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Quantum process tomography with unsupervised learning and tensor networks.

Giacomo Torlai1,2, Christopher J Wood3, Atithi Acharya4,5

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We developed a new quantum process tomography technique using tensor networks and machine learning. This method efficiently characterizes quantum hardware, significantly reducing data and processing needs for complex quantum circuits.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing Hardware

Background:

  • Quantum technology advancement necessitates scalable methods for quantum hardware characterization.
  • Quantum process tomography (QPT) is crucial for device validation but faces scalability challenges due to exponential data and processing requirements, limiting it to small systems.

Purpose of the Study:

  • To present an efficient quantum process tomography technique.
  • To overcome the scalability limitations of traditional QPT for larger quantum circuits.

Main Methods:

  • Combined tensor network representations of quantum channels with data-driven optimization inspired by unsupervised machine learning.
  • Demonstrated on synthetic data for 1D and 2D random quantum circuits up to 10 qubits, and a noisy 5-qubit circuit.

Main Results:

  • Achieved process fidelities exceeding 0.99.
  • Required several orders of magnitude fewer measurement shots compared to traditional QPT.
  • Demonstrated scalability for quantum circuits up to 10 qubits.

Conclusions:

  • The developed technique offers a practical and timely solution for benchmarking quantum circuits.
  • Significantly reduces the computational burden of quantum process tomography.
  • Enables robust characterization of quantum hardware for current and near-term quantum computers.