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Hyperscaling in the Coherent Hyperspin Machine.
Marcello Calvanese Strinati1, Claudio Conti1,2
1Centro Ricerche Enrico Fermi (CREF), Via Panisperna 89a, 00184 Rome, Italy.
Physical Review Letters
|January 19, 2024
Summary
We developed dimensional annealing for Ising Hamiltonian simulations, significantly improving success rates in large-scale optimization and machine learning tasks. This method enhances performance for quantum and classical Ising devices.
Area of Science:
- Computational Physics
- Quantum Computing
- Machine Learning
Background:
- Ising Hamiltonians are crucial for optimization and machine learning.
- Current quantum and classical devices struggle with scalability, showing decreased success rates as system size increases.
Purpose of the Study:
- To introduce a novel method to improve the performance and scalability of Ising Hamiltonian simulations.
- To counteract the exponential drop in success rates observed in large-scale systems.
Main Methods:
- High-dimensional embeddings of the Ising Hamiltonian.
- A new technique termed "dimensional annealing" to manage system complexity.
- Validation through convergence dynamics in high-performance computing.
Main Results:
- Achieved exponential improvement in success rates and performance metrics.
- Demonstrated a slowed decline in performance with increasing system size.
- Validated the methodology using high-performance computing simulations.
Conclusions:
- Dimensional annealing offers a significant advancement for simulating Ising Hamiltonians.
- The approach is adaptable for various quantum and classical Ising devices, including coherent Ising machines.
- Hyperscaling heuristics provide a generalizable framework for improving Ising device performance.
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