Scalable evaluation of quantum-circuit error loss using Clifford
sampling
- URL: http://arxiv.org/abs/2007.10019v1
- Date: Mon, 20 Jul 2020 11:51:36 GMT
- Title: Scalable evaluation of quantum-circuit error loss using Clifford
sampling
- Authors: Zhen Wang, Yanzhu Chen, Zixuan Song, Dayue Qin, Hekang Li, Qiujiang
Guo, H. Wang, Chao Song, Ying Li
- Abstract summary: We use the quadratic error loss and the final-state fidelity loss to characterize quantum circuits.
It is shown that these loss functions can be efficiently evaluated in a scalable way by sampling from Clifford-dominated circuits.
Our results pave the way towards the optimization-based quantum device and algorithm design in the intermediate-scale quantum regime.
- Score: 8.140947383885262
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A major challenge in developing quantum computing technologies is to
accomplish high precision tasks by utilizing multiplex optimization approaches,
on both the physical system and algorithm levels. Loss functions assessing the
overall performance of quantum circuits can provide the foundation for many
optimization techniques. In this paper, we use the quadratic error loss and the
final-state fidelity loss to characterize quantum circuits. We find that the
distribution of computation error is approximately Gaussian, which in turn
justifies the quadratic error loss. It is shown that these loss functions can
be efficiently evaluated in a scalable way by sampling from Clifford-dominated
circuits. We demonstrate the results by numerically simulating ten-qubit noisy
quantum circuits with various error models as well as executing four-qubit
circuits with up to ten layers of two-qubit gates on a superconducting quantum
processor. Our results pave the way towards the optimization-based quantum
device and algorithm design in the intermediate-scale quantum regime.
Related papers
- QuCLEAR: Clifford Extraction and Absorption for Significant Reduction in Quantum Circuit Size [8.043057448895343]
Currently available quantum devices suffer from noisy quantum gates, which degrade the fidelity of executed quantum circuits.
We present QuCLEAR, a compilation framework designed to optimize quantum circuits.
arXiv Detail & Related papers (2024-08-23T18:03:57Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Fast Flux-Activated Leakage Reduction for Superconducting Quantum
Circuits [84.60542868688235]
leakage out of the computational subspace arising from the multi-level structure of qubit implementations.
We present a resource-efficient universal leakage reduction unit for superconducting qubits using parametric flux modulation.
We demonstrate that using the leakage reduction unit in repeated weight-two stabilizer measurements reduces the total number of detected errors in a scalable fashion.
arXiv Detail & Related papers (2023-09-13T16:21:32Z) - Efficient estimation of trainability for variational quantum circuits [43.028111013960206]
We find an efficient method to compute the cost function and its variance for a wide class of variational quantum circuits.
This method can be used to certify trainability for variational quantum circuits and explore design strategies that can overcome the barren plateau problem.
arXiv Detail & Related papers (2023-02-09T14:05:18Z) - On proving the robustness of algorithms for early fault-tolerant quantum computers [0.0]
We introduce a randomized algorithm for the task of phase estimation and give an analysis of its performance under two simple noise models.
We calculate that the randomized algorithm can succeed with arbitrarily high probability as long as the required circuit depth is less than 0.916 times the dephasing scale.
arXiv Detail & Related papers (2022-09-22T21:28:12Z) - Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most.
We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
arXiv Detail & Related papers (2022-04-12T19:39:31Z) - Scalable error mitigation for noisy quantum circuits produces
competitive expectation values [1.51714450051254]
We show the utility of zero-noise extrapolation for relevant quantum circuits using up to 26 qubits, circuit depths of 60, and 1080 CNOT gates.
We show that the efficacy of the error mitigation is greatly enhanced by additional error suppression techniques and native gate decomposition.
arXiv Detail & Related papers (2021-08-20T14:32:16Z) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z) - Analyzing the Performance of Variational Quantum Factoring on a
Superconducting Quantum Processor [0.0]
We study a QAOA-based quantum optimization algorithm by implementing the Variational Quantum Factoring (VQF) algorithm.
We demonstrate the impact of different noise sources on the performance of QAOA and reveal the coherent error caused by the residual ZZ-coupling between qubits.
arXiv Detail & Related papers (2020-12-14T18:58:30Z) - Boundaries of quantum supremacy via random circuit sampling [69.16452769334367]
Google's recent quantum supremacy experiment heralded a transition point where quantum computing performed a computational task, random circuit sampling.
We examine the constraints of the observed quantum runtime advantage in a larger number of qubits and gates.
arXiv Detail & Related papers (2020-05-05T20:11:53Z)
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.