Machine learning of noise-resilient quantum circuits
- URL: http://arxiv.org/abs/2007.01210v2
- Date: Sun, 21 Feb 2021 15:37:26 GMT
- Title: Machine learning of noise-resilient quantum circuits
- Authors: Lukasz Cincio, Kenneth Rudinger, Mohan Sarovar, Patrick J. Coles
- Abstract summary: Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers.
We present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits.
Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state.
- Score: 0.8258451067861933
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Noise mitigation and reduction will be crucial for obtaining useful answers
from near-term quantum computers. In this work, we present a general framework
based on machine learning for reducing the impact of quantum hardware noise on
quantum circuits. Our method, called noise-aware circuit learning (NACL),
applies to circuits designed to compute a unitary transformation, prepare a set
of quantum states, or estimate an observable of a many-qubit state. Given a
task and a device model that captures information about the noise and
connectivity of qubits in a device, NACL outputs an optimized circuit to
accomplish this task in the presence of noise. It does so by minimizing a
task-specific cost function over circuit depths and circuit structures. To
demonstrate NACL, we construct circuits resilient to a fine-grained noise model
derived from gate set tomography on a superconducting-circuit quantum device,
for applications including quantum state overlap, quantum Fourier transform,
and W-state preparation.
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