Quantum pattern recognition with liquid-state nuclear magnetic resonance
arXiv:0802.1592 · doi:10.1103/PhysRevA.79.042321
Abstract
A novel quantum pattern recognition scheme is presented, which combines the idea of a classic Hopfield neural network with adiabatic quantum computation. Both the input and the memorized patterns are represented by means of the problem Hamiltonian. In contrast to classic neural networks, the algorithm can return a quantum superposition of multiple recognized patterns. A proof of principle for the algorithm for two qubits is provided using a liquid state NMR quantum computer.
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References in corpus (14)
- Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
- Quantum Mechanics helps in searching for a needle in a haystack
- A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
- Quantum Search by Local Adiabatic Evolution
- Mathematical Foundation of Quantum Annealing
- Ion-trap quantum logic using long-wavelength radiation
- Effective Pure States for Bulk Quantum Computation
- Size dependence of the minimum excitation gap in the Quantum Adiabatic Algorithm
- Quantum Gates and Circuits
- Optimal Control of Coupled Josephson Qubits
- Efficient Refocussing of One Spin and Two Spin Interactions for NMR Quantum Computation
- Trapped Ion Chain as a Neural Network: Error Resistant Quantum Computation
- Trapped ion chain as a neural network
- Experimental implementation of local adiabatic evolution algorithms by an NMR quantum information processor
Cited by in corpus (15)
- Quantum Machine Learning
- An introduction to quantum machine learning
- The quest for a Quantum Neural Network
- Quantum reservoir processing
- Basic protocols in quantum reinforcement learning with superconducting circuits
- Reconstructing quantum states with quantum reservoir networks
- Machine learning \& artificial intelligence in the quantum domain
- Experimental quantum pattern recognition in IBMQ and diamond NVs
- An Integrated Programming and Development Environment for Adiabatic Quantum Optimization
- Adiabatic Quantum Optimization for Associative Memory Recall
- Exponential capacity of associative memories under quantum annealing recall
- The speed of quantum and classical learning for performing the k-th root of NOT
- Designing ground states of Hopfield networks for quantum state preparation
- The Roles of Kerr nonlinearity in a Bosonic Quantum Neural Network
- Neural networks with quantum states of light