)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of controlling a quantum chip, as disclosed in Wu and modified by Abdelhafez, and the updating of chip parameters, as disclosed in Jiang. One would be motivated to combine the two teachings, prior to the filing date of the current application, as updating chip parameters with a gradient can improve benchmarks such as FPS [frames per seconds] and energy efficiency, as disclosed by Jiang. (pg. 6, right column, Table 2: Benchmark Results (See Jiang’s Table 2 below)
However, Wu, as modified by Abdelhafez and Jiang, does not explicitly teach implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal, nor the chip parameter is correlated with the structure of the quantum chip.
Qu teaches implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; (“The transmitter generates single frequency or multi-frequencies synthesized primary signal, establishing harmonic primary field around the target.”, pg. 2, under “2. System Design”; See Figure 3 for waveforms) (The transmitter coil, which has chip parameters such at inductance, generates a current which has a waveform associated with it.) measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal, (“The CEM-2 system includes 2 channel receiver systems, collecting reference signal from reference coil and secondary signal from differential connection between receiver coil and reference coil.”, pg. 3, under “2.2 Receiver System”) (The secondary signal corresponds to a second electromagnetic signal emitted by the structure of the quantum chip, responsive to a first signal.) and the chip parameter is correlated with the structure of the quantum chip. (“The physical parameters of transmitter coil are inductance L = 3 mH, resistance R = 1.765 Ω, resonance frequency f c = 55.45 kHz… Data acquisition module is the core of the receiver system. Eventually, 24-Bit, Sigma-Delta analog-digital converter (ADC) chips AD7763 are selected after comparison and screening. AD7763 made in Analog Devices works at a sampling rate of 78125 kHz. The dynamic range is approximately 120 dB. Bluetooth data transmission module using TI’s CC254X chip conveys operating parameters and control instructions from operator and transfers the processed data to PDA for display and storage. PDA is used to achieve human-computer interactions”, pg. 3, second paragraph; pg. 5, second paragraph)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of controlling a quantum chip, as disclosed in Wu, with the modifications as shown above of Abdelhafez and Jiang, and further modify it with the transmitter coils and signals of Qu. One would be motivated to combine the two teachings, prior to the filing date of the current application, as Qu’s method allows for metallic, magnetic, and ferrous objects to be distinguished from background. (“metallic objects with high conductivity and low relative permeability can be distinguished from background by quadrature response while magnetic and ferrous objects can be distinguished by in-phase response.”, pg. 8, bottom paragraph)
Regarding claim 17, Wu teaches a computer-implemented method for parameter optimization, comprising: obtaining a quantum gate precision corresponding to a quantum chip; ("FIG. 1 is a schematic architectural diagram of an experimental platform for a superconducting quantum computer... The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement...optimizations, and optimization is performed by using the actual measurement data as feedback, this application ensures the effect and precision of optimization. With reference to the foregoing two aspects, this application achieves a quantum gate optimization solution that improves both the precision and efficiency.", Paragraphs 39 and 40) (The measurement data is used to obtain a precision.) performing an operation on the quantum gate precision to obtain a gradient of a chip parameter and a control parameter (For this claim, the measurement data or measurement system acts as our chip parameter and the control external field or control system acts as the control parameter) (“calculating a gradient corresponding to the control external field based on the actual measurement data and ideal data, the ideal data being used for reflecting an ideal characteristic of the quantum gate”, Paragraphs 9) wherein the chip parameter and the control parameter are configured to control the quantum chip to perform operations; (This is done in the following quotation by using the chip parameter (measurement system) and control parameter (control system) to provide control and measurement to a field programmable gate array (FPGA) and analog-to-digital converter (ADC) to produce analog waveforms which in turn control the quantum chip) (“The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement...” Paragraph 39) (An ADC is a chip, which implicitly has operational parameters such as frequency.) and updating the control parameter based on the gradient of the control parameter. (“Step 240: Update the control external field according to the gradient to obtain an updated control external field, the updated control external field being applied to the qubit corresponding to the quantum gate to optimize the precision of the quantum gate.” Paragraph 72)
However Wu does not explicitly teach implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal; nor performing specifically a reverse differentiation operation on the quantum gate precision to obtain a gradient of a chip parameter AND a gradient of a control parameter nor the chip parameter is correlated with the structure of the quantum chip nor updating the chip parameter based on the gradient of the chip parameter to improve quantum chip performance.
Abdelhafez teaches performing a reverse-mode automatic differentiation operation (which encompasses reverse differentiation) to obtain a gradient of a chip parameter and a gradient of a control parameter. (“The goal is to determine the optimal control parameters that minimize a certain cost function… For the purpose of qubit readout, the circuit parameters are chosen such that the system is in the dispersive regime… Using Table II as the basic-operation set, we next define the computational graph (see Fig. 12). After the computational graph is run in forward direction (from x1 and x2 toward C), reverse-mode automatic differentiation identifies paths between C and each of x1 and x2. In this example, there is one path (the orange path) relating C to x2 while there are three paths (red paths) between C and x1. Gradients are then automatically calculated in a backward fashion by recursively multiplying the gradients in each path from bottom to top, then summing over all paths contributing to the same variable. Following this scheme, automatic differentiation will calculate the gradients in the reverse mode." Pg. 3, left column, third paragraph; Pg. 10, Last right column, last paragraph under “Problem overview”; Pg. 14, right column, last paragraph (See Table II below))
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of obtaining a precision and controlling a quantum chip, as disclosed in Wu, with the method of obtaining a gradient via reverse-mode automatic differentiation, as disclosed by Abdelhafez. One would be motivated to combine the two teachings, prior to the filing date of the current application, as this allows for automatic differentiation, which has a reverse mode that calculates gradients for any computation graph, (defined in terms of the building blocks of the differentiator) as disclosed in Abdelhafez, (“Gradients are then automatically calculated in a backward fashion by recursively multiplying the gradients in each path from bottom to top, then summing over all paths contributing to the same variable. Following this scheme, automatic differentiation will calculate the gradients in the reverse mode to be which are indeed the correct gradients. In this way, automatic differentiation allows for calculating gradients for any computational graph that is defined in terms of the building blocks of the differentiator.” Pgs. 14-15, last paragraph of 14 and first paragraph in 15.) and using automatic differentiation allows for “flexibility in optimization” and the ability to “suit the different constraints and diverse parameter regimes of real-life experiments” (Abstract of Abdelhafez)
However, Wu nor Abdelhafez teaches implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal nor the chip parameter is correlated with the structure of the quantum chip nor updating the chip parameter based on the gradient of the chip parameter to improve quantum chip performance.
Jiang teaches updating the chip parameter based on the gradient of the chip parameter. (“During the BP [backpropagation] process, the main goal is to calculate the gradient on the weights of each layer...All the weight gradients will be stored off-chip for weight update...Table I shows chip-level parameters including the hardware configuration, precision, area and energy for key circuit modules.", pg. 2, right column, paragraph 2; pg. 5, right column, paragraphs 3, and directly under “4.2 Chip Parameters” (See Jiang’s Table 1 below)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of controlling a quantum chip, as disclosed in Wu and modified by Abdelhafez, and the updating of chip parameters, as disclosed in Jiang. One would be motivated to combine the two teachings, prior to the filing date of the current application, as updating chip parameters with a gradient can improve benchmarks such as FPS [frames per seconds] and energy efficiency, as disclosed by Jiang. (pg. 6, right column, Table 2: Benchmark Results (See Jiang’s Table 2 below)
However, Wu, as modified by Abdelhafez and Jiang, does not explicitly teach implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; nor measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal, nor the chip parameter is correlated with the structure of the quantum chip.
Qu teaches implementing a first electromagnetic signal generated by a waveform generator, at a structure of a quantum chip; (“The transmitter generates single frequency or multi-frequencies synthesized primary signal, establishing harmonic primary field around the target.”, pg. 2, under “2. System Design”; See Figure 3 for waveforms) (The transmitter coil, which has chip parameters such at inductance, generates a current which has a waveform associated with it.) measuring a second electromagnetic signal emitted by the structure of the quantum chip, wherein the second electromagnetic signal is responsive to the first electromagnetic signal, (“The CEM-2 system includes 2 channel receiver systems, collecting reference signal from reference coil and secondary signal from differential connection between receiver coil and reference coil.”, pg. 3, under “2.2 Receiver System”) (The secondary signal corresponds to a second electromagnetic signal emitted by the structure of the quantum chip, responsive to a first signal.) and the chip parameter is correlated with the structure of the quantum chip. (“The physical parameters of transmitter coil are inductance L = 3 mH, resistance R = 1.765 Ω, resonance frequency f c = 55.45 kHz… Data acquisition module is the core of the receiver system. Eventually, 24-Bit, Sigma-Delta analog-digital converter (ADC) chips AD7763 are selected after comparison and screening. AD7763 made in Analog Devices works at a sampling rate of 78125 kHz. The dynamic range is approximately 120 dB. Bluetooth data transmission module using TI’s CC254X chip conveys operating parameters and control instructions from operator and transfers the processed data to PDA for display and storage. PDA is used to achieve human-computer interactions”, pg. 3, second paragraph; pg. 5, second paragraph)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the method of controlling a quantum chip, as disclosed in Wu, with the modifications as shown above of Abdelhafez and Jiang, and further modify it with the transmitter coils and signals of Qu. One would be motivated to combine the two teachings, prior to the filing date of the current application, as Qu’s method allows for metallic, magnetic, and ferrous objects to be distinguished from background. (“metallic objects with high conductivity and low relative permeability can be distinguished from background by quadrature response while magnetic and ferrous objects can be distinguished by in-phase response.”, pg. 8, bottom paragraph)
Regarding claims 8 and 16, Wu, as modified by Abdelhafez and Jiang, teaches the non-transitory computer-readable medium/apparatus/method of claims 1, 9, and 17 respectively, as well as wherein the quantum gate precision is configured to identify performance of the quantum chip (“The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement… an ideal gate operator may be equivalent to a time evolution operator that completely depends on a control external field. Therefore, optimization of a quantum gate may be equivalent to optimization of the control external field corresponding to the quantum gate… In the embodiments of this application, a plurality of iterative updates are performed on the control external field to optimize the control external field, and the optimized control external field is applied to the qubit corresponding to the quantum gate to optimize precision of the quantum gate.” Paragraphs 39, 40, and 47 (Wu)) the method further comprises: in response to obtaining the quantum gate precision corresponding to the quantum chip, performing multiple reverse differentiation operations on the quantum gate precision to obtain a high-order derivative of the chip parameter and a high-order derivative of the control parameter (Table II and "After completion of the forward path, reverse-mode automatic differentiation is utilized to trace backward all paths relating the cost function to the inputs. This involves properly summing the stored partial derivatives of each path, generated by a recursive chain rule." pg. 14, right column, step 4 (Abdelhafez) (See Abdelhafez’s Table II below)) and evaluating performance robustness of a quantum gate corresponding to the quantum gate precision based on the high-order derivative of the chip parameter and the high-order derivative of the control parameter. (“Our optimizer harnesses automatic differentiation to provide flexibility in optimization and to suit the different constraints and diverse parameter regimes of real-life experiments.” pg. 1, Abstract (Abdelhafez))
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177
329
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Abdelhafez’s Table II
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447
321
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Jiang’s Table 1
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216
314
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Jiang’s Table 2
Claims 2, 3, 4, 6, 7, 10, 11, 12, 14, 15, 18, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (Herein referred to as Wu) (U.S. Patent Application No. US 20220351064 A1) in view of Abdelhafez et al. (Herein referred to as Abdelhafez) in further view of Jiang et al. (Herein referred to as Jiang) (CIMAT: A Transpose SRAM-based Compute-In-Memory Architecture for Deep Neural Network On-Chip Training) in further view of Qu et al. (Herein referred to as Qu) (A Portable Frequency Domain Electromagnetic System for Shallow Metal Targets Detection) and in even further view of Magesan et al. (Herein referred to as Magesan (NPL)
Regarding claims 2, 10, and 18, Wu, as modified by Abdelhafez, Jiang, and Qu teaches the non-transitory computer-readable medium/apparatus/method of claims 1, 9, and 17 respectively, but does not explicitly teach obtaining an actual quantum gate generated by the quantum chip and a theoretical quantum gate corresponding to the actual quantum gate, determining a degree of matching between the actual quantum gate and the theoretical quantum gate, nor determining the quantum gate precision based on the degree of matching.
Magesan teaches obtaining an actual quantum gate generated by the quantum chip and a theoretical quantum gate corresponding to the actual quantum gate, (“Ideal gate U" (theoretical quantum gate), "noisy quantum operation E" (actual quantum gate) pg. 1, left column, paragraph 1 under “Introduction”) determining a degree of matching between the actual quantum gate and the theoretical quantum gate, ("The gate fidelity is an experimentally relevant measure of how close E and U are given the input state ρ” pg. 1, left column, paragraph 2) and determining the quantum gate precision based on the degree of matching. (“The gate fidelity is an experimentally relevant measure of how close E and U are given the input state ρ. Often one wants to remove this state-dependence because understanding quantum noise and designing error-resistant devices requires state-independent characterizations of the noise. One method for obtaining a state independent quantity from the gate fidelity is to average it over all [pure] input states. This average gate fidelity, F, provides a concise, useful measure of error.” pg. 1, left column, paragraph 2)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the non-transitory computer-readable medium/apparatus/method of Wu, as modified by Abdelhafez and Jiang, with the measure of gate fidelity, as disclosed in Magesan. One of ordinary skill in the art would be motivated to combine the two teachings prior to the filing date of the current application, as gate fidelity provides a concise, useful measure of error, as disclosures in Magesan. (“This average gate fidelity, F, provides a concise, useful measure of error.” pg. 1, left column, bottom of paragraph 2)
Regarding claims 3, 11, and 19, Wu, as modified by Abdelhafez, Jiang, Qu and Magesan, teaches the non-transitory computer-readable medium/apparatus/method of claims 2, 10, and 18 respectively, as well as obtaining the chip parameter and the control parameter (“obtaining an initialized control external field corresponding to a quantum gate; acquiring actual measurement data of the quantum gate, the actual measurement data being used for reflecting an actual characteristic of the quantum gate;”, Paragraphs 7 and 8 (Wu)) and controlling, based on the chip parameter and the control parameter, the quantum chip to perform an operation to obtain the actual quantum gate. (“The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement… An ideal gate operator may be equivalent to a time evolution operator that completely depends on a control external field...because the actual measurement data of the quantum gate is acquired during optimizations, and optimization is performed by using the actual measurement data as feedback, this application ensures the effect and precision of optimization.” Paragraphs 39 and 40 (Wu))
Regarding claims 4, 12, and 20, Wu, as modified by Abdelhafez, Jiang, Qu and Magesan, teaches the non-transitory computer-readable medium/apparatus/method of claims 3, 13, and 19 respectively, as well as, generating a Hamiltonian based on the chip parameter and the control parameter; (“Control external field: With regard to the SC qubit, a control external field generally refers to a microwave drive applied to a qubit. A microwave applied to a diagonal element part of a bit Hamiltonian is referred to as a control external field in a z direction. A microwave applied to a non-diagonal element part of the bit Hamiltonian is referred to as a control external field in an xy direction... when a control external field in a z direction is applied, a resonance environment can be created for the two bits… Under such a control external field, the two-bit system can also be reduced to a 5-level status for processing... In a simplified 5-level Hilbert space the Hamiltonian of the two-bit quantum system can be expressed…” Paragraphs 36, 83, 84, and 85 (See paragraphs 83, 84, and 85 in the excerpt below (Wu)) and controlling, based on the Hamiltonian and the control parameter, the quantum chip to perform the operation to obtain the actual quantum gate. (“The quantum chip 15 is controlled by analog waveforms, and therefore, requires a measurement and control system 12 mainly including a field programmable gate array (FPGA) and an analog-to-digital converter (ADC)/digital-to-analog converter (DAC) to provide control and measurement…However, when a control external field in a z direction is applied, a resonance environment can be created for the two bits… Under such a control external field, the two-bit system can also be reduced to a 5-level status for processing... In a simplified 5-level Hilbert space the Hamiltonian of the two-bit quantum system can be expressed…” Paragraph 39 (Also see paragraphs 83, 84, and 85 in the excerpts below) (Wu))
Regarding claims 6 and 14, Wu, as modified by Abdelhafez, Jiang, Qu and Magesan, teaches the non-transitory computer-readable medium/apparatus of claims 4 and 12 respectively, as well as, the control parameter comprises a waveform configured to control the quantum chip to perform the operation. (“In the embodiments of this application, a control external field refers to a microwave drive applied to a qubit corresponding to a quantum gate, and the control external field may be a pulse waveform. The initialized control external field may be selected based on experience or experiments.”, Paragraph 46 (Wu))
Regarding claims 7 and 15, Wu, as modified by Abdelhafez, Jiang, Qu and Magesan, teaches the non-transitory computer-readable medium/apparatus of claims 4 and 12 respectively, as well as, the gradient of the control parameter is correlated to the Hamiltonian. (“In addition, in some other embodiments, considering that there are some determinate errors in a real quantum system, a more complex model can be established, to take specific forms of the actual error effects (such as the filtering effect) into consideration in the Hamiltonian of the system, to obtain a more precise gradient.” Paragraph 160 (Wu))
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Excerpts from Wu’s disclosure
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over WU et al. (Herein referred to as Wu) (U.S. Patent Application No. US 20220351064 A1) in view of Abdelhafez et al. (Herein referred to as Abdelhafez) in further view of Jiang et al. (Herein referred to as Jiang) (CIMAT: A Transpose SRAM-based Compute-In-Memory Architecture for Deep Neural Network On-Chip Training) in further view of Qu et al. (Herein referred to as Qu) (A Portable Frequency Domain Electromagnetic System for Shallow Metal Targets Detection) in further view of Magesan et al. (Herein referred to as Magesan (NPL) and in even further view of Allan Widom. (Herein referred to as Widom) (Quantum electrodynamics of a Josephson-junction capacitor)
Regarding claims 5 and 13, Wang, as modified by Abdelhafez, Jiang, Qu and Magesan, teaches the non-transitory computer-readable medium/apparatus of claims 4 and 12 respectively, but they do not explicitly teach at least one of a capacitance corresponding to the quantum chip, an inductance corresponding to the quantum chip, OR a parameter corresponding to the quantum chip and used for characterizing energy stored in a Josephson junction.
Widom teaches at least one of a capacitance corresponding to the quantum chip, an inductance corresponding to the quantum chip, OR a parameter corresponding to the quantum chip and used for characterizing energy stored in a Josephson junction. (“In Sec. I it will be argued that the photon propagator when represented in a gauge-invariant form becomes a frequency-dependent mutual inductance function. In Secs. II and III it is shown that multiple photon-scattering processes can be represented via electrical engineering admittance matrices. In particular, for a SQUID [Superconducting Quantum Interference Device] coupled by mutual inductances to external circuitry, it is possible by "electrical feedback theorems" to measure the frequency-dependent capacitance of the Josephson junction in the SQUID.", pg. 1, left and right columns, bottom paragraph of the left column and top paragraph of the right column.)
Therefore, it would have been considered obvious to one of ordinary skill in the art, prior to the current application’s filing date, to combine the non-transitory computer-readable medium/apparatus/method of Wu, as modified by Abdelhafez and Magesan, with the electrodynamics of Widom. One of ordinary skill in the art would be motivated to combine the two teachings, prior to the current application’s filing date, as Widom’s method focuses on showing and solving propagations of stable localized “energy disturbances” (“In recent years there has been considerable interest in classical nonlinear wave equations which have solutions corresponding to the propagation of stable localized "energy disturbances." The sine-Gordon nonlinear quantum field theory has recently served as a mathematical model system by which one might hope to understand the instanton flash for the purpose of obtaining some insight into relativistic gauge-field theories. Fortunately, it is possible to find in the laboratory the "spectrum of elementary particles" produced by sine-Gordon quantum field theories in one or two spatial dimensions via the electrical properties of the superconducting quantum interference device (SQUID). The purpose of this work is to show how this might be done.”, pg. 1, left column, first and second paragraphs)
Response to Arguments
Applicant's arguments filed on October 27th, 2025, have been fully considered but they are not fully persuasive. The applicant’s arguments have overcome the 35 U.S.C. 101 rejection and as such the rejections have been withdrawn.
The applicant argues in substance:
Argument 1: Wu does not teach “the chip parameter and the control parameter are configured to control the quantum chip to perform operations."
The examiner respectfully disagrees. The control external field corresponds to a control parameter and an analog-digital converter (ADC) is a type of chip used to convert real-world analog signals to digital data, and implicitly has operating parameters such as frequency.
Argument 2: Jiang does not teach “updating the chip parameter based on the gradient of the chip parameter”, nor does Jiang pertain to quantum computing
The examiner respectfully disagrees. According to Jiang’s Table 1, the updated weights of Jiang are considered to be chip-level parameters, which under the broadest reasonable interpretation, is a chip parameter. (See also “4.3 Benchmark Results and Discussion” for more evidence. “…the chip area are evaluated for FF (Eq. 1), BP (Eq. 2-3) and weight update (Eq. 4), respectively.”) Gradients are calculated on the weights and the weights are updated based on the gradient calculation. While Jiang’s disclosure doesn’t explicitly mention quantum computing, the components within the disclosure, such as the ADC, are configured to do quantum computing. Furthermore, the term “quantum chip” is interpreted to be, under the broadest reasonable interpretation, A traditional chip used to simulate a chip performed quantum computing. Jiang teaches a chip capable of simulating quantum computing, teaching the “quantum chip”.
Argument 3: The combination of Wu, Abdelhafez and Jiang does not teach the newly amended limitations.
Applicant’s arguments with respect to claim(s) 1, 9, and 17 have been considered but are moot because the new ground of rejection.
Conclusion
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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/T.E.I./ Patent Examiner, Art Unit 2122
/KAKALI CHAKI/ Supervisory Patent Examiner, Art Unit 2122