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Parallelising the Queries in Bucket Brigade Quantum RAM

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arxiv 2002.09340 v2 pith:JQQTNGDJ submitted 2020-02-21 quant-ph cs.ET

classification quant-phcs.ET
keywords quantumtimebrigadebucketqueriesancillaeeffectivemethod
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Quantum algorithms often use quantum RAMs (QRAM) for accessing information stored in a database-like manner. QRAMs have to be fast, resource efficient and fault-tolerant. The latter is often influenced by access speeds, because shorter times introduce less exposure of the stored information to noise. The total execution time of an algorithm depends on the QRAM access time which includes: 1) address translation time, and 2) effective query time. The bucket brigade QRAMs were proposed to achieve faster addressing at the cost of exponentially many ancillae. We illustrate a systematic method to significantly reduce the effective query time by using Clifford+T gate parallelism. The method does not introduce any ancillae qubits. Our parallelisation method is compatible with the surface code quantum error correction. We show that parallelisation is a result of advantageous Toffoli gate decomposition in terms of Clifford+T gates, and after addresses have been translated, we achieve theoretical $\mathcal{O}(1)$ parallelism for the effective queries. We conclude that, in theory: 1) fault-tolerant bucket brigade quantum RAM queries can be performed approximately with the speed of classical RAM; 2) the exponentially many ancillae from the bucket brigade addressing scheme are a trade-off cost for achieving exponential query speedup compared to quantum read-only memories whose queries are sequential by design. The methods to compile, parallelise and analyse the presented QRAM circuits were implemented in software which is available online.

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  1. A distillation-teleportation protocol for fault-tolerant QRAM

    quant-ph 2025-05 accept novelty 8.0 of 10

    An adaptive distillation-teleportation protocol implements a fault-tolerant QRAM query with poly(n) quantum resources and 1/poly(n) device fidelity, at the cost of an exponential classical dataset update each round.

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