Quantum Random Access Memory: High-bandwidth solution and low-overhead QEC protocol.

Quantum Random Access Memory (QRAM) is a crucial architectural component for querying classical or quantum data in superposition, enabling algorithms with wide-ranging applications in quantum arithmetic, quantum chemistry, machine learning, and quantum cryptography. In this talk, we introduce a systematic QRAM framework, focusing on the improvement of QRAM’s throughput and error robustness.

Specifically, we will first introduce a novel query architecture called Fat-Tree QRAM, which is capable of pipelining multiple quantum queries simultaneously while maintaining desirable scalings in query speed and fidelity. We will also demonstrate its experimental feasibility, by proposing modular and on-chip implementations of Fat-Tree QRAM based on superconducting circuits and analyzing their performance and fidelity under realistic parameters. Furthermore, a query scheduling protocol is presented to maximize hardware utilization and access the underlying data at an optimal rate.

We then introduce a new low-overhead error correction methodology for QRAM, which enables stabilizer checks in QRAM without introducing extra data qubits for encoding. When combined with a fault-tolerant CSWAP gate for [[4,2,2]] code under the erasure error channel, QRAM query fidelity could be asymptotically improved with minimum overhead. By numerical simulation, the validated results suggest that QRAM is a noise-robust architecture that is possibly achievable even with NISQ hardware.