IP Library Granted Patent US 12,299,538
Granted Patent B2
US 12,299,538 · App. 17/257,895 · Granted May 13, 2025

Preparing superpositions of computational basis states on a quantum computer

Inventors: Zhang Jiang (El Segundo, CA); Ryan Babbush (Venice, CA)
Assignee: Google LLC
G06N10/60G06F9/30101G06N10/20
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Quick Facts
Patent No.
US 12,299,538
App. No.
17/257,895
Granted
May 13, 2025
Kind
B2
Abstract

Methods, systems and apparatus for preparing arbitrary superposition quantum states of a quantum register on a quantum computer, the quantum state comprising a superposition of L computational basis states. In one aspect, a register of log L qubits is prepared in a weighted sum of register basis states, where each register basis state indexes a corresponding quantum state computational basis state, and the amplitude of each register basis state in the weighted sum of register basis states is equal to the amplitude of the corresponding computational basis state in the superposition of L computational basis states. A unitary transformation that maps the register basis states to the corresponding L computational basis states is then implemented, including, for each index 1 to L, controlling, by the register of log L qubits, transformation of the quantum system register state for the index to the corresponding computational basis state for the index.

Claims (74)

1. A method for preparing a quantum state of a quantum system register on a quantum computer, wherein the quantum state comprises a superposition of L computational basis states, the method comprising:

preparing a register of log L qubits in an initial state, the initial state comprising a weighted sum of register basis states, wherein:

each register basis state indexes a corresponding quantum state computational basis state, and

an amplitude of each register basis state in the weighted sum of register basis states is equal to the amplitude of the corresponding computational basis state in the superposition of L computational basis states; and

preparing the quantum state by implementing a unitary transformation that maps the register basis states to the corresponding L computational basis states, comprising, for each index 1 to L;

controlling, by the register of log L qubits, transformation of the quantum system register state for the index to the corresponding computational basis state for the index, comprising:

applying a unitary operator for the index to the quantum system register state controlled by a state of a unary register, wherein the state of the unary register is determined by the register of log L qubits, to read the computational basis state corresponding to the index to the quantum system register,

erasing the state of the register of log L qubits using a unitary operator controlled by the unary register to put the register of log L qubits in a zero state; and

un-computing the unary register.

2. The method of claim 1 , wherein applying a unitary operator for the index to the quantum system register state controlled by a state of a unary register, wherein the state of the unary register is determined by the register of log L qubits, to read the computational basis state corresponding to the index to the quantum system register comprises implementing a unary iteration quantum circuit.

3. The method of claim 1 , further comprising providing the register of log L qubits for use in further computations.

4. The method of claim 1 , wherein applying the unitary operator for the index to the quantum system register state controlled by the state of the unary register that is determined by the register of log L qubits comprises controlling applications of products of Pauli-X quantum logic gates.

5. The method of claim 1 , wherein implementing a unitary transformation that maps the register basis states to the corresponding L computational basis states comprises applying select unitary methods.

6. The method of claim 1 , wherein preparing the register of log L qubits in the initial state comprises applying quantum circuit synthesis techniques.

7. The method of claim 1 , wherein the superposition of L computational basis states is determined using an adaptive sampling configuration interaction method.

8. The method of claim 1 , further comprising providing the quantum state for use in a quantum phase estimation algorithm.

9. The method of claim 1 , further comprising:

performing a quantum simulation using the prepared quantum state as an initial state of the quantum simulation.

10. The method of claim 1 , wherein the quantum computer comprises a circuit model quantum computer.

11. An apparatus comprising:

quantum hardware; and

one or more classical processors;

wherein the apparatus is configured to perform operations for preparing a quantum state of a quantum system register on a quantum computer, wherein the quantum state comprises a superposition of L computational basis states, the operations comprising:

preparing a register of log L qubits in an initial state, the initial state comprising a weighted sum of register basis states, wherein:

each register basis state indexes a corresponding quantum state computational basis state, and

an amplitude of each register basis state in the weighted sum of register basis states is equal to the amplitude of the corresponding computational basis state in the superposition of L computational basis states; and

preparing the quantum state by implementing a unitary transformation that maps the register basis states to the corresponding L computational basis states, comprising, for each index 1 to L;

controlling, by the register of log L qubits, transformation of the quantum system register state for the index to the corresponding computational basis state for the index, comprising:

applying a unitary operator for the index to the quantum system register state controlled by a state of a unary register, wherein the state of the unary register is determined by the register of log L qubits, to read the computational basis state corresponding to the index to the quantum system register,

erasing the state of the register of log L qubits using a unitary operator controlled by the unary register to put the register of log L qubits in a zero state; and

un-computing the unary register.

12. The apparatus of claim 11 , wherein the quantum hardware comprises:

a quantum circuit comprising:

a quantum system register comprising multiple target qubits;

an index register comprising log L index qubits;

a control register comprising multiple control qubits;

one or more control devices configured to operate the quantum circuit.

13. A method for preparing a target quantum state of a quantum system register on a quantum computer, wherein the target quantum state comprises a superposition of L computational basis states, the method comprising, sequentially for each index l=1 to l=L:

preparing the quantum system register and a unary register in a quantum state, wherein:

the state of the quantum system register is entangled with the unary register,

at an initial time step the state of the quantum system register equals the target quantum state up to the first (l−1) computational basis states if the state of the unary register is |0 , and

the state of the quantum system register equals a l-th computational basis state if the unary register is in state |1 ;

selecting a qubit from the quantum system register whose value is different in the l-th computational basis state and a l+1-th computational basis state;

applying a rotation to the selected qubit, wherein the rotation is controlled by the state of a unary register;

erasing the unary register value for the l-th computational basis state; and

implementing a NOT logic gate on the remaining qubits in the quantum system register whose values are different in the l-th computational basis state and the l+1-th computational basis state, wherein implementation of the NOT logic gate is controlled by the state of the unary register.

14. The method of claim 13 , wherein preparing the quantum system register and a see tiff unary register in a quantum state comprises preparing the quantum system register and unary for register in a quantum state |ψ l −β l |D l |1 +Σ l′=1 l−1 α l′ |D l′ |0 , wherein l represents the index, |D l represents the l-th computational basis state, α represents a computational basis state amplitude, and |β l |=√{square root over (1−Σ l′=1 l−1 |α l′ | 2 )}.

15. The method of claim 13 , further comprising ordering the computational basis states such that Hamming distances between neighboring computational basis states are reduced.

16. The method of claim 13 , wherein applying a rotation to the selected qubit comprises applying a Pauli X gate to the selected qubit.

17. The method of claim 13 , wherein selecting a qubit from the quantum system register whose value is different the l-th computational basis state and the l+l-th computational basis state comprises selecting a qubit from the quantum system whose occupation numbers d l,k and d l+1,k are different.

18. The method of claim 13 , wherein an amplitude of the l-th basis state is derived from normalization.

19. The method of claim 14 , wherein |β l |, is derived from normalization.

20. The method of claim 13 , wherein the superposition of L computational basis states is determined using an adaptive sampling configuration interaction method.

21. The method of claim 13 , further comprising providing the target quantum state for use in a quantum phase estimation algorithm.

22. The method of claim 13 , further comprising:

initializing a quantum simulation using the prepared target quantum state; and

performing a the quantum simulation.

23. An apparatus comprising:

quantum hardware; and

one or more classical processors;

wherein the apparatus is configured to perform operations for preparing a target quantum state of a quantum system register on a quantum computer, wherein the target quantum state comprises a superposition of L computational basis states, the operations comprising, sequentially for each index l=1 to l=L:

preparing the quantum system register and a unary register in a quantum state, wherein:

the state of the quantum system register is entangled with the unary register,

at an initial time step the state of the quantum system register equals the target quantum state up to the first (l−1) computational basis states if the state of the unary register is |0 , and

the state of the quantum system register equals a l-th computational basis state if the unary register is in state |l ;

selecting a qubit from the quantum system register whose value is different in the l-th computational basis state and the l+1-th computational basis state;

applying a rotation to the selected qubit, wherein the rotation is controlled by the state of a unary register;

erasing the unary register value for the 1-th computational basis state; and

implementing a NOT logic gate on the remaining qubits in the quantum system register whose values are different in the l-th computational basis state and the l+1-th computational basis state, wherein implementation of the NOT logic gate is controlled by the state of the unary register.

24. The apparatus of claim 23 , wherein the quantum hardware comprises:

a quantum circuit comprising:

a quantum system register comprising multiple target qubits;

a unary register comprising multiple control qubits; and

one or more control devices configured to operate the quantum.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 2, 2021
From: JIANG, ZHANG; BABBUSH, RYAN
To: GOOGLE LLC
Reel/Frame 055109/0416 →
Continuity (2)
Provisional Application 62694850 · Jul 6, 2018
Related Publication 20210271477A1 · Sep 2, 2021
References Cited (110)
Babbush, “Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity”, 2018 (Year: 2018). [cited by examiner]
Ruiz-Perez, “Quantum arithmetic with the quantum Fourier transform”, 2017 (Year: 2017). [cited by examiner]
Office Action in Canada Appln. No. 3,156,724, dated Jun. 9, 2023, 6 pages. [cited by applicant]
Office Action in Canadian Appln. No. 3,102,290, dated Aug. 22, 2022, 5 pages. [cited by applicant]
Notice of Allowance in Australian Appln. No. 2023203463, mailed on Oct. 13, 2023, 3 pages. [cited by applicant]
EP Office Action in European Appln. No. 19745436.6, dated Sep. 23, 2022, 5 pages. [cited by applicant]
CA Office Action in Canadian Appln. No. 3,102,290, dated Aug. 22, 2022, 5 pages. [cited by applicant]
Notice of Allowance in Canadian Appln. No. 3,102,290, dated Jun. 8, 2023, 1 page. [cited by applicant]
Notice of Allowance in European Appln. No. 19745436.6, dated Jun. 21, 2023, 9 pages. [cited by applicant]
Office Action in Australian Appln. No. 2021240206, dated Jul. 28, 2022, 3 pages. [cited by applicant]
Abrams et al., “Full configuration interaction potential energy curves for the X 1 Σ g+, B 1 Δ g, and B′ 1 Σ g [cited by applicant]
Abrams et al., “Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors,” Physical Review Letters, Dec. 1999, 83(24):5162. [cited by applicant]
Abrams et al., “Simulation of many-body Fermi systems on a universal quantum computer,” Physical Review Letters, Sep. 1997, 79(13):2586. [cited by applicant]
Acharya et al., “Metal-insulator transition in copper oxides induced by apex displacements,” Physical Review, May 2018, 8(2):021038. [cited by applicant]
Aspuru-Guzik et al., “Simulated quantum computation of molecular energies,” Science, Sep. 2005, 309(5741):1704-7. [cited by applicant]
Babbush et al., “Adiabatic quantum simulation of quantum chemistry,” Scientific Reports, Oct. 2014, 4(1):1-1. [cited by applicant]
Babbush et al., “Encoding electronic spectra in quantum circuits with linear T complexity,” Physical Review, Oct. 2018, 8(4):041015. [cited by applicant]
Babbush et al., “Low-depth quantum simulation of materials,” Physical Review X, Mar. 2018, 8(1):011044. [cited by applicant]
Bartlett et al., “Alternative coupled-cluster ansätze II. The unitary coupled-cluster method,” Chemical Physics Letters, Feb. 1989, 155(1):133-40. [cited by applicant]
Bauer et al., “Hybrid quantum-classical approach to correlated materials,” Physical Review X, Sep. 2016, 6(3):031045. [cited by applicant]
Bender et al., “Studies in configuration interaction: The first-row diatomic hydrides,” Physical Review, Jul. 1969, 183(1):23. [cited by applicant]
Bernu et al., “Hartree-Fock phase diagram of the two-dimensional electron gas,” Physical Review, Sep. 2011, 84(11):115115. [cited by applicant]
Berry et al., “Improved techniques for preparing eigenstates of fermionic Hamiltonians,” npj Quantum Information, May 2018, 4(1):1-7. [cited by applicant]
Brown et al., “Path-integral Monte Carlo simulation of the warm dense homogeneous electron gas,” Physical Review Letters, Apr. 2013, 110(14):146405. [cited by applicant]
Buenker et al., “Applicability of the multi-reference double-excitation CI (MRD-CI) method to the calculation of electronic wavefunctions and comparison with related techniques,” Molecular Physics, Mar. 1978, 35(3):771-… [cited by applicant]
Ceperley et al., “Ground state of the electron gas by a stochastic method,” Physical Review Letters, Aug. 1980, 45(7):566. [cited by applicant]
Childs et al., “Toward the first quantum simulation with quantum speedup,” Proceedings of the National Academy of Sciences, Sep. 2018, 115(38):9456-61. [cited by applicant]
Cody Jones et al., “Faster quantum chemistry simulation on fault-tolerant quantum computers,” New Journal of Physics, 2012, 14(11). [cited by applicant]
Colless et al., “Computation of molecular spectra on a quantum processor with an error-resilient algorithm,” Physical Review X, Feb. 2018, 8(1):011021. [cited by applicant]
Coulson et al., “Notes on the molecular orbital treatment of the hydrogen molecule,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Apr. 1949, 40(303):386-93. [cited by applicant]
Dallaire-Demers et al., “Low-depth circuit ansatz for preparing correlated fermionic states on a quantum computer,” CoRR, Jan. 2018, arXiv:1801.01053, 15 pages. [cited by applicant]
Dreuw et al., “Single-reference ab initio methods for the calculation of excited states of large molecules,” Chemical Reviews, Nov. 2005, 105(11):4009-37. [cited by applicant]
Dunning et al., “Gaussian basis sets for use in correlated molecular calculations—The atoms boron through neon and hydrogen,” The Journal of Chemical Physics, Jan. 1989, 90(2):1007-23. [cited by applicant]
Ehlers et al., “Hybrid-space density matrix renormalization group study of the doped two-dimensional Hubbard model,” Physical Review, Mar. 2017, 95(12):125125. [cited by applicant]
Feller et al., “A survey of factors contributing to accurate theoretical predictions of atomization energies and molecular structures,” The Journal of Chemical Physics, 2008, 129(20), 204105. [cited by applicant]
Feynman et al., “Simulating physics with computers,” Int. J. Theor. Phys., 1982, 21(6/7). [cited by applicant]
Gan et al., “Calibrating quantum chemistry: A multi-teraflop, parallel-vector, full-configuration interaction program for the Cray-X1,” Proceedings of the ACM/IEEE SC 2005 Conference, 2005, 22-22. [cited by applicant]
Gan et al., “The lowest energy states of the group-IIIA-group-VA heteronuclear diatomics: BN, BP, AIN, and AIP from full configuration interaction calculations,” The Journal of Chemical Physics, Sep. 2006, 125(12):12431… [cited by applicant]
Ge et al., “Faster ground state preparation and high-precision ground energy estimation with fewer qubits,” Journal of Mathematical Physics, Feb. 2019, 60(2):022202. [cited by applicant]
Georges et al., “Dynamical mean-field theory of strongly correlated fermion systems and the limit of infinite dimensions,” Reviews of Modern Physics, Jan. 1996, 68(1):13. [cited by applicant]
Gull et al., “Momentum-space anisotropy and pseudogaps: A comparative cluster dynamical mean-field analysis of the doping-driven metal-insulator transition in the two-dimensional Hubbard model,” Physical Review B, Oct. … [cited by applicant]
Gwaltney et al., “A perturbative correction to the quadratic coupled-cluster doubles method for higher excitations,” Chemical Physics Letters, Feb. 2002, 353(5-6):359-67. [cited by applicant]
Hait et al., “How accurate is density functional theory at predicting dipole moments? An assessment using a new database of benchmark values,” Journal of Chemical Theory and Computation, Mar. 2018, 14(4):1969-81. [cited by applicant]
Hait et al., “Prediction of excited-state energies and singlet-triplet gaps of charge-transfer states using a restricted open-shell Kohn-Sham approach,” Journal of Chemical Theory and Computation, Jul. 2016, 12(7):3353-… [cited by applicant]
Hamilton et al., “Direct inversion in the iterative subspace (DIIS) optimization of open-shell, excited-state, and small multiconfiguration SCF wave functions,” The Journal of Chemical Physics, May 1986, 84(10):5728-34. [cited by applicant]
Huron et al., “Iterative perturbation calculations of ground and excited state energies from multiconfigurational zeroth-order wavefunctions,” The Journal of Chemical Physics, Jun. 1973, 58(12):5745-59. [cited by applicant]
Hwang et al., “Emergence of Kondo resonance in graphene intercalated with cerium,” Nano Letters, May 2018, 18(6):3661-6. [cited by applicant]
Illas et al., “Selected versus complete configuration interaction expansions,” The Journal of Chemical Physics, Aug. 1991, 95(3):1877-83. [cited by applicant]
Jiang et al., “Quantum algorithms to simulate many-body physics of correlated fermions,” Physical Review Applied, Apr. 2018, 9(4):044036. [cited by applicant]
Kitaev et al., “Quantum measurements and the Abelian stabilizer problem,” arXiv preprint quant-ph/9511026, Nov. 1995, 22 pages. [cited by applicant]
Kivlichan et al., “Quantum simulation of electronic structure with linear depth and connectivit,” Physical review letters, Mar. 2018, 120(11):110501. [cited by applicant]
Kohn et al., “Nobel Lecture: Electronic structure of matter—wave functions and density functionals,” Reviews of Modern Physics, Oct. 1999, 71(5):1253. [cited by applicant]
Kotliar et al., “Cellular dynamical mean field approach to strongly correlated systems,” Physical Review Letters, Oct. 2001, 87(18):186401. [cited by applicant]
Kowalczyk et al., “Assessment of the ΔSCF density functional theory approach for electronic excitations in organic dyes,” The Journal of Chemical Physics, Feb. 2011, 134(5):054128. [cited by applicant]
Kowalczyk et al., “Excitation energies and Stokes shifts from a restricted open-shell Kohn-Sham approach,” The Journal of Chemical Physics, Apr. 2013, 138(16):164101. [cited by applicant]
Lanyon et al., “Towards quantum chemistry on a quantum computer,” Nature Chemistry, Feb. 2010, 2(2):106-11. [cited by applicant]
Läuchli et al., “Ground-state energy and spin gap of spin-1 2 Kagomé-Heisenberg antiferromagnetic clusters: Large-scale exact diagonalization results,” Physical Review B, Jun. 2011, 83(21):212401. [cited by applicant]
Low et al., “Hamiltonian simulation by qubitization,” Quantum, Jul. 2019, 3:163. [cited by applicant]
Malone et al., “Accurate exchange-correlation energies for the warm dense electron gas,” Physical Review Letters, Sep. 2016, 117(11):115701. [cited by applicant]
Mardirossian et al., “Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of density functionals,” Molecular Physics, Oct. 2017, 115(19):2315-72. [cited by applicant]
McClean et al., “Barren plateaus in quantum neural network training landscapes,” Nature Communications, Nov. 2018, 9(1):1-6. [cited by applicant]
McClean et al., “Exploiting locality in quantum computation for quantum chemistry” The journal of physical chemistry letters, Dec. 2014, 5(24):4368-80. [cited by applicant]
McClean et al., “Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states,” Physical Review A, Apr. 2017, 95(4):042308. [cited by applicant]
McClean et al., “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics, Feb. 2016, 18(2):023023. [cited by applicant]
Mejuto-Zaera et al., “Dynamical mean field theory simulations with the adaptive sampling configuration interaction method,” Physical Review, Sep. 2019, 100(12):125165. [cited by applicant]
O'Malley et al., “Scalable quantum simulation of molecular energies,” Physical Review X, Jul. 2016, 6(3):031007. [cited by applicant]
Olivares-Amaya et al., “The ab-initio density matrix renormalization group in practice,” The Journal of Chemical Physics, Jan. 2015, 142(3):034102. [cited by applicant]
Ortiz et al., “The challenge of quantum computer simulations of physical phenomena,” Nuclear Physics B-Proceedings Supplements, Mar. 2002, 106:151-8. [cited by applicant]
PCT International Preliminary Report on Patentability in International Appln. No. PCT/US2019/040518, Jan. 21, 2021, 12 pages. [cited by applicant]
PCT International Search Report and Written Opinion in International Appln. No. PCT/US2019/040518, dated Oct. 18, 2019, 19 pages. [cited by applicant]
Perdew et al., Erratum: Accurate and simple analytic representation of the electron-gas correlation energy, Physical Review, Aug. 2018, 98(7):079904. [cited by applicant]
Peruzzo et al., “A variational eigenvalue solver on a photonic quantum processor,” Nature communications, Jul. 2014, 5(1):1-7. [cited by applicant]
Piecuch et al., “Recent advances in electronic structure theory: Method of moments of coupled-cluster equations and renormalized coupled-cluster approaches,” International Reviews in Physical Chemistry, Oct. 2002, 21(4)… [cited by applicant]
Pople et al., “Gaussian-1 theory: A general procedure for prediction of molecular energies,” The Journal of Chemical Physics, May 1989, 90(10):5622-9. [cited by applicant]
Poulin et al., “Quantum algorithm for spectral measurement with a lower gate count,” Physical Review Letters, Jul. 2018, 121(1):010501. [cited by applicant]
Reiher et al., “Elucidating reaction mechanisms on quantum computers,” Proceedings of the National Academy of Sciences, Jul. 2017, 114(29):7555-60. [cited by applicant]
Romero et al., “Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz,” Quantum Science and Technology, Oct. 2018, 4(1):014008. [cited by applicant]
Roth et al., “Importance truncation for large-scale configuration interaction approaches,” Physical Review C, Jun. 2009, 79(6):064324. [cited by applicant]
Rubin et al., “A hybrid classical/quantum approach for large-scale studies of quantum systems with density matrix embedding theory,” arXiv preprint arXiv:1610.06910, Oct. 2016, 10 pages. [cited by applicant]
Sakai et al., “Evolution of electronic structure of doped mott insulators: Reconstruction of poles and zeros of Green's function,” Physical Review Letters, Feb. 2009, 102(5):056404. [cited by applicant]
Santagati et al., “Witnessing eigenstates for quantum simulation of Hamiltonian spectra,” Science Advances, Jan. 2018, 4(1):eaap9646. [cited by applicant]
Sharma et al., “Semistochastic heat-bath configuration interaction method: Selected configuration interaction with semistochastic perturbation theory,” Journal of Chemical Theory and Computation, Apr. 2017, 13(4):1595-6… [cited by applicant]
Shende et al., “Synthesis of quantum-logic circuits,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, May 2006, 25(6):1000-10. [cited by applicant]
Shepherd et al., “Investigation of the full configuration interaction quantum Monte Carlo method using homogeneous electron gas models,” The Journal of Chemical Physics, Jun. 2012, 136(24):244101. [cited by applicant]
Sherrill et al., “The Configuration Interaction Method: Advances in Highly Correlated Approaches,” Advances in Quantum Chemistry, 1999, pp. 143-269. [cited by applicant]
Sordi et al., “Strong coupling superconductivity, pseudogap, and mott transition,” Physical Review Letters, May 2012, 108(21):216401. [cited by applicant]
Szalay et al., “Multiconfiguration self-consistent field and multireference configuration interaction methods and applications,” Chemical Reviews, Jan. 2012, 112(1):108-81. [cited by applicant]
Takeshita et al., “Increasing the representation accuracy of quantum simulations of chemistry without extra quantum resources,” Physical Review X, Jan. 2020, 10(1):011004. [cited by applicant]
Tubman et al., “A deterministic alternative to the full configuration interaction quantum Monte Carlo method,” The Journal of Chemical Physics, Jul. 2016, 145(4):044112. [cited by applicant]
Tubman et al., “An efficient deterministic perturbation theory for selected configuration interaction methods,” arXiv preprint arXiv:1808.02049, Aug. 2018, 13 pages. [cited by applicant]
Tubman et al., “Postponing the orthogonality catastrophe: efficient state preparation for electronic structure simulations on quantum devices,” arXiv preprint arXiv:1809.05523, Sep. 2018, 13 pages. [cited by applicant]
Van Voorhis et al., “The quadratic coupled cluster doubles model,” Chemical Physics Letters, Nov. 2000, 330(5-6):585-94. [cited by applicant]
Vittorio et al., “Quantum Rantdom Access Memory,” Physical Review Letters, Apr. 2008, 100:16(21). [cited by applicant]
Vogiatzis et al., “Pushing configuration-interaction to the limit: Towards massively parallel MCSCF calculations,” The Journal of Chemical Physics, Nov. 2017, 147(18):184111. [cited by applicant]
Vosko et al., “Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis,” Canadian Journal of Physics, Aug. 1980, 58(8):1200-11. [cited by applicant]
Wang et al., “Quantum simulation of helium hydride cation in a solid-state spin register,” ACS nano, Aug. 2015, 9(8):7769-74. [cited by applicant]
Ward et al., “Preparation of many-body states for quantum simulation,” The Journal of Chemical Physics, May 2009, 130(19):194105. [cited by applicant]
Wecker et al., “Gate-count estimates for performing quantum chemistry on small quantum computers,” Physical Review A, Aug. 2014, 90(2):022305. [cited by applicant]
Wecker et al., “Progress towards practical quantum variational algorithms,” Physical Review A, Oct. 2015, 92(4):042303. [cited by applicant]
Wecker et al., “Solving strongly correlated electron models on a quantum computer,” Physical Review A, Dec. 2015, 92(6):062318. [cited by applicant]
Whitfield et al., “Simulation of electronic structure Hamiltonians using quantum computers,” Molecular Physics, Mar. 2011, 109(5):735-50. [cited by applicant]
Woon et al., “Gaussian basis sets for use in correlated molecular calculations—The atoms aluminum through argon, ” The Journal of Chemical Physics, Jan. 1993, 98(2):1358-71. [cited by applicant]
Wu et al., “Polynomial-time simulation of pairing models on a quantum computer,” Physical Review Letters, Jul. 2002, 89(5):057904. [cited by applicant]
Ye et al., “σ-SCF: A direct energy-targeting method to mean-field excited states,” The Journal of chemical physics, Dec. 2017, 147(21):214104. [cited by applicant]
Yung et al., “From transistor to trapped-ion computers for quantum chemistry,” Scientific Reports, Jan. 2014, 4(1):1-7. [cited by applicant]
Zheng et al., “Stripe order in the underdoped region of the two-dimensional Hubbard model,” Science, Dec. 2017, 358(6367):1155-60. [cited by applicant]
AU Office Action in Australian Appln. No. 2019297413, dated Jun. 15, 2021, 4 pages. [cited by applicant]
Office Action in Australian Appln. No. 2023203463, mailed on Sep. 12, 2023, 3 pages. [cited by applicant]
Office Action in Canadian Appln. No. 3,102,290, dated Nov. 26, 2021, 4 pages. [cited by applicant]
Extended European Search Report in European Appln. No. 23206819.7, mailed on Mar. 28, 2024, 10 pages. [cited by applicant]