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Experimental Factoring Integers Using Fixed-Point-QAOA with a Trapped-Ion Quantum Processor Cover

Figures & Tables

Figure 1.

General scheme of the QAOA-based Schnorr factoring algorithm.
General scheme of the QAOA-based Schnorr factoring algorithm.

Figure 2.

The example of the rounding refinement in the QAOA-based Schnorr factoring algorithm.
The example of the rounding refinement in the QAOA-based Schnorr factoring algorithm.

Figure 3.

Architecture of the executed quantum circuits in fixed-point-QAOA algorithm. Each pair of connected black circles corresponds to ZZ (χij) gate acting on i-th and j-th qubits, where for each involved qubit pair χij is unique. Angles θi in Rz (θi) gates are also different for each i-th qubit in each circuit. β in Rx (φ) is equal to 2.64. For each of the 9 executed circuits parameters of these gates are given in Table 5 of Supplementary Materials.
Architecture of the executed quantum circuits in fixed-point-QAOA algorithm. Each pair of connected black circles corresponds to ZZ (χij) gate acting on i-th and j-th qubits, where for each involved qubit pair χij is unique. Angles θi in Rz (θi) gates are also different for each i-th qubit in each circuit. β in Rx (φ) is equal to 2.64. For each of the 9 executed circuits parameters of these gates are given in Table 5 of Supplementary Materials.

Figure 4.

Scheme of the fixed-parameters QAOA algorithm: (a) training—search for fixed parameters; (b) using for real problems.
Scheme of the fixed-parameters QAOA algorithm: (a) training—search for fixed parameters; (b) using for real problems.

Figure 5.

A comparison of sr-pairs collection rates between cases where QUBO-subproblems samples are generated with a random sampling (red lines), a noiseless quantum emulator (blue), and a real trapped-ion quantum processor (green) for different number of qubits. The left sub-figure shows both experimental (averaged over 10 runs) and simulation data (averaged over 30 runs), while other figures contain only simulation results (averaged over 10 trajectories). Here N stands for the factorized number, n is for the number of qubits, and Nsh is for the number of shots per circuit. The dashed horizontal line shows a B2 + 1 sr-pairs threshold which guarantees the factorization.
A comparison of sr-pairs collection rates between cases where QUBO-subproblems samples are generated with a random sampling (red lines), a noiseless quantum emulator (blue), and a real trapped-ion quantum processor (green) for different number of qubits. The left sub-figure shows both experimental (averaged over 10 runs) and simulation data (averaged over 30 runs), while other figures contain only simulation results (averaged over 10 trajectories). Here N stands for the factorized number, n is for the number of qubits, and Nsh is for the number of shots per circuit. The dashed horizontal line shows a B2 + 1 sr-pairs threshold which guarantees the factorization.

Figure 6.

Dependency of computational complexity (number of shots) on the number of bits.
Dependency of computational complexity (number of shots) on the number of bits.

Figure 7.

Output states probabilities for circuits 1 and 6 from Table 5 sampled by the quantum processor and the noiseless emulator. Output states are numbered as a decimal representation of the output bitstrings. The first qubit corresponds to the high-order digit in the bitstrings. Each histogram is an average of 2000 shots.
Output states probabilities for circuits 1 and 6 from Table 5 sampled by the quantum processor and the noiseless emulator. Output states are numbered as a decimal representation of the output bitstrings. The first qubit corresponds to the high-order digit in the bitstrings. Each histogram is an average of 2000 shots.

Figure 8.

Output states probabilities sampled by the quantum processor and the noiseless emulator for a set of circuits used to factorize number 437 using 5 qubits. Output states are numbered as a decimal representation of the output bitstrings. The first qubit corresponds to the high-order digit in the bitstrings. Each histogram is an average of 2000 shots.
Output states probabilities sampled by the quantum processor and the noiseless emulator for a set of circuits used to factorize number 437 using 5 qubits. Output states are numbered as a decimal representation of the output bitstrings. The first qubit corresponds to the high-order digit in the bitstrings. Each histogram is an average of 2000 shots.

Parameters of the experimental setup_

ParameterValue
Number of qubits10
Single-qubit gate fidelity99.946(6)%
Two-qubit gate fidelity196.3(3)%
T2*T_2^*30(2) ms
ConnectivityFull
Single-qubit gate duration20 μs
Two-qubit gate duration1.14 ms
Secular frequencies (ωx, ωy, ωz)2π × (3.7, 3.6, 0.13) MHz

Optimal n for different bit-lengths and QUBO-solving methods_

method / nb25262728293031323334353637383940
fpQAOA, p = 111101111111212111212121312141213
fpQAOA, p = 312141214151414141414151515151616
random sampling9999910101010

Comparison of the main results of the NISQ factoring in the current work with previous studies_

MethodNQubitsInteger-specificFull factoring
Schnorr + QAOA [21]48-bit10 trapped-ionnono1
Schnorr + DCQA [31]48-bit10 trapped-ionnono1
VQF [19]41-bit3 superconductingyes2yes
VQE [20]8-bit9 superconductingnoyes
Schnorr + fpQAOA (current work)11-bit6 trapped-ionnoyes

Steps of the factoring_

StepPermutationCircuitMeasurement Resultsr-pair#sr-pairsFactoring
1(1, 3, 2, 5, 6, 4)1010001 0
2(1, 3, 2, 5, 6, 4)1101000 0
3(1, 3, 2, 5, 6, 4)1000100 0
4(1, 3, 2, 5, 6, 4)1001010 0
5(1, 3, 2, 5, 6, 4)1000001 0
6(4, 1, 3, 6,5, 2)2000010 0
7(4, 1, 3, 6, 5, 2)2001101 0
8(4, 1, 3, 6, 5, 2)2000000(1521, 1)1
9(4, 1, 3, 6, 5, 2)2000000 1
10(4, 1, 3, 6, 5, 2)2100000(1690, 1)2
11(3, 5, 2, 6, 4, 1)3001000(5005, 3)3
12(3, 5, 2, 6, 4, 1)3101000 3
13(3, 5, 2, 6, 4, 1)3100001 3
14(3, 5, 2, 6, 4, 1)3001100 3
15(3, 5, 2, 6, 4, 1)3000001 3
16(1, 4, 2, 6, 5, 3)4000010 3
17(1, 4, 2, 6, 5,3)4000000(1625, 1)4
18(1, 4, 2, 6, 5, 3)4001000 4
19(1, 4, 2, 6, 5, 3)4001000 4
20(1, 4, 2, 6, 5, 3)4100000 4
21(1, 5, 4, 2, 3, 6)5000000(1540, 1)5
22(1, 5, 4, 2, 3, 6)5000000 5
23(1, 5, 4, 2, 3, 6)5100000 5
24(1, 5, 4, 2, 3, 6)5010000 5
25(1, 5, 4, 2, 3, 6)5100000 5
26(6, 5, 1, 2, 3, 4)6000001 5
27(6, 5, 1, 2, 3, 4)6101101(41503, 25)6
28(6, 5, 1, 2, 3, 4)6000011 6
29(6, 5, 1, 2, 3, 4)6100110(5775, 4)7
30(6, 5, 1, 2, 3, 4)6010011 7
31(5, 4, 2, 3, 1, 6)7000100 7
32(5, 4, 2, 3, 1, 6)7001010(1375, 1)8
33(5, 4, 2, 3, 1, 6)7000000(1573, 1)9
34(5, 4, 2, 3, 1, 6)7110000 9
35(5, 4, 2, 3, 1, 6)7100100(3185, 2)10
36(5, 6, 2, 4, 1, 3)8010100 10
37(5, 6, 2, 4, 1, 3)8100000 10
38(5, 6, 2, 4, 1, 3)8100010(3125, 2)11
39(5, 6, 2, 4, 1, 3)8011000 11
40(5, 6, 2, 4, 1, 3)8011000 11
41(5, 4, 3, 1, 2, 6)9011010 11
42(5, 4, 3, 1, 2, 6)9001000 11
43(5, 4, 3, 1, 2, 6)9000000(1617, 1)12

Rz and ZZ gates rotation angles of quantum circuits used in the factorization of 1591_

Circuit1Circuit2Circuit3Circuit4Circuit5Circuit6Circuit7Circuit8Circuit9
θ1– 0.6190.190–0.513– 0.619– 0.867–1.6670.400–1.1330.476
θ20.667–1.429–0.3080.6670.133– 0.444–1.067– 3.000– 0.857
θ3–1.095– 0.714–1.436–1.0950.667– 0.556– 0.867–1.267–1.143
θ4– 0.095–1.381–0.205– 0.0950.0670.333– 0.933– 2.067– 0.095
θ5–1.714–1.571–1.026–1.714– 0.267–1.444-0.867–1.200– 0.190
θ6– 0.952– 2.095–0.308– 0.952– 0.7330.444-0.067–1.067– 0.190
χ12– 0.095-0.190–0.026– 0.0950.3000.333– 0.2330.233– 0.286
χ130.0480.0950.1280.048– 0.2330.333– 0.1330.067– 0.190
χ140.024– 0.0480.1280.024– 0.2000– 0.0330.033– 0.238
χ150.048– 0.0240.1030.048– 0.0670.27800.167– 0.238
χ160.095–0.095–0.1280.0950.067–0.389–0.133–0.1330.238
χ23–0.095–0.167–0.231–0.0950.100–0.1670.2000.2000.333
χ240.0480.190–0.2050.048–0.2330.0560.0670.233–0.286
χ25–0.0950.1670.077–0.095–0.2000.0560.1670.1670.048
χ26–0.1900.1900.077–0.190–0.200–0.38900.1670.048
χ34–0.095–0.0480.333–0.095–0.033–0.333–0.167–0.167–0.095
χ350.2140.0710.1790.214–0.167–0.278–0.133–0.133–0.190
χ360.0240.071–0.1280.024–0.2000.2220.1330.1330.143
χ450–0.119–0.1280–0.16700.3330.333–0.286
χ46–0.1430.333–0.179–0.143–0.100–0.389–0.233–0.067–0.048
χ560.2140.119–0.1280.2140–0.444–0.100–0.267–0.286
DOI: https://doi.org/10.2478/qic-2025-0021 | Journal eISSN: 3106-0544 | Journal ISSN: 1533-7146
Language: English
Page range: 369 - 384
Submitted on: Mar 28, 2025
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Accepted on: Jul 29, 2025
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Published on: Dec 31, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Ilia V. Zalivako, Andrey Yu. Chernyavskiy, Anastasiia S. Nikolaeva, Alexander S. Borisenko, Nikita V. Semenin, Kristina P. Galstyan, Andrey E. Korolkov, Sergey V. Grebnev, Evgeniy O. Kiktenko, Ksenia Yu. Khabarova, Aleksey K. Fedorov, Ilya A. Semerikov, Nikolay N. Kolachevsky, published by Cerebration Science Publishing Co., Limited
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