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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Hamiltonian simulation algorithms for near-term quantum hardware.

Laura Clinton1,2, Johannes Bausch3,4, Toby Cubitt5

  • 1PhaseCraft Ltd., London, UK. laura.clinton.17@ucl.ac.uk.

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This summary is machine-generated.

We developed new quantum algorithms for Hamiltonian simulation that significantly reduce circuit depth by operating closer to the hardware. This makes complex quantum simulations more feasible for current noisy quantum computers.

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Area of Science:

  • Quantum Computing
  • Quantum Simulation
  • Condensed Matter Physics

Background:

  • The quantum circuit model is standard for quantum algorithms but incurs overhead.
  • Abstraction from hardware limits the efficiency of quantum simulations.

Purpose of the Study:

  • To develop quantum algorithms for Hamiltonian simulation operating below the standard circuit model.
  • To reduce overhead and improve feasibility for quantum simulations on current hardware.

Main Methods:

  • Developed quantum algorithms exploiting direct qubit interaction control.
  • Derived analytic circuit identities for synthesizing multi-qubit evolutions from two-qubit interactions.
  • Analyzed algorithm performance under per-gate and per-unit-time error models.

Main Results:

  • Reduced circuit depth for simulating a 5x5 Fermi-Hubbard lattice from 1,243,586 to 3,209 (per-gate error model).
  • Achieved a circuit-depth-equivalent of 259 under a per-time error model.
  • Demonstrated significant reductions in circuit depth for Hamiltonian simulation.

Conclusions:

  • The developed techniques bring Hamiltonian simulation closer to feasibility in the NISQ era.
  • Operating "one level below" the circuit model offers substantial efficiency gains.
  • New error bounds and analysis tailored for non-asymptotic regimes improve simulation accuracy.