DETAILED ACTION
1. This communication is in response to the amendments filed on August 18, 2026 for Application No. 18/378,938 in which Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
3. The amendments filed on August 18, 2026 have been considered. Claims 1, 7, 9, and 15 have been amended. Thus, Claims 1-20 are pending and presented for examination.
4. Applicant's arguments filed August 18, 2026 with respect to the claim objection of claim 9 have been fully considered but they are not persuasive. Claim 9 still incorrectly recites the trapped ion as “Mg+” when it should instead be corrected to read “Mg+” (the charge being indicated by superscript) – this is similarly supported/shown by Applicant’s arguments on Pg. 8 of filed Arguments/Remarks which similarly recites the trapped ion as “Mg+”. Thus, the objection is maintained until the appropriate corrections are made.
5. Applicant’s arguments and corresponding amendments filed August 18, 2026 with respect to the 35 U.S.C. 101 signals per se rejection of Claims 15-20 have been fully considered and are persuasive. Thus, the 35 U.S.C. 101 signals per se rejection of Claims 15-20 has been withdrawn.
6. Applicant’s arguments filed August 18, 2026 with respect to the 35 U.S.C. 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Note: Examiner has introduced Arrazola et al. (“Universal Quantum Circuits for Quantum Chemistry”) as necessitated by amendment, to teach the newly added limitations of the Independent claims – see the updated 35 U.S.C. 103 rejection below.
7. With respect to the nonstatutory double patenting rejection, Applicant’s deferment of response and right to submit a terminal disclaimer filed August 18, 2026 is acknowledged by the Examiner. The claims, as currently drafted, are still rejected on the ground of nonstatutory double patenting and the rejection has been updated in the subsequent section below, as necessitated by amendment.
Claim Objections
8. Claim 9 is objected to because of the following informalities:
Claim 9 recites a plurality of trapped ions wherein each of the trapped ions is one selected from Be+, Ca+, Sr+, Mg+, Ba+, Zn+, Hg+, Cd+. However, the ion “Mg+” should instead be corrected to read “Mg+” to properly indicate the charge of the ion, similar to the others recited.
Appropriate correction is required.
Double Patenting
9. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
10. Claims 1, 7-9, and 15 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1, 8-10, and 16 of copending Application No. 18/423,208 in view of Arrazola et al. (hereinafter Arrazola) (“Universal Quantum Circuits for Quantum Chemistry”). Although the claims at issue are not identical they are not patentably distinct from each other because the subject matter claimed in the instant application is disclosed in copending Application No. 18/423,208 since the instant application claims common subject matter.
This is a provisional nonstatutory double patenting rejection.
The bolded portions below highlight the differences between the instant application and the copending application, which illustrates the obvious and anticipatory relationship of the claim limitations at issue:
Instant Application (18/378,938)
Copending Application (18/423,208)
Claim 1: A method of performing computation in a hybrid quantum-classical computing system comprising a classical computer and a quantum processor, comprising:
mapping, by a classical computer, an objective function of an optimization problem to a model Hamiltonian;
selecting, by the classical computer, a set of variational parameters to construct a parametrized quantum circuit comprising an entangling circuit based on the model Hamiltonian and a mixing circuit;
setting, by a system controller, a quantum processor in an initial state, wherein the quantum processor comprises a plurality of trapped ions, each of which has two hyperfine states defining a qubit;
executing iterations, each iteration comprising:
applying, by the system controller, the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state;
measuring, by the system controller, an expectation value of the model Hamiltonian; and
replacing, by the classical computer, the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in the previous iteration is more than a predetermined value; and
outputting the set of the variational parameters,
wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and
the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state.
Claim 1: A method of performing computation in a hybrid quantum-classical computing system comprising a classical computer and a quantum processor, comprising:
computing, by a classical computer, an approximate cost function of an optimization problem with variables constrained by an inequality, wherein the inequality constraint is included using a polynomial approximation of a Heaviside step function;
mapping, by the classical computer, the approximate cost function of the optimization problem to a model Hamiltonian to be implemented on a quantum processor comprising a plurality of trapped ions, each of which has two hyperfine states defining a qubit;
selecting, by the classical computer, a set of variational parameters to construct a parametrized quantum circuit comprising an entangling circuit based on the model Hamiltonian and a mixing circuit;
setting, by a system controller, the quantum processor in an initial state;
[see underlined portion of copending claim 1 above, which discloses the same limitation as the instant application]
executing one or more iterations, each iteration comprising:
applying, by the system controller, the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state;
measuring, by the system controller, an expectation value of the model Hamiltonian; and
replacing, by the classical computer, the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in a previous iteration is more than a predetermined value; and
outputting, by the classical computer, the set of the variational parameters after executing the one or more iterations.
Copending Application 18/423,208 discloses the same limitations as the instant application except that the copending application additionally discloses the cost/objective function being constrained by an inequality, wherein the inequality constraint is included using a polynomial approximation of a Heaviside step function. In comparison, the instant claim broadly/generally discloses an objective/cost function but does not detail any further constraints – hence, this part of the instant claim is anticipated by the copending claim.
Further, the instant application additionally specifies “wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state” where the copending claims are seemingly silent on these particular limitations, as indicated by the preceding mapping. However, Arrazola et al. (“Universal Quantum Circuits for Quantum Chemistry”) teaches wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner rep resentation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number.” & Pg. 3, “It is therefore convenient to work with a set of quantum gates that create superpositions between the original and the excited state. In the simplest non-trivial case of a single particle and two qubits, these correspond to gates that perform arbitrary U(2) rotations between the states |10⟩,|01⟩ while leaving other basis states unchanged”, thus, the initial state and trial state are a superposition of states where the number of trapped ions in the hyperfine excited state is constant (preserved Hamming weight and therefore particle number)), and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state (Arrazola, Pg. 1, Abstract, “In this work, we show that controlled single-excitation gates in the form of Givens rotations are universal for particle-conserving unitaries. Single excitation gates describe an arbitrary U(2) rotation on the two-qubit subspace spanned by the states |01⟩,|10⟩, while leaving other states unchanged– a transformation that is analogous to a single-qubit rotation on a dual-rail qubit. The proof is constructive, so our result also provides an explicit method for compiling arbitrary particle-conserving unitaries. Additionally, we describe a method for using controlled single-excitation gates to prepare an arbitrary state of a fixed number of particles. We derive analytical gradient formulas for Givens rotations as well as decompositions into single qubit and CNOT gates. Our results offer a unifying framework for quantum computational chemistry where every algorithm is a unique recipe built from the same universal ingredients: Givens rotations.”, therefore, the quantum circuit presented by Arrazola teaches the mixing circuit comprising one or more Givens rotation gates (which function as such a mixer), where the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (particle-conserving unitaries, as also supported by Pg. 1 which states “To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number”)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the hybrid quantum-classical computing system, as disclosed by copending Application 18/423,208 to include wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state, as disclosed by Arrazola. One of ordinary skill in the art would have been motivated to make this modification to enable native preservation of constraints on quantum hardware and the use of one or more Givens rotation gates which preserves hard constraints, such as a fixed hamming weight or particle number, while allowing the quantum state to explore valid solutions, hence improving system accuracy while reducing the use of computational resources (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner representation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number. […] In this work, we provide such a framework by giving a constructive proof that controlled single excitation gates are universal for particle-conserving unitaries.”)
Claim 7: A hybrid quantum-classical computing system, comprising:
a quantum processor comprising a plurality of trapped ions, each of the trapped ions having two hyperfine states defining a qubit;
one or more lasers configured to emit a laser beam, which is provided to trapped ions in the quantum processor; and
a classical computer configured to:
map an objective function of an optimization problem to a model Hamiltonian;
select a set of variational parameters to construct a parametrized quantum circuit comprising an entangling circuit based on the model Hamiltonian and a mixing circuit;
control a system controller to the quantum processor in an initial state;
execute iterations, each iteration comprising:
controlling the system controller to apply the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state;
controlling by the system controller to measure an expectation value of the model Hamiltonian; and
replacing the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in the previous iteration is more than a predetermined value; and
output the set of the variational parameters,
wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and
the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state.
Claim 8: A hybrid quantum-classical computing system, comprising:
a quantum processor comprising a plurality of trapped ions, each of the trapped ions having two hyperfine states defining a qubit;
one or more lasers configured to emit a laser beam, which is provided to trapped ions in the quantum processor; and
a classical computer configured to:
compute an approximate cost function of an optimization problem with variables constrained by an inequality, wherein the inequality constraint is included using a polynomial approximation of a Heaviside step function;
map the approximate cost function of the optimization problem to a model Hamiltonian to be implemented on the quantum processor;
select a set of variational parameters to construct a parametrized quantum circuit comprising an entangling circuit based on the model Hamiltonian and a mixing circuit;
control a system controller to set the quantum processor in an initial state;
execute one or more iterations, each iteration comprising:
control the system controller to apply the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state;
control the system controller to measure an expectation value of the model Hamiltonian; and
replace the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in a previous iteration is more than a predetermined value; and
output the set of the variational parameters after executing the one or more iterations.
Application 18/423,208 discloses the same limitations as the instant application except that the copending application additionally discloses the cost/objective function being constrained by an inequality, wherein the inequality constraint is included using a polynomial approximation of a Heaviside step function. In comparison, the instant claim broadly/generally discloses an objective/cost function but does not detail any further constraints – hence, this part of the instant claim is anticipated by the copending claim.
Further, the instant application additionally specifies “wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state” where the copending claims are seemingly silent on these particular limitations, as indicated by the preceding mapping. However, Arrazola et al. (“Universal Quantum Circuits for Quantum Chemistry”) teaches wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner representation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number.” & Pg. 3, “It is therefore convenient to work with a set of quantum gates that create superpositions between the original and the excited state. In the simplest non-trivial case of a single particle and two qubits, these correspond to gates that perform arbitrary U(2) rotations between the states |10⟩,|01⟩ while leaving other basis states unchanged”, thus, the initial state and trial state are a superposition of states where the number of trapped ions in the hyperfine excited state is constant (preserved Hamming weight and therefore particle number)), and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state (Arrazola, Pg. 1, Abstract, “In this work, we show that controlled single-excitation gates in the form of Givens rotations are universal for particle-conserving unitaries. Single excitation gates describe an arbitrary U(2) rotation on the two-qubit subspace spanned by the states |01⟩,|10⟩, while leaving other states unchanged– a transformation that is analogous to a single-qubit rotation on a dual-rail qubit. The proof is constructive, so our result also provides an explicit method for compiling arbitrary particle-conserving unitaries. Additionally, we describe a method for using controlled single-excitation gates to prepare an arbitrary state of a fixed number of particles. We derive analytical gradient formulas for Givens rotations as well as decompositions into single qubit and CNOT gates. Our results offer a unifying framework for quantum computational chemistry where every algorithm is a unique recipe built from the same universal ingredients: Givens rotations.”, therefore, the quantum circuit presented by Arrazola teaches the mixing circuit comprising one or more Givens rotation gates (which function as such a mixer), where the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (particle-conserving unitaries, as also supported by Pg. 1 which states “To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number”)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the hybrid quantum-classical computing system, as disclosed by copending Application 18/423,208 to include wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state, as disclosed by Arrazola. One of ordinary skill in the art would have been motivated to make this modification to enable native preservation of constraints on quantum hardware and the use of one or more Givens rotation gates which preserves hard constraints, such as a fixed hamming weight or particle number, while allowing the quantum state to explore valid solutions, hence improving system accuracy while reducing the use of computational resources (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner representation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number. […] In this work, we provide such a framework by giving a constructive proof that controlled single excitation gates are universal for particle-conserving unitaries.”)
Claim 8: The hybrid quantum-classical computing system of claim 7, wherein each of the trapped ions is
Y
b
+
171
having
S
1
/
2
2
hyperfine states.
Claim 9: The hybrid quantum-classical computing system of claim 8, wherein each of the trapped ions is
Y
b
+
171
having
S
1
/
2
2
hyperfine states.
Claim 9: The hybrid quantum-classical computing system of claim 7, wherein each of the trapped ions is one selected from
Be+, Ca+, Sr+, Mg+, Ba+, Zn+, Hg+, Cd+.
Claim 10: The hybrid quantum-classical computing system of claim 8, wherein
each of the trapped ions is one selected from
Be+, Ca+, Sr+, Mg+, Ba+, Zn+, Hg+, Cd+.
Claim 15: A hybrid quantum-classical computing system comprising non-volatile memory having a number of instructions stored therein which, when executed by one or more processors, causes the hybrid quantum-classical computing system to perform operations comprising:
mapping, by a classical computer, an objective function of an optimization problem to a model Hamiltonian;
selecting, by the classical computer, a set of variational parameters to construct a parametrized quantum circuit comprising an entangling circuit based on the model Hamiltonian and a mixing circuit;
setting, by a system controller, a quantum processor in an initial state, wherein the quantum processor comprises a plurality of trapped ions, each of which has two hyperfine states defining a qubit;
executing iterations, each iteration comprising:
applying, by the system controller, the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state; measuring, by the system controller, an expectation value of the model Hamiltonian; and
replacing, by the classical computer, the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in the previous iteration is more than a predetermined value; and
outputting the set of the variational parameters,
wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and
the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state.
Claim 16: A hybrid quantum-classical computing system comprising non-volatile memory having a number of instructions stored therein which, when executed by one or more processors, causes the hybrid quantum-classical computing system to perform operations comprising:
computing, by a classical computer, an approximate cost function of an optimization problem with variables constrained by an inequality, wherein the inequality constraint is included using a polynomial approximation of a Heaviside step function;
mapping, by the classical computer, the approximate cost function of the optimization problem to a model Hamiltonian to be implemented on a quantum processor comprising a plurality of trapped ions, each of which has two hyperfine states defining a qubit;
selecting, by the classical computer, a set of variational parameters to construct a parametrized quantum circuit comprising an entangling circuit based on the model Hamiltonian and a mixing circuit;
setting, by a system controller, the quantum processor in an initial state;
[see underlined portion of copending claim 16 above, which discloses the same limitation as the instant application]
executing one or more iterations, each iteration comprising:
applying, by the system controller, the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state;
measuring, by the system controller, an expectation value of the model Hamiltonian; and
replacing, by the classical computer, the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in a previous iteration is more than a predetermined value; and
outputting, by the classical computer, the set of the variational parameters after executing the one or more iterations.
Application 18/423,208 discloses the same limitations as the instant application except that the copending application additionally discloses the cost/objective function being constrained by an inequality, wherein the inequality constraint is included using a polynomial approximation of a Heaviside step function. In comparison, the instant claim broadly/generally discloses an objective/cost function but does not detail any further constraints – hence, this part of the instant claim is anticipated by the copending claim.
Further, the instant application additionally specifies “wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state” where the copending claims are seemingly silent on these particular limitations, as indicated by the preceding mapping. However, Arrazola et al. (“Universal Quantum Circuits for Quantum Chemistry”) teaches wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner rep resentation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number.” & Pg. 3, “It is therefore convenient to work with a set of quantum gates that create superpositions between the original and the excited state. In the simplest non-trivial case of a single particle and two qubits, these correspond to gates that perform arbitrary U(2) rotations between the states |10⟩,|01⟩ while leaving other basis states unchanged”, thus, the initial state and trial state are a superposition of states where the number of trapped ions in the hyperfine excited state is constant (preserved Hamming weight and therefore particle number)), and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state (Arrazola, Pg. 1, Abstract, “In this work, we show that controlled single-excitation gates in the form of Givens rotations are universal for particle-conserving unitaries. Single excitation gates describe an arbitrary U(2) rotation on the two-qubit subspace spanned by the states |01⟩,|10⟩, while leaving other states unchanged– a transformation that is analogous to a single-qubit rotation on a dual-rail qubit. The proof is constructive, so our result also provides an explicit method for compiling arbitrary particle-conserving unitaries. Additionally, we describe a method for using controlled single-excitation gates to prepare an arbitrary state of a fixed number of particles. We derive analytical gradient formulas for Givens rotations as well as decompositions into single qubit and CNOT gates. Our results offer a unifying framework for quantum computational chemistry where every algorithm is a unique recipe built from the same universal ingredients: Givens rotations.”, therefore, the quantum circuit presented by Arrazola teaches the mixing circuit comprising one or more Givens rotation gates (which function as such a mixer), where the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (particle-conserving unitaries, as also supported by Pg. 1 which states “To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number”)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the hybrid quantum-classical computing system, as disclosed by copending Application 18/423,208 to include wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, and the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state, as disclosed by Arrazola.. One of ordinary skill in the art would have been motivated to make this modification to enable native preservation of constraints on quantum hardware and the use of one or more Givens rotation gates which preserves hard constraints, such as a fixed hamming weight or particle number, while allowing the quantum state to explore valid solutions, hence improving system accuracy while reducing the use of computational resources (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner rep resentation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number. […] In this work, we provide such a framework by giving a constructive proof that controlled single excitation gates are universal for particle-conserving unitaries.”)
Claim Rejections - 35 USC § 103
11. 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.
12. Claims 1, 3-8, 11-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Niroula et al. (hereinafter Niroula) (“Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer”), in view of Wang et al. (hereinafter Wang) (US PG-PUB 20230153373), further in view of Arrazola et al. (hereinafter Arrazola) (“Universal Quantum Circuits for Quantum Chemistry”).
Regarding Claim 1, Niroula teaches a method of performing computation in a hybrid quantum-classical computing system comprising a classical computer and a quantum processor (Niroula, Pg. 2, “In this paper, we present experimental and numerical results demonstrating the challenges associated with solving constrained-optimization problems with near-term quantum computers. Our contribution is twofold. First, we demonstrate experimental results showing successful execution of the Quantum Alternating Opera tor Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA)22,23 on the quantum processor Quantinuum H1-1.” & Pg. 2, “The parameters β,γ are chosen using a classical algorithm, typically an optimization routine27, with the goal of maximizing the expected objective value of QAOA state-measurement outcomes.”, therefore, methods of performing computation in a hybrid quantum-classical computing system are disclosed. Niroula also describes the quantum computer as a “near-term quantum computer” – as known in the art, these arrangements rely on both a quantum computer and classical computer. However, Wang is also disclosed below for explicit teaching of a classical computer), comprising:
mapping, by a classical computer, an objective function of an optimization problem to a model Hamiltonian (Niroula, Pg. 2, “Problem description For a given objective function f defined on the N-dimensional Boolean cube and a set of feasible solutions F ⊆{0,1}N , consider the problem of finding a binary string x ∈ F that maximizes it: max x∈F f(x). (1) The set of feasible solutions F is typically given by constraints of the form g(x) = 0 or g(x) ≤ 0 . A binary string x ∈ F is said to be “in-constraint”. Let C ∈ C2N× 2N denote the Hamiltonian (Hermitian operator) encod ing f on qubits. his operator is diagonal in the computational basis ( C = diag(f (x)) ) and is defined by its action on the computational basis: […] QAOA solves the problem (1) by preparing a parameterized quantum state”, therefore, an objective function of an optimization problem is mapped to a model Hamiltonian);
selecting, by the classical computer, a set of variational parameters to construct a parametrized quantum circuit (Niroula, Pg. 5, “This is due to the QAOA parameters being chosen to trade-off the two metrics of success; we discuss this issue in detail in “Quantum circuit needs to preserve constraints” below. […] L-VQE uses a very expressive parameterized circuit that can in principle solve the problem exactly with no two-qubit gates, just by optimizing the parameters of the initial single-qubit-gate layer V(θ0) . At the same time, the expressiveness of L-VQE circuit makes the parameters hard to optimize, both due to their high number and due to the gradients vanishing as the number of qubits grows in some cases44,45. As we consider modestly sized problems in this work, we are able to optimize the parameters and obtain solutions with very high approximation ratio and in-constraint probability.”, thus, a set of variational parameters may be selected/chosen to construct a parameterized quantum circuit) comprising an entangling circuit based on the model Hamiltonian and a mixing circuit (Niroula, Pg. 5, “For most near-term circuit architectures, including H1-1, the single qubit gates are relatively less noisy than the two-qubit entangling gates46,47. As a result, the mixer unitary does not add much noise to the evolution. Each time-step of QAOA, therefore, has at most N(N − 1)/2 entangling gates, all of which come from the pairwise interaction in the problem Hamiltonian. On the other hand, XY-QAOA uses a more complex mixer operator, which preserves the Hamming weight of the states it acts on. This operator is defined as: UXY M (βj) = N k=1e−i βj 2 (xkxk+1+ykyk+1) . It adds additional O(N) gates in each layer of QAOA circuit as it requires entangling gates on all adjacent pairs.”, therefore, the parameterized circuit may comprise an entangling circuit based on the model Hamiltonian and a mixing circuit (mixer));
setting, by a system controller (See introduction of Wang reference below for explicit disclosure of a system controller), a quantum processor in an initial state (Niroula, Pg. 2, “QAOA25,26 solves the problem (1) by preparing a parameterized quantum state […] where xj denotes a single-qubit Pauli x acting on qubit j and the initial state […] is a uniform superposition over all computational basis states.”, thus, the quantum processor is set/prepared in an initial state), wherein the quantum processor comprises a plurality of trapped ions, each of which has two hyperfine states defining a qubit (Niroula, Pg. 10, “The Quantinuum H1-1 Quantum Processor uses quantum charge-couple device architecture with five parallel gate zones in a linear trap. The quantum states are stored in hyperfine states of twenty 171Yb+ atoms. All-to-all connectivity is implemented by rearranging of the physical location of qubits, which introduces a negligible amount of error.”, thus, the quantum processor comprises a plurality of trapped ions (see abstract which explicitly discloses reporting results on a trapped-ion quantum computer) each of which has two hyperfine states defining a qubit);
executing iterations (Niroula, Pg. 9, “For each instance of the optimization problem, we evaluate QAOA for each of the values in the grid using 1000 shots.”, therefore, iterations are executed), each iteration comprising:
applying, by the system controller, the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state (Niroula, Pg. 9, “For L-VQE and XY-QAOA the parameters are optimized by running COBYLA72,73 from a fixed number of randomly chosen initial points. The number of initial points is 20 for L-VQE with 14 qubits, 5 for L-VQE with 5 qubits and 10 for XY-QAOA for all qubit counts. We choose the parameters giving the best approximation ratio. n.”, thus, the parameterized quantum circuit is applied to the quantum processor based on the set of variational parameters and the model Hamiltonian (parameters are optimized) to transform the quantum processor to a trial state from the initial state);
measuring, by the system controller, an expectation value of the model Hamiltonian (Niroula, Pg. 9, “In order to decide the best parameters for each optimization instance, we impose a threshold on the QAOA in-constraint ratio to be higher than 0.06 and we selected the γ , β that maximize the expected approximation ratio. For all the instances, the value obtained was higher than the expected approximation ratio of a random feasible solution.”, thus, the expected value of the model Hamiltonian is measured – further, Appendix on Pg. 11 Figure 6 also shows the expected recall-oriented understudy for gisting evaluation (ROUGE) metric); and
replacing, by the classical computer, the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in the previous iteration is more than a predetermined value (Niroula, Pg. 2, “The parameters β,γ are chosen using a classical algorithm, typically an optimization routine27, with the goal of maximizing the expected objective value of QAOA state-measurement outcomes. The depth of a QAOA circuit is controlled by a free parameter p. In the limit p →∞ , QAOA can solve the problem exactly via adiabatic evolution25.” & Pg. 4, “We optimize the parameters in noiseless simulation and then execute the circuits with optimized parameters on hardware with 2000 shots”, thus, the set of variational parameters may be replaced/chosen in order to maximize the expected objective value of the QAOA state-measurement outcomes between iterations, in order to find an optimal set of parameters to optimize the system. Again, this is similarly supported by Appendix on Pg. 11 Figures 6 and 7 which show such optimal solutions across the ROUGE metric); and
outputting the set of the variational parameters (Niroula, Pg. 4, “Pg. 4, “We optimize the parameters in noiseless simulation and then execute the circuits with optimized parameters on hardware with 2000 shots”, thus, the set of variational parameters, which are optimized, may be outputted for use in the optimized system),
wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant (Niroula, Pg. 3, “The Quantum Alternating Operator Ansatz algorithm22 overcomes this limitation by using a parameterized circuit which limits the quantum evolution to a constraint-preserving subspace” & Pg. 4, “For the XY-QAOA circuit, a significant part of the two-qubit gate depth comes from the circuit preparing the initial state, which is a uniform superposition of all in-constraint states (Dicke state). […] All the statistics we report are computed over 10 problem instances for each number of qubits, with the exception of XY-QAOA on H1-1 where only three 14-qubit and three 20-qubit instances are solved due to the high circuit depth and correspondingly high running time on trapped-ion hardware. “Random” and “Random in-constraint” always refers to statistics computed over all binary strings and all in-constraint binary strings respectively. This is equivalent to computing them with respect to uniform random distribution over corresponding sets.”, therefore, the initial state and trial state may be a uniform superposition of states where the number of trapped ions in the hyperfine excited state is constant (hamming weights preserved by the parameterized circuit)), and
the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (See introduction of Arrazola reference below for teaching of one or more Givens rotation gates).
While Niroula discloses a “near-term quantum computer”, which may comprise both a quantum processor and a classical computer, including the use of a classical algorithm as described above, Niroula does not explicitly disclose a classical computer and a system controller.
However, Wang teaches:
a hybrid quantum-classical computing system comprising a classical computer and a quantum processor (Wang, Claim 1, “A method, performed on a hybrid quantum-classical computer system, for finding an enhanced problem solution to a combinatorial optimization problem, the hybrid quantum-classical computer system comprising a classical computer and a quantum computer,”, thus, a hybrid quantum-classical computer system comprising a classical computer and a quantum processor is disclosed)
a system controller (Wang, Par. [0148], “The quantum computer 102 includes a control unit 106, which may include any of a variety of circuitry and/or other machinery for performing the functions disclosed herein. The control unit 106 may, for example, consist entirely of classical components. The control unit 106 generates and provides as output one or more control signals 108 to the qubits 104.”, therefore, a system controller (control unit) is disclosed)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of performing computation in a hybrid quantum-classical computing system, as disclosed by Niroula to include a classical computer and system controller, as disclosed by Wang. One of ordinary skill in the art would have been motivated to make this modification to enable a hybrid quantum-classical computing system, including a system controller and classical computer, which may be used to tune parameters of a quantum circuit to maximize the probability of obtaining high-quality solutions – hence, providing an integrated hybrid system which improves system accuracy and performance (Wang, Par. [0003], “Also, there is a growing interest in using near-term quantum devices to solve complex problems in combinatorial optimization. Although these problems have been studied for decades using classic computers, there is a desire to use quantum circuits to solve these problems. One proposed solution for addressing combinatorial optimization problems is known as the Quantum Approximate Optimization Algorithm (QAOA), which is a hybrid quantum-classical algorithm. Using QAOA, a classical optimization algorithm tunes the parameters of a quantum circuit to maximize the probability of obtaining high-quality solutions from a final quantum state.”).
Although Niroula teaches the use of a Hamming-weight-preserving XY mixer on a trapped-ion quantum computer (See Niroula abstract and Pg. 5), Niroula in view of Wang does not explicitly disclose the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state
However, Arrazola teaches the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (Arrazola, Pg. 1, Abstract, “In this work, we show that controlled single-excitation gates in the form of Givens rotations are universal for particle-conserving unitaries. Single excitation gates describe an arbitrary U(2) rotation on the two-qubit subspace spanned by the states |01⟩,|10⟩, while leaving other states unchanged– a transformation that is analogous to a single-qubit rotation on a dual-rail qubit. The proof is constructive, so our result also provides an explicit method for compiling arbitrary particle-conserving unitaries. Additionally, we describe a method for using controlled single-excitation gates to prepare an arbitrary state of a fixed number of particles. We derive analytical gradient formulas for Givens rotations as well as decompositions into single qubit and CNOT gates. Our results offer a unifying framework for quantum computational chemistry where every algorithm is a unique recipe built from the same universal ingredients: Givens rotations.”, therefore, the quantum circuit presented by Arrazola teaches the mixing circuit comprising one or more Givens rotation gates (which function as such a mixer), where the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (particle-conserving unitaries, as also supported by Pg. 1 which states “To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number”)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of performing computation in a hybrid quantum-classical computing system, as disclosed by Niroula in view of Wang to include where the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state, as disclosed by Arrazola. One of ordinary skill in the art would have been motivated to make this modification to enable the use of one or more Givens rotation gates which preserves hard constraints, such as a fixed hamming weight or particle number, while allowing the quantum state to explore valid solutions, hence improving system accuracy while reducing the use of computational resources (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner representation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number. […] In this work, we provide such a framework by giving a constructive proof that controlled single excitation gates are universal for particle-conserving unitaries.”)
Regarding Claim 3, Niroula in view of Wang in view of Arrazola teaches the method of claim 1, wherein the initial state is a superposition of states where the trapped ions in the hyperfine ground state are spread over the quantum processor (Niroula, Pg. 5, “On the other hand, XY-QAOA uses a more complex mixer operator, which preserves the Hamming weight of the states it acts on. This operator is defined as: UXY M (βj) = N k=1e−i βj 2 (xkxk+1+ykyk+1) . It adds additional O(N) gates in each layer of QAOA circuit as it requires entangling gates on all adjacent pairs.”, thus, as the circuit requires entangling gates on all adjacent pairs, this indicates that ions are spread over the quantum processor – hence, the initial state is a superposition of states (see preceding rejection of claim 1 which states the same) where trapped ions in hyperfine ground state are spread over the quantum processor).
Regarding Claim 4, Niroula in view of Wang in view of Arrazola teaches the method of claim 1, wherein the initial state is a non-uniform superposition of all possible states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant (Wang, Par. [0109], “Recall that GM-QAOA always starts with a uniform superposition of the feasible solutions (many of which have low qualities), while CBQOA starts with a non-uniform superposition of the feasible solutions in which the neighbors of the seed receive higher amplitudes than the other”, thus, the initial state may also comprise a non-uniform superposition of all possible states where the number of trapped ions of the plurality in the hyperfine excited state is constant).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of performing computation in a hybrid quantum-classical computing system of claim 1, as disclosed by Niroula in view of Wang in view of Arrazola to include wherein the initial state is a non-uniform superposition of all possible states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant, as disclosed by Wang. One of ordinary skill in the art would have been motivated to make this modification to enable an initial state in non-uniform superposition in which neighbors of the approximate solution receive higher amplitudes than others, providing a larger overlap with states corresponding to high-quality solutions, thus improving system performance and accuracy (Wang, Par. [0109], “Recall that GM-QAOA always starts with a uniform superposition of the feasible solutions (many of which have low qualities), while CBQOA starts with a non-uniform superposition of the feasible solutions in which the neighbors of the seed receive higher amplitudes than the others. As a consequence, the initial state of CBQOA has larger overlap with the states corresponding to the high-quality solutions than that of GM-QAOA, which means that CBQOA needs fewer layers than GM-QAOA to reach the same performance, as supported by the experimental data.”)
Regarding Claim 5, Niroula in view of Wang in view of Arrazola teaches the method of claim 1, wherein the initial state is a uniform superposition of all possible states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant (Niroula, Pg. 6, “The implementation of XY-QAOA requires preparing an initial state, which is a uniform superposition of all in-constraint states (Dicke state).”, thus, the initial state is a uniform superposition of all possible states where the number of trapped ions of the plurality in the hyperfine excited state is constant (Dicke state))
Regarding Claim 6, Niroula in view of Wang in view of Arrazola teaches the method of claim 1, wherein the set of the variational parameters is initially selected randomly (Niroula, Pg. 9, “For L-VQE and XY-QAOA the parameters are optimized by running COBYLA72,73 from a fixed number of randomly chosen initial points. The number of initial points is 20 for L-VQE with 14 qubits, 5 for L-VQE with 5 qubits and 10 for XY-QAOA for all qubit counts”, thus, the set of variational parameters may be initially selected randomly).
Regarding Claim 7, Niroula teaches hybrid quantum-classical computing system (Niroula, Pg. 2, “In this paper, we present experimental and numerical results demonstrating the challenges associated with solving constrained-optimization problems with near-term quantum computers. Our contribution is twofold. First, we demonstrate experimental results showing successful execution of the Quantum Alternating Opera tor Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA)22,23 on the quantum processor Quantinuum H1-1.” & Pg. 2, “The parameters β,γ are chosen using a classical algorithm, typically an optimization routine27, with the goal of maximizing the expected objective value of QAOA state-measurement outcomes.”, therefore, methods of performing computation in a hybrid quantum-classical computing system are disclosed), comprising:
a quantum processor comprising a plurality of trapped ions, each of the trapped ions having two hyperfine states defining a qubit (Niroula, Pg. 10, “The Quantinuum H1-1 Quantum Processor uses quantum charge-couple device architecture with five parallel gate zones in a linear trap. The quantum states are stored in hyperfine states of twenty 171Yb+ atoms. All-to-all connectivity is implemented by rearranging of the physical location of qubits, which introduces a negligible amount of error.”, thus, the quantum processor comprises a plurality of trapped ions (see abstract which explicitly discloses reporting results on a trapped-ion quantum computer) each of which has two hyperfine states defining a qubit);
one or more lasers configured to emit a laser beam, which is provided to trapped ions in the quantum processor (Niroula teaches a quantum processor comprising a plurality of trapped ions, as disclosed above, seemingly this also implies the use of one or more lasers configured to emit a laser beam – however, this is not explicitly disclosed in Niroula. See introduction of Wang reference below for explicit disclosure of one or more lasers configured to emit a laser beam, which provided to trapped ions in the quantum processor); and
a classical computer (Niroula describes the use of classical algorithms on Pg. 2 & further describes the trapped ion quantum computer as a “near-term quantum computer”, as known in the art, these arrangements rely on both a quantum computer and classical computer – however, this is not explicitly disclosed in Niroula. See introduction of Wang reference below for explicit disclosure of a classical computer) configured to:
map an objective function of an optimization problem to a model Hamiltonian (Niroula, Pg. 2, “Problem description For a given objective function f defined on the N-dimensional Boolean cube and a set of feasible solutions F ⊆{0,1}N , consider the problem of finding a binary string x ∈ F that maximizes it: max x∈F f(x). (1) The set of feasible solutions F is typically given by constraints of the form g(x) = 0 or g(x) ≤ 0 . A binary string x ∈ F is said to be “in-constraint”. Let C ∈ C2N× 2N denote the Hamiltonian (Hermitian operator) encoding f on qubits. his operator is diagonal in the computational basis ( C = diag(f (x)) ) and is defined by its action on the computational basis: […] QAOA solves the problem (1) by preparing a parameterized quantum state”, therefore, an objective function of an optimization problem is mapped to a model Hamiltonian);
select a set of variational parameters to construct a parametrized quantum circuit (Niroula, Pg. 5, “This is due to the QAOA parameters being chosen to trade-off the two metrics of success; we discuss this issue in detail in “Quantum circuit needs to preserve constraints” below. […] L-VQE uses a very expressive parameterized circuit that can in principle solve the problem exactly with no two-qubit gates, just by optimizing the parameters of the initial single-qubit-gate layer V(θ0) . At the same time, the expressiveness of L-VQE circuit makes the parameters hard to optimize, both due to their high number and due to the gradients vanishing as the number of qubits grows in some cases44,45. As we consider modestly sized problems in this work, we are able to optimize the parameters and obtain solutions with very high approximation ratio and in-constraint probability.”, thus, a set of variational parameters may be selected/chosen to construct a parameterized quantum circuit) comprising an entangling circuit based on the model Hamiltonian and a mixing circuit (Niroula, Pg. 5, “For most near-term circuit architectures, including H1-1, the single qubit gates are relatively less noisy than the two-qubit entangling gates46,47. As a result, the mixer unitary does not add much noise to the evolution. Each time-step of QAOA, therefore, has at most N(N − 1)/2 entangling gates, all of which come from the pairwise interaction in the problem Hamiltonian. On the other hand, XY-QAOA uses a more complex mixer operator, which preserves the Hamming weight of the states it acts on. This operator is defined as: UXY M (βj) = N k=1e−i βj 2 (xkxk+1+ykyk+1) . It adds additional O(N) gates in each layer of QAOA circuit as it requires entangling gates on all adjacent pairs.”, therefore, the parameterized circuit may comprise an entangling circuit based on the model Hamiltonian and a mixing circuit (mixer));
control a system controller (See introduction of Wang reference below for explicit disclosure of a system controller) to the quantum processor in an initial state (Niroula, Pg. 2, “QAOA25,26 solves the problem (1) by preparing a parameterized quantum state […] where xj denotes a single-qubit Pauli x acting on qubit j and the initial state […] is a uniform superposition over all computational basis states.”, thus, the quantum processor is set/prepared in an initial state);
execute iterations (Niroula, Pg. 9, “For each instance of the optimization problem, we evaluate QAOA for each of the values in the grid using 1000 shots.”, therefore, iterations are executed), each iteration comprising:
controlling the system controller to apply the parametrized quantum circuit to the quantum processor based on the set of the variational parameters and the model Hamiltonian, to transform the quantum processor to a trial state (Niroula, Pg. 9, “For L-VQE and XY-QAOA the parameters are optimized by running COBYLA72,73 from a fixed number of randomly chosen initial points. The number of initial points is 20 for L-VQE with 14 qubits, 5 for L-VQE with 5 qubits and 10 for XY-QAOA for all qubit counts. We choose the parameters giving the best approximation ratio. n.”, thus, the parameterized quantum circuit is applied to the quantum processor based on the set of variational parameters and the model Hamiltonian (parameters are optimized) to transform the quantum processor to a trial state from the initial state);
controlling by the system controller to measure an expectation value of the model Hamiltonian (Niroula, Pg. 9, “In order to decide the best parameters for each optimization instance, we impose a threshold on the QAOA in-constraint ratio to be higher than 0.06 and we selected the γ , β that maximize the expected approximation ratio. For all the instances, the value obtained was higher than the expected approximation ratio of a random feasible solution.”, thus, the expected value of the model Hamiltonian is measured – further, Appendix on Pg. 11 Figure 6 also shows the expected recall-oriented understudy for gisting evaluation (ROUGE) metric); and
replacing the set of the variational parameters with another set of variational parameters, if a difference between the measured expectation value of the model Hamiltonian and the expectation value of the model Hamiltonian measured in the previous iteration is more than a predetermined value (Niroula, Pg. 2, “The parameters β,γ are chosen using a classical algorithm, typically an optimization routine27, with the goal of maximizing the expected objective value of QAOA state-measurement outcomes. The depth of a QAOA circuit is controlled by a free parameter p. In the limit p →∞ , QAOA can solve the problem exactly via adiabatic evolution25.” & Pg. 4, “We optimize the parameters in noiseless simulation and then execute the circuits with optimized parameters on hardware with 2000 shots”, thus, the set of variational parameters may be replaced/chosen in order to maximize the expected objective value of the QAOA state-measurement outcomes between iterations, in order to find an optimal set of parameters to optimize the system. Again, this is similarly supported by Appendix on Pg. 11 Figures 6 and 7 which show such optimal solutions across the ROUGE metric); and
output the set of the variational parameters (Niroula, Pg. 4, “Pg. 4, “We optimize the parameters in noiseless simulation and then execute the circuits with optimized parameters on hardware with 2000 shots”, thus, the set of variational parameters, which are optimized, may be outputted for use in the optimized system),
wherein the initial state and the trial state each are a superposition of states where the number of trapped ions of the plurality of trapped ions in the hyperfine excited state is constant (Niroula, Pg. 3, “The Quantum Alternating Operator Ansatz algorithm22 overcomes this limitation by using a parameterized circuit which limits the quantum evolution to a constraint-preserving subspace” & Pg. 4, “For the XY-QAOA circuit, a significant part of the two-qubit gate depth comes from the circuit preparing the initial state, which is a uniform superposition of all in-constraint states (Dicke state). […] All the statistics we report are computed over 10 problem instances for each number of qubits, with the exception of XY-QAOA on H1-1 where only three 14-qubit and three 20-qubit instances are solved due to the high circuit depth and correspondingly high running time on trapped-ion hardware. “Random” and “Random in-constraint” always refers to statistics computed over all binary strings and all in-constraint binary strings respectively. This is equivalent to computing them with respect to uniform random distribution over corresponding sets.”, therefore, the initial state and trial state may be a uniform superposition of states where the number of trapped ions in the hyperfine excited state is constant (hamming weights preserved by the parameterized circuit)), and
the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of the trapped ions in the hyperfine excited state (See introduction of Arrazola reference below for teaching of one or more Givens rotation gates).
While Niroula teaches a quantum processor comprising a plurality of trapped ions, as disclosed above, seemingly this also implies the use of one or more lasers configured to emit a laser beam – however, this is not explicitly disclosed in Niroula. Further, Niroula discloses a “near-term quantum computer”, which may comprise both a quantum processor and a classical computer, including the use of a classical algorithm as described above – however, Niroula does not explicitly disclose a classical computer and a system controller.
However, Wang teaches:
one or more lasers configured to emit a laser beam, which is provided to trapped ions in the quantum processor (Wang, Par. [0162], “For example, a laser unit may be used both to generate the control signals 108 and to provide stimulus (e.g., one or more laser beams) to the qubits 104 to cause the measurement signals 112 to be generated.”, therefore, one or more lasers configured to emit a laser beam, which is provided to trapped ions (see Wang Par. [0130] which explicitly discloses trapped ions) in the quantum processor (qubits) are disclosed)
classical computer (Wang, Claim 1, “A method, performed on a hybrid quantum-classical computer system, for finding an enhanced problem solution to a combinatorial optimization problem, the hybrid quantum-classical computer system comprising a classical computer and a quantum computer,”, thus, a hybrid quantum-classical computer system comprising a classical computer and a quantum processor is disclosed)
system controller (Wang, Par. [0148], “The quantum computer 102 includes a control unit 106, which may include any of a variety of circuitry and/or other machinery for performing the functions disclosed herein. The control unit 106 may, for example, consist entirely of classical components. The control unit 106 generates and provides as output one or more control signals 108 to the qubits 104.”, therefore, a system controller (control unit) is disclosed)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the hybrid quantum-classical computing system, as disclosed by Niroula to include one or more lasers configured to emit a laser beam, which is provided to trapped ions in the quantum processor, a classical computer, and a system controller, as disclosed by Wang. One of ordinary skill in the art would have been motivated to make this modification to enable a hybrid quantum-classical computing system, including one or more laser beams, a system controller and classical computer, which may be used to tune parameters of a quantum circuit to maximize the probability of obtaining high-quality solutions – hence, providing an integrated hybrid system which improves system accuracy and performance (Wang, Par. [0003], “Also, there is a growing interest in using near-term quantum devices to solve complex problems in combinatorial optimization. Although these problems have been studied for decades using classic computers, there is a desire to use quantum circuits to solve these problems. One proposed solution for addressing combinatorial optimization problems is known as the Quantum Approximate Optimization Algorithm (QAOA), which is a hybrid quantum-classical algorithm. Using QAOA, a classical optimization algorithm tunes the parameters of a quantum circuit to maximize the probability of obtaining high-quality solutions from a final quantum state.”).
Although Niroula teaches the use of a Hamming-weight-preserving XY mixer on a trapped-ion quantum computer (See Niroula abstract and Pg. 5), Niroula in view of Wang does not explicitly disclose the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state
However, Arrazola teaches the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (Arrazola, Pg. 1, Abstract, “In this work, we show that controlled single-excitation gates in the form of Givens rotations are universal for particle-conserving unitaries. Single excitation gates describe an arbitrary U(2) rotation on the two-qubit subspace spanned by the states |01⟩,|10⟩, while leaving other states unchanged– a transformation that is analogous to a single-qubit rotation on a dual-rail qubit. The proof is constructive, so our result also provides an explicit method for compiling arbitrary particle-conserving unitaries. Additionally, we describe a method for using controlled single-excitation gates to prepare an arbitrary state of a fixed number of particles. We derive analytical gradient formulas for Givens rotations as well as decompositions into single qubit and CNOT gates. Our results offer a unifying framework for quantum computational chemistry where every algorithm is a unique recipe built from the same universal ingredients: Givens rotations.”, therefore, the quantum circuit presented by Arrazola teaches the mixing circuit comprising one or more Givens rotation gates (which function as such a mixer), where the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state (particle-conserving unitaries, as also supported by Pg. 1 which states “To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number”)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the hybrid quantum-classical computing system, as disclosed by Niroula in view of Wang to include where the mixing circuit comprises one or more Givens rotation gates, wherein the one or more Givens rotation gates maintain the number of trapped ions in the hyperfine excited state, as disclosed by Arrazola. One of ordinary skill in the art would have been motivated to make this modification to enable the use of one or more Givens rotation gates which preserves hard constraints, such as a fixed hamming weight or particle number, while allowing the quantum state to explore valid solutions, hence improving system accuracy while reducing the use of computational resources (Arrazola, Pg. 1, “This motivates the use of gate sets that preserve subspaces of fixed particle number. We focus on the Jordan-Wigner representation [10], which encodes the subspace of states with k particles in n spin-orbitals into n qubits. This space is spanned by the set of all n-qubit states with Hamming weight k, i.e., states with k ones and n−k zeros. To ensure that output states remain valid, quantum circuits for quantum chemistry benefit from employing gates that preserve Hamming weight and therefore particle number. […] In this work, we provide such a framework by giving a constructive proof that controlled single excitation gates are universal for particle-conserving unitaries.”)
Regarding Claim 8, Niroula in view of Wang in view of Arrazola teaches the hybrid quantum-classical computing system of claim 7, wherein each of the trapped ions is
Y
b
+
171
having
S
1
/
2
2
hyperfine states (Niroula, Pg. 10, “The quantum states are stored in hyperfine states of twenty 171Yb+ atoms. All-to-all connectivity is implemented by rearranging of the physical location of qubits, which introduces a negligible amount of error.”, thus, each of the trapped ions is
Y
b
+
171
having
S
1
/
2
2
hyperfine states).
Claim 11 recites substantially the same limitations as Claim 3 in the form of a system, therefore it is rejected under the same rationale.
Claim 12 recites substantially the same limitations as Claim 4 in the form of a system, therefore it is rejected under the same rationale.
Claim 13 recites substantially the same limitations as Claim 5 in the form of a system, therefore it is rejected under the same rationale.
Claim 14 recites substantially the same limitations as Claim 6 in the form of a system, therefore it is rejected under the same rationale.
Regarding Claim 15, Niroula in view of Wang in view of Arrazola teaches a hybrid quantum-classical computing system (Niroula, Pg. 2, “In this paper, we present experimental and numerical results demonstrating the challenges associated with solving constrained-optimization problems with near-term quantum computers. Our contribution is twofold. First, we demonstrate experimental results showing successful execution of the Quantum Alternating Opera tor Ansatz algorithm with a Hamming-weight-preserving XY mixer (XY-QAOA)22,23 on the quantum processor Quantinuum H1-1.” & Pg. 2, “The parameters β,γ are chosen using a classical algorithm, typically an optimization routine27, with the goal of maximizing the expected objective value of QAOA state-measurement outcomes.”, therefore, methods of performing computation in a hybrid quantum-classical computing system are disclosed) comprising a non-transitory computer-readable memory having a number of instructions stored therein which, when executed by one or more processors (Wang, Claim 1, “the classical computer including a processor, a non-transitory computer readable medium, and computer instructions stored in the non-transitory computer readable medium;”, thus, a non-transitory computer-readable memory having a number of instructions stored to be executed by one or more processors is disclosed), causes the hybrid quantum-classical computing system to perform operations comprising: […]
The rest of the claim language in Claim 15 recites substantially the same limitations as Claim 1, in the form of a system, therefore it is rejected under the same rationale.
The reasons of obviousness have been noted in the rejection of Claim 1 above and applicable herein.
Claim 17 recites substantially the same limitations as Claim 3 in the form of a system, therefore it is rejected under the same rationale.
Claim 18 recites substantially the same limitations as Claim 4 in the form of a system, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 5 in the form of a system, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 6 in the form of a system, therefore it is rejected under the same rationale.
13. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Niroula et al. (hereinafter Niroula) (“Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer”), in view of Wang et al. (hereinafter Wang) (US PG-PUB 20230153373), in view of Arrazola et al. (hereinafter Arrazola) (“Universal Quantum Circuits for Quantum Chemistry”), further in view of Brown et al. (hereinafter Brown) (“Co-designing a scalable quantum computer with trapped atomic ions”).
Regarding Claim 9, Niroula in view of Wang in view of Arrazola teaches the hybrid quantum-classical computing system of claim 7.
Niroula in view of Wang in view of Arrazola does not explicitly disclose wherein each of the trapped ions is one selected from Be+, Ca+, Sr+, Mg+, Ba+, Zn+, Hg+, Cd+.
However, Brown teaches wherein each of the trapped ions is one selected from Be+, Ca+, Sr+, Mg+, Ba+, Zn+, Hg+, Cd+ (Brown, Pg. 2, “The qubits can be initialised and detected with nearly perfect accuracy using conventional optical pumping and state-dependent fluorescence techniques.17 This restricts the atomic species of trapped ion qubits to those with simple electronic structure (e.g., those with a single valence electron: Be+, Mg+, Ca+, Sr+, Ba+, Zn+, Hg+, Cd+ and Yb+).”, thus, the trapped ions are selected from one of the aforementioned ions).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the hybrid quantum-classical computing system of claim 7, as disclosed by Niroula in view of Wang in view of Arrazola to include wherein each of the trapped ions is one selected from Be+, Ca+, Sr+, Mg+, Ba+, Zn+, Hg+, Cd+, as disclosed by Brown. One of ordinary skill in the art would have been motivated to make this modification to limit the trapped ions to those with a simple electronic structure and a single valence electron, which may provide simplified energy levels without interference making them easier to manipulate with lasers during quantum operations and additionally enables high-fidelity initialization and readout (Brown, Pg. 2, “The qubits can be initialised and detected with nearly perfect accuracy using conventional optical pumping and state-dependent fluorescence techniques.17 This restricts the atomic species of trapped ion qubits to those with simple electronic structure (e.g., those with a single valence electron: Be+, Mg+, Ca+, Sr+, Ba+, Zn+, Hg+, Cd+ and Yb+).”)
14. Claims 2, 10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Niroula et al. (hereinafter Niroula) (“Constrained quantum optimization for extractive summarization on a trapped-ion quantum computer”), in view of Wang et al. (hereinafter Wang) (US PG-PUB 20230153373), in view of Arrazola et al. (hereinafter Arrazola) (“Universal Quantum Circuits for Quantum Chemistry”), in view of Hadfield et al. (hereinafter Hadfield) (“From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz”), further in view of Scalettar et al. (hereinafter Scalettar) (“An Introduction to the Hubbard Hamiltonian”).
Regarding Claim 2, Niroula in view of Wang in view of Arrazola teaches the method of claim 1.
Niroula in view of Wang in view of Arrazola does not explicitly disclose wherein the optimization problem is the travelling salesman problem.
However, Hadfield teaches wherein the optimization problem is the travelling salesman problem (Hadfield, Pg. 22, “Here, we introduce the machinery for mapping such problems to QAOA, using the traveling salesperson and several single-machine scheduling problems as illustrative examples.”, thus, QAOA is applied with the traveling salesman problem).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 1, as disclosed by Niroula in view of Wang in view of Arrazola to include wherein the optimization problem is the travelling salesman problem, as disclosed by Hadfield. One of ordinary skill in the art would have been motivated to make this modification to enable the efficient implementation of mixers for optimization problems with hard constraints, such as the traveling salesman problem, hence improving implementation costs whilst also efficiently minimizing constraints (Hadfield, Pg. 1, “For cases that call for mixing only within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficiently implementable mixers than was possible in the original framework. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace, and soft constraints whose violation we wish to minimize.” & Pg. 7, “The quantum alternating operator ansatz (QAOAp) quantum circuit schematic. Here, an encoding to qubits for a given problem domain is assumed. The box shows an example decomposition of a QAOA mixing operator family UM(β) into a sequence of partial mixers UM,α(β). In this ansatz, a one-parameter family of mixing operators does not in general correspond to time evolution under a fixed mixing Hamiltonian HM. The construction of this paper includes different orderings of the partial mixers, resulting in a variety of inequivalent mixing operators with different implementation costs.” & Pg. 22, “Given a set of n cities, and distances d : [n]2 → R+, find an ordering of the cities that minimizes the total distance traveled for the corresponding tour.”)
Niroula in view of Wang in view of Arrazola does not explicitly disclose wherein […] the model Hamiltonian is a Hubbard Hamiltonian.
However, Scalettar teaches wherein […] the model Hamiltonian is a Hubbard Hamiltonian (Scalettar, Pg. 2, “Over the intervening years, the HH has been applied to many systems, from ‘heavy fermions’ and the Cerium volume collapse transition in the 1980’s, to high temperature superconductors in the 1990’s. Indeed, it is an amazing feature of the HH that, despite its simplicity, its exhibits behavior relevant to many of the most subtle and beautiful properties of solid state systems. We focus here for the most part on the single-band HH. Multi-band variants like the Periodic Ander son Model (PAM) allow one to introduce other fundamental concepts in many-body physics, such as the competition between magnetic order and singlet formation.”, thus, a Hubbard Hamiltonian (HH) may be applied to the hybrid quantum system).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 1, as disclosed by Niroula in view of Wang in view of Arrazola to include wherein […] the model Hamiltonian is a Hubbard Hamiltonian, as disclosed by Scalettar. One of ordinary skill in the art would have been motivated to make this modification to enable the use of a Hubbard Hamiltonian which may provide improved insights on interactions between electrons of a hybrid quantum system (Scalettar, Pg. 2, “The Hubbard Hamiltonian (HH) offers one of the most simple ways to get insight into how the interactions between electrons give rise to insulating, magnetic, and even novel superconducting effects in a solid. It was written down [1–4] in the early 1960’s and initially applied to the behavior of the transition-metal monoxides (FeO, NiO, CoO), compounds which are anti ferromagnetic insulators, yet had been predicted to be metallic by methods which treat strong interactions less carefully”).
Claim 10 recites substantially the same limitations as Claim 2 in the form of a system, therefore it is rejected under the same rationale.
Claim 16 recites substantially the same limitations as Claim 2 in the form of a system, therefore it is rejected under the same rationale.
Conclusion
15. 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.
16. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
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