Learning the quantum algorithm for state overlap
Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap $$\mathrm{Tr}(\rho \sigma )$$ between two quantum states ρ and σ. The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ = σ, quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error—compared to the Swap Test—on these computers.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE; LANL Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 1482266
- Alternate ID(s):
- OSTI ID: 1482937
- Report Number(s):
- LA-UR-18-21984
- Journal Information:
- New Journal of Physics, Journal Name: New Journal of Physics Vol. 20 Journal Issue: 11; ISSN 1367-2630
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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