IP Library › Granted Patent US 12,288,136
Granted Patent B2
US 12,288,136 · App. 18/654,208 · Granted Apr 29, 2025

Systems and methods for compressed sensing measurement of long-range correlated noise

Inventors: Seyed Alireza Seif Tabrizi (Adelphi, MD); Mohammad Hafezi (Washington, DC); Yi-Kai Liu (Gaithersburg, MD)
Assignees: University of Maryland, College Park; Government of the United States of America, as Represented by the Secretary of Commerce
G06N10/70
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Quick Facts
Patent No.
US 12,288,136
App. No.
18/654,208
Granted
Apr 29, 2025
Kind
B2
Abstract

A method for detecting a two-qubit correlated dephasing error includes accessing a signal of a quantum system, where the quantum system includes a plurality of qubits. Every qubit has a nonzero rate of dephasing and some qubits have a nonzero rate of correlated dephasing. The signal further includes information about a matrix that includes diagonal elements and off-diagonal elements. The off-diagonal elements of the matrix are 2s-sparse. The method further includes performing randomized measurements of the off-diagonal elements of the matrix and recovering the matrix based on a direct measurement of the diagonal elements of the matrix.

Claims (43)

1. A computer-implemented method for detecting error, the method comprising:

accessing a signal of a quantum device, the quantum device including a plurality of qubits, wherein the signal includes information about a matrix, the matrix including diagonal elements and off-diagonal elements, and wherein the off-diagonal elements of the matrix are approximately sparse;

performing randomized measurements of the off-diagonal elements of the matrix by preparing entangled Greenberger-Horne-Zeilinger states (GHZ states) on random subsets of the plurality of qubits;

recovering the matrix based on a direct measurement of the diagonal elements of the matrix; and

estimating at least one of a two-qubit correlated dephasing error or a Hamiltonian error based on the recovered matrix.

2. The computer-implemented method of claim 1 , wherein the randomized measurements are based on noise spectroscopy and quantum sensing.

3. The computer-implemented method of claim 1 , wherein the recovered matrix includes a restricted isometry property.

4. The computer-implemented method of claim 1 , further comprising estimating a vector of decay rates that are dependent on the matrix.

5. The computer-implemented method of claim 4 , wherein the recovered matrix is further based on the estimated decay rate.

6. The computer-implemented method of claim 4 , further comprising estimating a relaxation time of the quantum device based on the estimated vector of decay rates.

7. The computer-implemented method of claim 4 , further comprising estimating a decoherence time of the quantum device based on the estimated vector of decay rates.

8. The computer-implemented method of claim 1 , further comprising detecting a long-range correlated dephasing error based on the recovered matrix.

9. The computer-implemented method of claim 1 , wherein every qubit has a nonzero rate of dephasing.

10. The computer-implemented method of claim 1 , wherein some qubits have a nonzero rate of correlated dephasing.

11. A computer-implemented method for detecting error, the method comprising:

accessing a signal of a quantum device, the quantum device including a plurality of qubits;

dephasing entangled states of the plurality of qubits based on performing Ramsey spectroscopy using entangled states of random subsets of qubits of the plurality of qubits;

measuring a linear function of a correlation matrix, where the correlation matrix corresponds to correlated Markovian dephasing between pairs of qubits of the plurality of qubits;

generating a first vector and a second vector, wherein a plurality of elements of the first vector and of the second vector are randomly chosen; and

estimating a decay rate based on the first vector and the second vector.

12. The computer-implemented method of claim 11 , further comprising generating a restricted isometry property-based recovery matrix, wherein the recovery matrix is further based on the estimated decay rate.

13. The computer-implemented method of claim 12 , further comprising detecting a long-range correlated dephasing error based on the recovery matrix.

14. The computer-implemented method of claim 11 , further comprising estimating a relaxation time of the quantum device based on the estimated decay rate.

15. The computer-implemented method of claim 11 , further comprising estimating a decoherence time of the quantum device based on the estimated decay rate.

16. A system for detecting a two-qubit correlated dephasing error, the system comprising:

a processor; and

a memory, including instructions stored thereon, which when executed by the processor, cause the system to:

access a signal of a quantum device, the signal including a plurality of qubits;

generate a matrix C in R n×n , wherein its off-diagonal part is 2s sparse and wherein the matrix C is based on the accessed signal; and

detect at least one of a long-range correlated dephasing error or a Hamiltonian error based on the generated matrix C.

17. The system of claim 16 , wherein the instructions, when executed by the processor, further cause the system to:

perform randomized measurements based on:

estimating (b−a) T C(b−a) for any a≠b in {0, 1}n;

choosing a sequence of random vectors r(1), . . . , r(m)in {1, 0, −1} n ; and

estimating ΦC=(r (1){circumflex over ( )}T Cr (1) , . . . , (r (m){circumflex over ( )}T Cr (m) ), where Φ:R n×n →R m .

18. The system of claim 17 , wherein the instructions, when executed by the processor, further cause the system to:

estimate a decay rate Γ ab based on any a≠b in {0, 1} n ; and

estimate a decoherence time of the quantum device based on the decay rate Γ ab .

19. The system of claim 18 , wherein the instructions, when executed by the processor, further cause the system to:

recover Matrix C based on:

measuring a plurality of diagonal elements of matrix C directly; and

determining an off-diagonal element of matrix C based on using 1 -regularized least-squares regression.

20. The system of claim 18 , wherein every qubit has a nonzero rate of dephasing.

Continuity (3)
Continuation 17743850 · May 13, 2022
Provisional Application 63188373 · May 13, 2021
Related Publication 20240296368A1 · Sep 5, 2024
References Cited (18)
US 11782779B2 · Albert et al. · 2023 [cited by applicant]
US 12014247B2 · Seif Tabrizi · 2024 [cited by examiner]
US 20060151775A1 · Hollenberg et al. · 2006 [cited by applicant]
US 20190049495A1 · Ofek et al. · 2019 [cited by applicant]
US 20200242500A1 · Girvin et al. · 2020 [cited by applicant]
US 20200334101A1 · Albert et al. · 2020 [cited by applicant]
US 20230359923A1 · Niu · 2023 [cited by examiner]
A. Holmes, M. R. Jokar, G. Pasandi, Y. Ding, M. Pedram and F. T. Chong, “NISQ+: Boosting quantum computing power by approximating quantum error correction,” 2020 ACM/IEEE 47th Annual International Symposium on Computer … [cited by examiner]
S. Brandhofer, S. Devitt and I. Polian, “ArsoNISQ: Analyzing Quantum Algorithms on Near-Term Architectures,” 2021 IEEE European Test Symposium (ETS), Bruges, Belgium, 2021, pp. 1-6, (Year: 2021). [cited by examiner]
T. Patel, A. Potharaju, B. Li, R. B. Roy and D. Tiwari, “Experimental Evaluation of NISQ Quantum Computers: Error Measurement, Characterization, and Implications,” SC20: International Conference for High Performance Com… [cited by examiner]
C. Kim, K. D. Park and J. -K. Rhee, “Quantum Error Mitigation With Artificial Neural Network,” in IEEE Access, vol. 8, pp. 188853-188860, 2020 (Year: 2020). [cited by examiner]
D. Gross, et al., “Quantum State Tomography via Compressed Sensing”, Physical Review Letters 105, 150401, Oct. 8, 2010, pp. 1-4. [cited by applicant]
A. Shabani, et al., “Efficient Measurement of Quantum Dynamics via Compressive Sensing”, Physical Review Letters 106, 100401, Mar. 11, 2011, pp. 1-4. [cited by applicant]
A. Shabani, et al., “Estimation of many-body quantum Hamiltonians via compressive sensing”, Physical Review A 84, 012107, pp. 1-8, (2011). [cited by applicant]
S. T. Flammia, et al., “Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators”, New Journal of Physics 14, 095022, pp. 1-29, (2012). [cited by applicant]
I. Roth, et al., “Recovering Quantum Gates from Few Average Gate Fidelities”, Phys. Rev. Letter 121, 170502, pp. 1-8, (2018). [cited by applicant]
R. Harper, et al., “Fast Estimation of Sparse Quantum Noise”, PRX Quantum 2, 010322, pp. 1-26, (2021). [cited by applicant]
D. Suess, et al., “Rapid characterisation of linear-optical networks via PhaseLift”, (2020), arXiv preprint 2010.00517, pp. 1-26. [cited by applicant]