Probabilistic Quantum Memories

Abstract

Typical address-oriented computer memories cannot recognize incomplete or noisy information. Associative (content-addressable) memories solve this problem but suffer from severe capacity shortages. I propose a model of a quantum memory that solves both problems. The storage capacity is exponential in the number of qbits and thus optimal. The retrieval mechanism for incomplete or noisy inputs is probabilistic, with postselection of the measurement result. The output is determined by a probability distribution on the memory which is peaked around the stored patterns closest in Hamming distance to the input.

Revised version to appear in Phys. Rev. Lett

Cited by in corpus (46)

• An introduction to quantum machine learning
• Quantum random access memory
• Quantum walks: a comprehensive review
• The quest for a Quantum Neural Network
• Architectures for a quantum random access memory
• Quantum learning and universal quantum matching machine
• Quantum perceptron over a field and neural network architecture selection in a quantum computer
• Pattern recognition on a quantum computer
• Probabilistic implementation of universal quantum processors
• Experimental realization of quantum cheque using a five-qubit quantum computer
• Demonstration of Entanglement Purification and Swapping Protocol to Design Quantum Repeater in IBM Quantum Computer
• Phase Transitions in Quantum Pattern Recognition
• A Robust Quantum Random Access Memory
• Circuit-based quantum random access memory for classical data with continuous amplitudes
• Complex Quantum Networks: a Topical Review
• Machine learning \& artificial intelligence in the quantum domain
• Configurable sublinear circuits for quantum state preparation
• Quantum-implemented selective reconstruction of high-resolution images
• Advances in quantum machine learning
• A hybrid classical-quantum workflow for natural language processing
• Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks
• Signing Perfect Currency Bonds
• Quantum algorithm for optical template recognition with noise filtering
• Quantum Associative Memory in HEP Track Pattern Recognition
• Storing Images in Entangled Quantum Systems
• Comment on "Probabilistic Quantum Memories"
• A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks
• Encoding classical data into a quantum computer
• Parametric Probabilistic Quantum Memory
• Signatures of avoided energy-level crossings in entanglement indicators obtained from quantum tomograms
• Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
• High-Capacity Quantum Associative Memories
• EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates
• Quantum Optimization for Combinatorial Searches
• Neural Networks Architecture Evaluation in a Quantum Computer
• Quantum enhanced cross-validation for near-optimal neural networks architecture selection
• Quantum computing for pattern classification
• Quantum Multiplexer Simplification for State Preparation
• Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories
• Escaping Local Minima with Quantum Coherent Cooling
• Comparing Algorithms for Loading Classical Datasets into Quantum Memory
• Classical Simulation of Quantum Adiabatic Algorithms using Mathematica on GPUs
• Signatures of nonclassical effects in tomograms
• String Comparison on a Quantum Computer Using Hamming Distance
• Schmidt quantum compressor
• Quantum Algorithms for State Preparation and Data Classification based on Stabilizer Codes