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most citedApproximate Quantum Random Access Memory Architectures

5 citations · 6 across the 4 of their papers we have counts for

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quant-ph2024

Optimizing Quantum Embedding using Genetic Algorithm for QML Applications

Koustubh Phalak, Archisman Ghosh, Swaroop Ghosh

Quantum Embeddings (QE) are essential for loading classical data into quantum systems for Quantum Machine Learning (QML). The performance of QML algorithms depends on the type of Q…

quant-ph20225 cited

Approximate Quantum Random Access Memory Architectures

Koustubh Phalak, Junde Li, Swaroop Ghosh

Quantum supremacy in many applications using well-known quantum algorithms rely on availability of data in quantum format. Quantum Random Access Memory (QRAM), an equivalent of cla…

quant-ph20221 cited

Optimization of Quantum Read-Only Memory Circuits

Koustubh Phalak, Mahabubul Alam, Abdullah Ash-Saki +2

Quantum computing is a rapidly expanding field with applications ranging from optimization all the way to complex machine learning tasks. Quantum memories, while lacking in practic…

quant-ph2021

A Survey and Tutorial on Security and Resilience of Quantum Computing

Abdullah Ash Saki, Mahabubul Alam, Koustubh Phalak +3

Present-day quantum computers suffer from various noises or errors such as gate error, relaxation, dephasing, readout error, and crosstalk. Besides, they offer a limited number of…

quant-ph2021

Quantum PUF for Security and Trust in Quantum Computing

Koustubh Phalak, Abdullah Ash-Saki, Mahabubul Alam +2

Quantum computing is a promising paradigm to solve computationally intractable problems. Various companies such as, IBM, Rigetti and D-Wave offer quantum computers using a cloud-ba…