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 .
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, 7, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Qiskit Metal, as evidenced by Richard Pell/EENews, “Open source EDA tool simplifies quantum device design”, published March 29, 2021 (“Qiskit”), in view of US Pub. No. 2020/0226221 to Lu et al. (“Lu”).
As to independent claim 9, and similarly recited independent claims 1 and 15, an apparatus/method/CRM for quantum layout optimization (Qiskit teaches Qiskit Metal, an EDA tool inherently having CRM and memory for quantum device design.), the apparatus comprising: (Qiskit teaches beginning the design process with “parameters desired for a specific device’s Hamiltonian”. Qiskit identifies example desired Hamiltonian-related parameters, including “quibit frequency” and “qubit-qubit coupling”. Qiskit also states the user compares the quantum analysis result to the “target Hamiltonian”. See ¶ 3, 4); determining an initial quantum layout of the quantum device and initial geometric parameters of the initial quantum layout (Qiskit teaches that, after defining the desired Hamiltonian parameters, “[t]he user would then guess at a first layout”. Qiskit further explains this first layout is generated using Qiskit Metal’s component library, including pre-built or custom components. See ¶ 1, 3.); (Qiskit teaches Metal performs automatic classical analysis to determine electromagnetic properties quantum analysis to return information such as the device’s energy eigenspectrum. Qiskit states that user can compare the quantum analysis result to the target Hamiltonian and “tweak as needed”. Qiskit also describes “easy generation of different processor designs before settling on an optimal layout”. See ¶ 4, 7).
Qiskit does not explicitly teach “determining a target gradient of Hamiltonian parameters of the quantum device to geometric parameters of the initial quantum layout” and “adjusting the initial geometric parameters based on the target gradient”. Qiskit teaches comparing the quantum analysis result to a target Hamiltonian and manually or interactively tweaking the layout as needed, but Qiskit does not explicitly teach computing a gradient/sensitivity relating Hamiltonian parameters to geometric layout parameters and using that gradient to update the geometry.
Liu teaches missing gradient based optimization and computer apparatus implementation.
Lu teaches a system including a controller having processor and memory (¶ 0022, 0059, 0060). Therefore, to the extent Qiskit does not expressly describe the claimed memory/processor apparatus structure, Lu provides an express teaching of that conventional computer implementation.
Lu teaches determining how changes in structural parameters affect a desired performance objective. Lu states its physics simulator performs backpropagation “to determine the influence of structural parameters of the devices on the field response” (¶ 0013). Lu further teaches the loss gradient and field gradient are combined to “determine a structural gradient” and the structural gradient corresponds to “the influence of changes in the structural parameter on the loss value” (¶ 0016). Lu also identifies structural parameters as including “the location of a material boundary”, “device geometry”, and the like (¶ 0016).
Thus, Lu teaches computing a structural gradient that indicates how changes in device geometry affect the difference between an analyzed performance parameter and a desired performance value.
Lu further teaches generating a revised device description by updating structural parameters to reduce the loss value. Lu states iterative cycles may be performed using “an optimization scheme such as gradient descent to adjust the structural parameters” (¶ 0031). Lu further states the structural parameters are adjusted until the “difference between the performance parameter and the desired performance value is within a threshold range” (¶ 0031). Lu also states the revised description is generated by “optimizing the structural parameters to reduce the loss value” (¶ 0056).
Thus, Lu teaches using gradient based optimization, including gradient descent, to iteratively update device geometry until the analyzed performance parameter satisfies the desired target value.
It would have been obvious to a PHOSITA to modify Qiksit’s quantum device layout design process to include Lu’s gradient based structural parameter optimization. Qiskit already teaches a quantum layout workflow in which a user begins with desired Hamiltonian parameters, generates a first layout, performs a electromagnetic and quantum analysis, compares the anaylysis result to a target Hamiltonian, and tweaks the layout as needed. Lu teaches a known way to automate and improve sucha a phsycal device optimization process by computing a gradient indicating the influence of structural/geometric parameter changes on a loss value representing the difference between an actual parameter and a desired performance value, and then updating the geometry by gradient based optimization. Applying Lu’s technique to Qiksit would have predictably automated Qiksit’s layout tweaking step and reduced the manual effort required to move the analyzed Hamiltonian parameters toward the target Hamiltonian parameters.
As to claim 7, the method according to claim 1, wherein adjusting the initial geometric parameters based on the target gradient to have the Hamiltonian parameters of the quantum device be the target Hamiltonian parameters to obtain the target quantum layout (See mapping above.) comprises: determining an adjustment direction of the initial geometric parameters based on the target gradient (Lu teaches combining field gradient and loss gradient information to determine a “structural gradient”, where the structural parameters include device geometry and material boundary location. ¶ 0016. Because gradient descent uses the gradient to determine the direction in which parameters are adjusted to reduce the loss value, Lu teaches determining an adjustment direction based on the gradient.); and
adjusting the initial geometric parameters based on the adjustment direction multiple times to have the Hamiltonian parameters of the quantum device be the target Hamiltonian parameters to obtain the target quantum layout (Lu teaches “iterative cycles” are performed and that structural parameters are adjusted repeatedly until the difference between the performance parameter and desired performance value is within a threshold range. ¶ 0031, 0057.).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Qiskit in view of Lu and in further view of US Patent No. 10,572,816 to Vavilov et al. (“Vavilov”).
Qiskit in view Lu teach the limitations of claim 7 from which claim 8 depends. The combination, however, does not teach the quantum device comprises a Fluxonium quantum bit.
Vavilov teaches a quantum device comprises a Fluxonium quantum bit and states the “fluxonium qubit is a superconducting device that is formed using a small-area Josephson Junction shunted by a series of large-area Josephson junctions” and further teaches multi-qubit architectures including “two or more fluxonium qubits” (3:58-67, 6:10-14).
It would have been obvious to a PHOSITA to use the known Fluxonium qubit of Vavilov as the superconducting quantum device in the Qiskit/Lu optimization process because Qiskit is directed to designing superconducting quantum devices, and Vavilov teaches a known superconducting qubit architecture suitable for quantum information processing. Such a substitution would have predictably applied Qiskit/Lu’s layout and gradient optimization process to a known superconducting qubit type.
Allowable Subject Matter
Claims 2-6, 10-14, and 16-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 2-6, 10-14, and 16-20 would be allowable if rewritten in the manner above because the prior art of record does not teach or suggest a method, apparatus, or CRM having all the combinations of limitations recited in and required by claims 2 or similarly recited claims 10 and 16. Claims 3-6, 11-14, and 17-20 depend from claims 2, 10, and 16, respectively.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner SURESH MEMULA whose telephone number is (571)272-8046, and any inquiry for a formal Applicant initiated interview must be requested via a PTOL-413A form and faxed to the Examiner's personal fax phone number: (571) 273-8046. Furthermore, Applicant is invited to contact the Examiner via email (suresh.memula@uspto.gov) on the condition the communication is pursuant to and in accordance with MPEP §502.03 and §713.01. The Examiner can normally be reached Monday-Thursday: 9am-6pm. If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Jack Chiang, can be reached on 571-272-7483. The fax phone number for the organization where this application or proceeding is assigned (i.e., central fax phone number) is 571-273-8300.
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/SURESH MEMULA/Primary Examiner, Art Unit 2851
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