Performance Survey of Current Gate-Model Quantum Processors
現在のゲート型量子プロセッサの性能調査
Yikai MAO, Masaaki KONDO, Hideharu AMANO
Graduate School of Science and Technology, Keio University

INTRODUCTIONBACKGROUND

Quantum computing is already in practice. As part of theFeasibility Studies on Next-Generation Supercomputing Infrastructures,
we want to investigate and study the current status ofQuantum Computing, especially the performance of the
popular gate-model quantum processors, and the possibilityof using its power to augment the next flagshipsupercomputing system. To summarize:• Survey current gate-model quantum processors• Evaluate programmability on different cloud platforms• Investigate usability with practical quantum algorithms• Test performance, especially their noisy output fidelity

Gate-model quantum computing use quantum
circuits to describe the calculation. Inside aquantum circuit, various quantum gates areapplied to different qubits. These gatesmanipulate the qubits in complex computing
spaces, using superposition and entanglement toachieve exponential speed-up for traditionally hard tasks, like prime factorization.
Currently, the hardware implementation of quantum processors is still imperfect. Oneof the major problem is that they are easily affected by external noise, causing theoutput to distribute around the correct answer. To mitigate the problem, quantumcircuits are usually executed many times to collect the high-probability output.

METHODOLOGY
We surveyed 9 publicly available gate-model quantum processors on the amazon AWS andthe IBM Q cloud platform, listed below:
1.Harmony by IonQ, 11 qubit (ion-trap)
2.Lucy by Oxford Quantum Circuits, 8 qubit (superconducting)
3.Aspen-M-3 by Rigetti, 79 qubit (superconducting)
4.ibm_nairobi by IBM, 7 qubit (superconducting)
5.ibm_oslo by IBM, 7 qubit (superconducting)
6.ibmq_belem by IBM, 5 qubit (superconducting)
7.ibmq_lima by IBM, 5 qubit (superconducting)
8.ibmq_manila by IBM, 5 qubit (superconducting)
9.ibmq_quito by IBM, 5 qubit (superconducting)
The test circuits are picked from the QASMBench opensource benchmark suite, according
to the processor’s topology and computation complexity.

Here is how the figures in the result section are constructed. First, a noise-free simulationof the circuit was performed, generating the expected output distribution in grey. Thenthe circuit was executed on the real processor, producing a noisy output distribution inblue. The two distributions are merged to give an intuitive comparison of how noise
affects the processor’s performance.

RESULT
Due to space limitations, only the top-5 simplest and most complex quantum circuits are displayed here. The first row are the top-5 simplest algorithms: 1. Deutsch algorithm, 2.
Quantum teleportation, 3. entangling swapping gate, 4. Grover’s algorithm, 5. Learning parity with noise. The second row are the top-5 most complex algorithms: 1. Controlled-swap
gate, 2. Quantum repetition code encoder, 3. Quantum ripple-carry adder, 4. Variational ansatz with a linear-swap network, 5. Variational quantum eigensolver with UCCSD.
Harmony by IonQ, 11 qubit (ion-trap)

Lucy by Oxford Quantum Circuits, 8 qubit (superconducting)

Aspen-M-3 by Rigetti, 79 qubit (superconducting)

ibm_nairobi by IBM, 7 qubit (superconducting)

ibm_oslo by IBM, 7 qubit (superconducting)

ibmq_belem by IBM, 5 qubit (superconducting)

ibmq_lima by IBM, 5 qubit (superconducting)ibmq_manila by IBM, 5 qubit (superconducting)

ibmq_quito by IBM, 5 qubit (superconducting)

DISCUSSION

REFERENCES• Current gate-model quantum processors can handle simple quantum computations
with reasonably high fidelity.
• Due to the random nature of some quantum algorithms (e.g., Quantum
teleportation), the performance might not be consistent across different quantumprocessors.
• Complex algorithms that use many CNOT connections, like the Variationalalgorithms, are still challenging for current quantum processors to compute.
• With active research on Quantum Error Correction and better hardware
implementation technologies, the future of quantum computing is extremelypromising.

• Qiskit contributors, Qiskit: An Open-source Framework for Quantum Computing(2023). https://doi.org/10.5281/zenodo.7897504• IBM Quantum (2021). https://quantum-computing.ibm.com/• Amazon Web Services, Amazon Bracket (2020). https://aws.amazon.com/braket/• A. Li, S. Stein, S. Krishnamoorthy, and J. Ang, Qasmbench: A low-level qasm
benchmark suite for NISQ evaluation and simulation (2022).