Quantum random access memory.

By Vittorio Giovannetti, Seth Lloyd, Lorenzo Maccone. Physical Review Letters, 2007

942 citations recorded by the source corpus.

A random access memory (RAM) uses n bits to randomly address N=2(n) distinct memory cells. A quantum random access memory (QRAM) uses n qubits to address any quantum superposition of N memory cells. We present an architecture that exponentially reduces the requirements for a memory call: O(logN) switches need be thrown instead of the N used in conventional (classical or quantum) RAM designs. This yields a more robust QRAM algorithm, as it in general requires entanglement among exponentially less gates, and leads to an exponential decrease in the power needed for addressing. A quantum optical implementation is presented.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/8578cf1abe804dbe7c53ddd09dd0a701fad81547

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

Quantum Generative Adversarial Networks for learning and loading random distributions (2019, background): “A common assumption considering data loading in quantum algorithms is that access to the required data is provided by a Quantum Random Access Memory (QRAM) [6].”
Quantum algorithm for linear systems of equations. (2008, background): “[26] Vittorio Giovannetti, Seth Lloyd, and Lorenzo Maccone.”
Quantum-enhanced machine learning (2016, background): “There, the classical source of samples is substituted by a quantum RAM [25] architecture, which allows for the accessing of many samples in superposition.”
Quantum machine learning: a classical perspective (2017, background): “The proponents of the QRAM [45,46] claim that only O(log(Nd)) components need to be active, while the others can be considered as ‘non-active’ and error-free.”
Quantum convolutional neural network for classical data classification (2021, methodology): “[35] Vittorio Giovannetti, Seth Lloyd, and Lorenzo Maccone.”
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity (2018, methodology): “Many quantum algorithms assume the existence of a hypothetical peripheral called “ quantum random-access memory ” (QRAM) [63], which allows classical or quantum data to be accessed via an index under super-position.”
Quantum machine learning (2016, positive): “utational complexity and query complexity. For quantum principal component analysis of classical data (qPCA [11]), we choose a data vector ~v j at random, and use a quantum random access memory (qRAM [41]) to map that vector into a quantum state: ~v j !jv ji. The quantum state that summarizes the ve”
Challenges and opportunities in quantum optimization (2023, neutral): “Although concrete proposals to build QRAM exist 106,107 , a scalable fault-tolerant implementation is yet to be found 108–110 , making this at best a long-term prospect.”
The future of quantum computing with superconducting qubits (2022, background): “(We leave aside speedups obtained in the so-called Quantum RAM model 25 , for although it appears to be more powerful than the standard quantum circuit model, it is unclear whether a Quantum RAM can be eﬃciently implemented in any real physical system.)”
An introduction to quantum machine learning (2014, methodology): “ done faster on a quantum computer. Given the quantum state6 j˜i= 1=p N ˜ P 2n i=1 j~x ijjiix , with 6The initial state can be constructed by using a Quantum Random Access Memory oracle described in [44], accessing a 9 N ˜= P 2n i=1 j~x ij2. The xi are a 2n-dimensional basis of the training vector s”