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Updated: Sep 8, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Tensor-based quantum phase difference estimation for large-scale demonstration.

Shu Kanno1,2, Kenji Sugisaki2,3,4,5, Hajime Nakamura2

  • 1Mitsubishi Chemical Corporation, Science & Innovation Center, Yokohama 227-8502, Japan.

Proceedings of the National Academy of Sciences of the United States of America
|July 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a quantum algorithm for energy calculations, significantly reducing noise and enabling larger quantum computations. It advances quantum computing capabilities on real devices for complex simulations.

Keywords:
quantum chemistryquantum computersquantum phase estimationtensor networks

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Area of Science:

  • Quantum Computing
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Quantum phase estimation (QPE) is crucial for quantum algorithms but faces limitations on current hardware.
  • Noise and qubit count restrict the scale of quantum simulations.

Purpose of the Study:

  • To develop an efficient quantum algorithm for energy calculations.
  • To enhance the scalability and noise resilience of quantum simulations on superconducting devices.

Main Methods:

  • Utilizing a quantum phase difference estimation (QPDE) scheme.
  • Employing tensor-network-based unitary compression for state preparation and time-evolution gates.
  • Implementing Q-CTRL error suppression modules.

Main Results:

  • Demonstrated energy gap calculations for 1D Hubbard models on up to 32 qubits, a five-fold increase over prior QPE.
  • Achieved simulations of linear polyenes up to 21 qubits using spatial orbital localization and index sorting.
  • Showcased exponential reduction in depolarization noise effects.

Conclusions:

  • The QPDE algorithm offers a significant advancement for quantum computing on real devices.
  • The method enables larger-scale quantum simulations for condensed matter and molecular systems.
  • This work overcomes key limitations of existing quantum algorithms for practical applications.