Majorization-based benchmark of the complexity of quantum processors
- URL: http://arxiv.org/abs/2304.04894v1
- Date: Mon, 10 Apr 2023 23:01:10 GMT
- Title: Majorization-based benchmark of the complexity of quantum processors
- Authors: Alexandre B. Tacla, Nina Machado O'Neill, Gabriel G. Carlo, Fernando
de Melo, and Raul O. Vallejos
- Abstract summary: We numerically simulate and characterize the operation of various quantum processors.
We identify and assess quantum complexity by comparing the performance of each device against benchmark lines.
We find that the majorization-based benchmark holds as long as the circuits' output states have, on average, high purity.
- Score: 105.54048699217668
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Here we investigate the use of the majorization-based indicator introduced in
[R. O. Vallejos, F. de Melo, and G. G. Carlo, Phys. Rev. A 104, 012602 (2021)]
as a way to benchmark the complexity within reach of quantum processors. By
considering specific architectures and native gate sets of currently available
technologies, we numerically simulate and characterize the operation of various
quantum processors. We characterize their complexity for different native gate
sets, qubit connectivity and increasing number of gates. We identify and assess
quantum complexity by comparing the performance of each device against
benchmark lines provided by randomized Clifford circuits and Haar-random pure
states. In this way, we are able to specify, for each specific processor, the
number of native quantum gates which are necessary, on average, for achieving
those levels of complexity. Lastly, we study the performance of the
majorization-based characterization in the presence of distinct types of noise.
We find that the majorization-based benchmark holds as long as the circuits'
output states have, on average, high purity ($\gtrsim 0.9$). In such cases, the
indicator showed no significant differences from the noiseless case.
Related papers
- Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML)
At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
arXiv Detail & Related papers (2024-11-13T23:56:20Z) - YAQQ: Yet Another Quantum Quantizer -- Design Space Exploration of Quantum Gate Sets using Novelty Search [0.9932551365711049]
We present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates.
The developed software, YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates.
arXiv Detail & Related papers (2024-06-25T14:55:35Z) - Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
We show that a quantum processor can correctly solve the basic classification task considered.
With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
arXiv Detail & Related papers (2024-06-17T18:20:51Z) - Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement [39.58317527488534]
Quantum Circuits (PQCs) are still not fully understood outside the scope of their principal application.
We analyse the generation of random states in PQCs under restrictions on the qubits connectivities.
We place a connection between how steep is the increase on the uniformity of the distribution of the generated states and the generation of entanglement.
arXiv Detail & Related papers (2024-05-03T17:32:55Z) - Characterizing quantum processors using discrete time crystals [0.0]
We present a method for characterizing the performance of noisy quantum processors using discrete time crystals.
We construct small sets of qubit layouts that cover the full topology of a target system, and execute our metric over these sets on a wide range of IBM Quantum processors.
arXiv Detail & Related papers (2023-01-18T16:08:50Z) - Scalable fast benchmarking for individual quantum gates with local
twirling [1.7995166939620801]
We propose a character-cycle benchmarking protocol and a character-average benchmarking protocol only using local twirling gates.
We numerically demonstrate our protocols for a non-Clifford gate -- controlled-$(TX)$ and a Clifford gate -- five-qubit quantum error-correcting encoding circuit.
arXiv Detail & Related papers (2022-03-19T13:01:14Z) - Coherent randomized benchmarking [68.8204255655161]
We show that superpositions of different random sequences rather than independent samples are used.
We show that this leads to a uniform and simple protocol with significant advantages with respect to gates that can be benchmarked.
arXiv Detail & Related papers (2020-10-26T18:00:34Z) - Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow [7.619626059034881]
We propose an efficient scheme for quantum circuit equivalence checking.
The proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less.
arXiv Detail & Related papers (2020-09-04T19:58:53Z) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.