IP Library Granted Patent US 12,198,003
Granted Patent B2
US 12,198,003 · App. 17/360,792 · Granted Jan 14, 2025

Non-boolean quantum amplitude ampification and quantum mean estimation systems and methods

Inventor: Prasanth Shyamsundar (Chicago, IL)
Assignee: Fermi Research Alliance, LLC
G06N10/00G06N10/60H03K19/195G06N10/20
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,198,003
App. No.
17/360,792
Granted
Jan 14, 2025
Kind
B2
Abstract

Generalizations of quantum amplitude amplification and amplitude estimation algorithms work with non-boolean oracles (by way of definition, the action of a non-boolean oracle U φ on an eigenstate |x is to apply a state-dependent phase-shift φ(x); unlike boolean oracles, the eigenvalues exp(iφ(x)) of a non-boolean oracle are not restricted to be ±1). The non-boolean amplitude amplification algorithm preferentially amplifies the amplitudes of the eigenstates based on the value of φ(x). Starting from a given initial superposition state |ψ 0 , the basis states with lower values of cos(φ) are amplified at the expense of the basis states with higher values of cos(φ). The non-boolean quantum mean estimation algorithm uses quantum phase estimation to estimate the expectation ψ 0 |U φ |ψ 0 (i.e., the expected value of exp(iφ(x)) for a random x sampled by making a measurement on |ψ 0 ). The quantum mean estimation algorithm offers a quadratic speedup over its counterpart boolean algorithm known in the art.

Claims (76)

1. A method of performing quantum calculation on an oracle U φ for a non-boolean function φ, comprising:

initializing an ancilla qubit in a |+ state and an input qubit in a |ψ 0 state of a plurality of eigenstates |x to define a two-register state |Ψ 0 ≡|+,ψ 0 ;

for each of a plurality K of iterations

receiving, using the input qubit, a respective one of the plurality of eigenstates |x defining an input basis state,

for odd iterations, acting on the input basis state using a selective phase-flip unitary operator circuit S Ψ 0 and a controlled unitary operator circuit U φ , and

for even iterations, acting on the input basis state using the selective phase-flip unitary operator circuit S Ψ 0 and a controlled inverse unitary operator circuit U φ † .

2. The method according to claim 1 , further comprising measuring, after the plurality K of iterations, the ancilla qubit in a 0/1 basis.

3. The method according to claim 1 , wherein the selective phase-flip unitary operator circuit S Ψ 0 further comprises:

a Hadamard transform H,

a unitary operator A 0 ,

a unitary operator I, and

an inverse unitary operator A 0 † ;

wherein the selective phase-flip unitary operator S Ψ 0 =[H⊗A 0 ][2|0,0 0,0|−I][H⊗A 0 † ].

4. The method according to claim 1 , wherein the acting on the input basis state using the selective phase-flip unitary operator circuit S Ψ 0 and the controlled unitary operator circuit U φ further comprises, for the ancilla qubit in a state |0 , acting on the input qubit as U φ |0,x =e +iφ(x) |0,x , U φ |1,x =e −iφ(x) |1,x .

5. The method according to claim 1 , wherein the acting on the input basis state using the selective phase-flip unitary operator circuit S Ψ 0 and the controlled inverse unitary operator circuit U φ † further comprises, for the ancilla qubit in a state |1 , acting on the input qubit as U φ † |0,x =e +iφ(x) |0,x , U φ † |1,x =e −iφ(x) |1,x .

6. The method according to claim 1 , further comprising:

receiving, using the input qubit, the |ψ 0 state defining an input random state;

acting on the input random state using a controlled estimation unitary operator circuit U φ−π/2 .

7. The method according to claim 6 , wherein the controlled estimation unitary operator circuit U φ−π/2 further comprises:

the controlled unitary operator circuit U φ ,

at least one bit-flip operator X, and

at least one phase-shift operator R ϕ ;

wherein the controlled estimation unitary operator circuit U φ−π/2 =e −iπ/2 |0 0|⊗U φ +e iπ/2 |1 1|⊗U φ † .

8. A quantum computing device for performing quantum calculation on an oracle U φ for a non-boolean function φ, comprising:

a two-register quantum system comprising

an ancilla qubit, and

an input qubit; and

a non-boolean quantum oracle comprising

a selective phase-flip unitary operator circuit S Ψ 0 ,

a controlled unitary operator circuit U φ , and

a controlled inverse unitary operator circuit U φ † ;

wherein the quantum computing device is configured to

initialize the ancilla qubit in a |+ state and the input qubit in a |ψ 0 state of a plurality of eigenstates |x to define a two-register state |Ψ 0 ≡|+,ψ 0 ;

for each of a plurality K of iterations

receive, using the input qubit, a respective one of the plurality of eigenstates |x defining an input basis state,

for odd iterations of the plurality K of iterations, act on the input basis state using the selective phase-flip unitary operator circuit S Ψ 0 and the controlled unitary operator circuit U φ , and

for even iterations of the plurality K of iterations, act on the input basis state using the selective phase-flip unitary operator circuit S Ψ 0 and the controlled inverse unitary operator circuit U φ † .

9. The quantum computing device according to claim 8 , further configured to measure, after the plurality K of iterations, the ancilla qubit in a 0/1 basis.

10. The quantum computing device according to claim 8 , wherein the selective phase-flip unitary operator circuit S Ψ 0 further comprises:

a Hadamard transform H,

a unitary operator A 0 ,

a unitary operator I, and

an inverse unitary operator A 0 † ;

wherein the selective phase-flip unitary operator S Ψ 0 =[H⊗A 0 ][2|0,0 0,0|−I][H⊗A 0 † ].

11. The quantum computing device according to claim 8 , further configured, for the ancilla qubit in a state |0 , to act on the input qubit as U φ |0,x =e +iφ(x) |0,x , U φ |1,x =e −iφ(x) |1,x .

12. The quantum computing device according to claim 8 , further configured, for the ancilla qubit in a state |1 , to act on the input qubit as U φ † |0,x =e +iφ(x) |0,x , U φ † |1,x =e −iφ(x) |1,x .

13. The quantum computing device according to claim 8 , further comprising a controlled estimation unitary operator circuit U φ−π/2 , and configured to:

receive, using the input qubit, the |ψ 0 state defining an input random state; and

act on the input random state using the controlled estimation unitary operator circuit U φ−π/2 .

14. The quantum computing device according to claim 13 , wherein the controlled estimation unitary operator circuit U φ−π/2 further comprises:

the controlled unitary operator circuit U φ ,

at least one bit-flip operator X, and

at least one phase-shift operator R ϕ ;

wherein the controlled estimation unitary operator circuit U φ−π/2 =e −iπ/2 |0 0|⊗U φ +e iπ/2 |1 1|⊗U φ † .

15. A system of quantum circuits for implementing an oracle U q for a non-boolean function φ, the system configured to:

initialize an ancilla qubit in a |+ state and an input qubit in a |ψ 0 state of a plurality of eigenstates |x to define a two-register state |Ψ 0 ≡|+,ψ 0 ;

for each of a plurality K of iterations

receive, using the input qubit, a respective one of the plurality of eigenstates |x defining an input basis state,

for odd iterations of the plurality K of iterations, act on the input basis state using a selective phase-flip unitary operator circuit S Ψ 0 and a controlled unitary operator circuit U φ , and

for even iterations of the plurality K of iterations, act on the input basis state using the selective phase-flip unitary operator circuit S Ψ 0 and a controlled inverse unitary operator circuit U φ † .

16. The system according to claim 15 , further configured to measure, after the plurality K of iterations, the ancilla qubit in a 0/1 basis.

17. The system according to claim 15 , wherein the selective phase-flip unitary operator circuit S Ψ 0 further comprises:

a Hadamard transform H,

a unitary operator A 0 ,

a unitary operator I, and

an inverse unitary operator A 0 † ;

wherein the selective phase-flip unitary operator S Ψ 0 =[H⊗A 0 ][2|0,0 0,0|−I][H⊗A 0 † ].

18. The system according to claim 15 , further configured, for the ancilla qubit in a state |0 , to act on the input qubit as U φ |0,x =e +iφ(x) |0,x , U φ |1,x =e −iφ(x) |1,x .

19. The system according to claim 15 , further configured, for the ancilla qubit in a state |1 , to act on the input qubit as U φ † |0,x =e +iφ(x) |0,x , U φ † |1,x =e −iφ(x) |1,x .

20. The system according to claim 15 , further configured to:

receive, using the input qubit, the |ψ 0 state defining an input random state; and

act on the input random state using a controlled estimation unitary operator circuit U φ−π/2 comprising:

the controlled unitary operator circuit U φ ,

at least one bit-flip operator X, and

at least one phase-shift operator R ϕ ;

wherein the controlled estimation unitary operator circuit U φ−π/2 =e −iπ/2 |0 0|⊗U φ +e iπ/2 |1 1|⊗U φ † .

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 2, 2025
From: FERMI RESEARCH ALLIANCE, LLC
To: FERMI FORWARD DISCOVERY GROUP, LLC
Reel/Frame 069716/0452 →
CONFIRMATORY LICENSE Recorded May 3, 2023
From: FERMI RESEARCH ALLIANCE, LLC
To: UNITED STATES DEPARTMENT OF ENERGY
Reel/Frame 063516/0564 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 28, 2021
From: SHYAMSUNDAR, PRASANTH
To: FERMI RESEARCH ALLIANCE, LLC
Reel/Frame 056692/0113 →
Continuity (1)
Related Publication 20220414508A1 · Dec 29, 2022
References Cited (64)
US 20190019102A1 · Babbush · 2019 [cited by examiner]
US 20190220497A1 · Wiebe · 2019 [cited by examiner]
US 20220067567A1 · O'Brien · 2022 [cited by examiner]
P. Shyamsundar, Non-Boolean quantum amplitude amplification and quantum mean estimation, Quantum Information Processing 22:423 at https://doi.org/10.1007/s11128-023-04146-3, 2023 (Year: 2023). [cited by examiner]
P. Shyamsundar, Non-Boolean Quantum Amplitude Amplification and ML appliations, APS March Meeting 2022 (Year: 2022). [cited by examiner]
“Qiskit: An Open-Source Framework for Quantum Computing”, Version 0.7.2, Zenodo, Jan. 23, 2019. [cited by applicant]
Aaronson, Scott, et al., “Quantum Approximate Counting, Simplified”, Symposium on Simplicity in Algorithms (SOSA), Aug. 28, 2019, pp. 24-32. [cited by applicant]
Arrazola, Juan, et al., “Using Gaussian Boson Sampling to Find Dense Subgraphs,” Physical Review Letters, vol. 121, Issue 3, Jul. 2018, 6 pages. [cited by applicant]
Baritompa, W.P., et al., “Grover's Quantum Algorithm Applied to Global Optimization,” SIAM Journal on Optimization, vol. 15, No. 4, 2005, pp. 1170-1184. [cited by applicant]
Benedetti, Marcell, et al., “Parameterized Quantum Circuits as Machine Learning Models,” Quantum Science and Technology, vol. 4, Nov. 2019, 18 pages. [cited by applicant]
Bennett, Charles H., et al., “Strengths and Weaknesses of Quantum Computing,” SIAM Journal on Computing, vol. 26, No. 5, 1997, pp. 1510-1523. [cited by applicant]
Biham, Eliu, et al., “Grover's Quantum Search Algorithm for an Arbitrary Initial Amplitude,” Physical Review A, vol. 60, No. 4, 1999, pp. 2742-2745. [cited by applicant]
Boyer, Gilles, et al., “Tight Bounds on Quantum Searching,” Fortschritte der Physik, vol. 46, No. 4-5, May 23, 1996, pp. 493-505. [cited by applicant]
Brassard, Gilles, et al., “Column—Quantum Cryptanalysis of Hash and Claw-Free Functions”, Third Latin American Symp. on Theoretical Informatics (Latin '98), 1998, pp. 163-169. [cited by applicant]
Brassard, Gilles, et al., “An Exact Quantum Polynomial-Time Algorithm for Simon's Problem,” Proceedings of the Fifth Israeli Symposium on Theory of Computing and Systems, Jun. 1997, pp. 12-23. [cited by applicant]
Brassard, Gilles, et al., “Quantum Amplitude Amplification and Estimation,” Quantum Computation and Information (AMS Contemporary Mathematics), vol. 305, 2002, pp. 53-74. [cited by applicant]
Brassard, Gilles, et al., “Quantum Counting,” 25th International Colloquium on Automata, Languages, and Programming, May 27, 1998, pp. 820-831. [cited by applicant]
Brassard, Gilles, et al., “Quantum Cryptanalysis of Hash and Claw-Free Functions,” ACM SIGACT News, vol. 28, Issue 2, Jun. 1997, pp. 14-19. [cited by applicant]
Brown, Eric, et al., “Quantum Amplitude Estimation in the Presence of Noise,” arXiv.org, Jun. 26, 2020, 14 pages. [cited by applicant]
Buhrman, Harry, et al., “Quantum Fingerprinting,” Physical Review Letters, vol. 87, Sep. 26, 2001, 8 pages. [cited by applicant]
Chabaud, Ulysse, et al., “Optimal Quantum-Programmable Projective Measurement With Linear Optics,” Physical Review A, vol. 98, Issue 6, Dec. 2018, (Revised Apr. 2020), 11 pages. [cited by applicant]
Chakrabarti, Shouvanik, “A Threshold for Quantum Advantage in Derivative Pricing,” arXiv.org, 2020, 41 pages. [cited by applicant]
Chakrabarty, Indranil, et al., “Dynamic Grover Search: Applications in Recommendation Systems and Optimization Problems,” Quantum Information Processing, Jun. 2017, 15 pages. [cited by applicant]
Chen, Yanhu, et al., “An Optimized Quantum Maximum or Minimum Searching Algorithm and Its Circuits,” arXiv.org, Aug. 21, 2019, 28 pages. [cited by applicant]
Cincio, Lukasz, et al., “Learning the Quantum Algorithm for State Overlap,” New Journal of Physics, vol. 20, Nov. 2018, 12 pages. [cited by applicant]
Cleve, Richard, et al., “Quantum Algorithms Revisited,” Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1996, pp. 339-354. [cited by applicant]
De Beaudrap, J. Niel, “One-Qubit Fingerprinting Schemes,” Physical Review A, vol. 69, Feb. 18, 2004, 9 pages. [cited by applicant]
Dürr, Christoph, et al., “A Quantum Algorithm for Finding the Minimum,” Quantum Physics, Jul. 18, 1996, 2 pages. [cited by applicant]
Fanizza, Marco, et al., “Beyond the Swap Test: Optimal Estimation of Quantum State Overlap,” Physical Review Letters, vol. 124, 2020, 6 pages. [cited by applicant]
Farhi, Edward, et al., “A Quantum Approximate Optimization Algorithm,” arXiv.org, 2014, (Revised Jun. 25, 2015), 13 pages. [cited by applicant]
Farhi, Edward, et al., “Classification with Quantum Neural Networks on Near Term Processors,” arXiv.org, 2018, 21 pages. [cited by applicant]
Farhi, Edward, et al., “Quantum Computation by Adiabatic Evolution,” arXiv.org, Jan. 28, 2000, 24 pages. [cited by applicant]
Farhi, Edward, et al., “Quantum Supremacy through the Quantum Approximate Optimization Algorithm,” arXiv.org, 2016 (Revised 2019), 23 pages. [cited by applicant]
Finnila, A.B., et al., “Quantum Annealing: A New Method for Minimizing Multidimensional Functions,” Chemical Physics Letters, vol. 219, Issues 5-6, 1994, pp. 343-348. [cited by applicant]
Furer, Martin, “Solving NP-Complete Problems with Quantum Search”, Third Latin American Symp. on Theoretical Informatics (Latin '08), 2008, pp. 784-792. [cited by applicant]
Giurgica-Tiron, Tudor, et al., “Low Depth Algorithms for Quantum Amplitude Estimation,” airXiv.org, Dec. 8, 2020, 27 pages. [cited by applicant]
Gong, Changqing, et al., “Quantum K-Means Algorithm Based on Trusted Server in Quantum Cloud Computing,” arXiv.org, 2020, 18 pages. [cited by applicant]
Grant, Edward, et al., “Hierarchical Quantum Classifiers,” npj Quantum Information, vol. 4, pp. 1-8. [cited by applicant]
Grinko, Dmitry, et al., “Iterative Quantum Amplitude Estimation,” Quantum Information, vol. 7, Apr. 20, 2021, p. 53, 13 pages. [cited by applicant]
Grover, Lov K., “A Fast Quantum Mechanical Algorithm for Database Search,” Proceedings of the 28th Annual ACM Symposium on Theory of Computing, Association for Computer Machinery, Philadelphia, Pennsylvania, 1996, pp. 2… [cited by applicant]
Grover, Lov K., “Quantum Computers Can Search Rapidly by Using Almost Any Transformation,” Physical Review Letters, vol. 80, May 1998, pp. 4329-4332. [cited by applicant]
Harrow, Aram W., et al., “Testing Product States, Quantum Merlin-Arthur Games and Tensor Optimization,”, Journal of the ACM, vol. 60, Issue 1, Feb. 2013, pp. 1-43. [cited by applicant]
Harrow, Aram, W., et al., “Quantum Algorithm for Linear Systems of Equations,” Physical Review Letters, vol. 103, Issue 15, Oct. 2009, 4 pages. [cited by applicant]
Havlíček, Vojtěch, et al., “Supervised Learning With Quantum-Enhanced Feature Spaces,” Nature, vol. 567, 2019, pp. 209-212. [cited by applicant]
Hogg, Tad, et al., “Quantum Optimization,” Information Sciences, vol. 128, Issues 3-4, Oct. 1, 2000, pp. 181-197. [cited by applicant]
Kadowaki, Tadashi, et al., “Quantum Annealing in the Transverse Ising Model,” Physical Review E, vol. 58, Issue 5, Nov. 1998, pp. 5355-5363. [cited by applicant]
Kumar, Niraj, “Efficient Quantum Communications With Coherent State Fingerprints Over Multiple Channels”, Physical Review A 95, Mar. 31, 2017, pp. 032337-1-032337-7. [cited by applicant]
Lloyd, Seth, et al., “Quantum Algorithms for Supervised and Unsupervised Machine Learning,” arXiv.org, Jul. 1, 2013 (revised Nov. 4, 2013), 11 pages. [cited by applicant]
Mishra, Nimish, et al., “Quantum Machine Learning: A Review and Current Status,” Data Management, Analytics and Innovation, 2021, 101-145. [cited by applicant]
Mitarai, Kosuke, et al., “Quantum Circuit Learning,” Physical Review A, vol. 98, Issue 3, Sep. 2018, 6 pages. [cited by applicant]
Nakaji, Kouhei, “Faster Amplitude Estimation.” Quantum Information & Computation, vol. 20, 2020, pp. 1109-1122. [cited by applicant]
Nayak, Ashwin, “The Quantum Query Complexity of Approximating the Median and Related Statistics,” Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, May 1999, pp. 384-393. [cited by applicant]
Peruzzo, Alberto, et al., “A Variational Eigenvalue Solver on a Photonic Quantum Processor,” Nature Communications, vol. 5, 2014, 7 pages. [cited by applicant]
Rebentrost, Patrick, et al., “Quantum Support Vector Machine for Big Data Classification,” Physical Review Letters, vol. 113, Sep. 2014, 5 pages. [cited by applicant]
Schuld, Maria, et al., “Circuit-Centric Quantum Classifiers,” Physical Review A, vol. 101, 2020, 17 pages. [cited by applicant]
Schuld, Maria, et al., “Evaluating Analytic Gradients on Quantum Hardware,” Physical Review A, vol. 99, Issue 3, 2019, 7 pages. [cited by applicant]
Sun, Guodong, et al., “Quantum Algorithm for Polynomial Root Finding Program,” 2014 Tenth International Conference on Computational Intelligence and Security, 2014, pp. 469-473. [cited by applicant]
Susuki, Yohichi, et al., “Amplitude Estimation Without Phase Estimation,” Quantum Information Processing, vol. 19, No. 75, Jan. 2020, 17 pages. [cited by applicant]
Svore, Krysta M., et al., “Faster Phase Estimation,” Quantum Information and Computation, vol. 14, Issue 3-4, Mar. 2014, pp. 306-328. [cited by applicant]
Toyama, F. M., et al., “Quantum Search with Certainty Based on Modified Grover Algorithms: Optimum Choice of Parameters,” Quantum Information Processing, vol. 12, Issue 5, 2013, pp. 1897-1914. [cited by applicant]
Verdon, Guillaume, et al., “Quantum Graph Neural Networks”, arXiv: 1909.12264 [quant-ph], Sep. 26, 2019. [cited by applicant]
Wang, Guoming, et al., “Bayesian Inference with Engineered Likelihood Functions for Robust Amplitude Estimation”, Zapata Computing, Inc., Jun. 17, 2020. [cited by applicant]
Wang, Yan, “A Quantum Walk Enhanced Grover Search Algorithm for Global Optimization,” arXiv.org, Nov. 18, 2017, 15 pages. [cited by applicant]
Wiebe, Nathan, et al., “Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning,” Quantum Information & Computation, vol. 15., 2015, pp. 316-356. [cited by applicant]