Name
Abstract | ||
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Quantum computation is traditionally expressed in terms of quantum bits, or qubits. In this work, we instead consider three-level qutrits. Past work with qutrits has demonstrated only constant factor improvements, owing to the log2(3) binary-to-ternary compression factor. We present a novel technique using qutrits to achieve a logarithmic depth (runtime) decomposition of the Generalized Toffoli gate using no ancilla-a significant improvement over linear depth for the best qubit-only equivalent. Our circuit construction also features a 70x improvement in two-qudit gate count over the qubit-only equivalent decomposition. This results in circuit cost reductions for important algorithms like quantum neurons and Grover search. We develop an open-source circuit simulator for qutrits, along with realistic near-term noise models which account for the cost of operating qutrits. Simulation results for these noise models indicate over 90% mean reliability (fidelity) for our circuit construction, versus under 30% for the qubit-only baseline. These results suggest that qutrits offer a promising path towards scaling quantum computation.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3307650.3322253 | Proceedings of the 46th International Symposium on Computer Architecture |
Keywords | DocType | Volume |
quantum computing, quantum information, qutrits | Conference | abs/1905.10481 |
ISSN | ISBN | Citations |
1063-6897 | 978-1-4503-6669-4 | 3 |
PageRank | References | Authors |
0.45 | 15 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pranav Gokhale | 1 | 27 | 4.44 |
| Jonathan M. Baker | 2 | 5 | 2.54 |
| Casey Duckering | 3 | 8 | 3.00 |
| Natalie C. Brown | 4 | 4 | 1.15 |
| Kenneth R. Brown | 5 | 29 | 6.08 |
| Frederic T. Chong | 6 | 1428 | 130.07 |