Circuit-Based Quantum Random Access Memory for Classical Data

Abstract

A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of $M$ entries, each represented by $n$ bits, the method requires $O(n)$ qubits and $O(Mn)$ steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.

9 pages, 5 figures

References in corpus (25)

• Quantum Machine Learning
• Elementary gates for quantum computation
• Quantum algorithm for solving linear systems of equations
• Quantum support vector machine for big data classification
• Quantum principal component analysis
• Quantum fingerprinting
• Quantum random access memory
• Toward the first quantum simulation with quantum speedup
• Quantum Circuits for General Multiqubit Gates
• Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
• Architectures for a quantum random access memory
• Efficient Toffoli Gates Using Qudits
• Quantum Algorithm for the Collision Problem
• Implementing a distance-based classifier with a quantum interference circuit
• Universal fault-tolerant quantum computation with only transversal gates and error correction
• Quantum Circuits for Isometries
• Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
• Demonstration of quantum advantage in machine learning
• Fault-tolerant conversion between the Steane and Reed-Muller quantum codes
• Improved Quantum Cost for n-bit Toffoli Gates
• Efficient Scheme for Initializing a Quantum Register with an Arbitrary Superposed State
• On the robustness of bucket brigade quantum RAM
• Using concatenated quantum codes for universal fault-tolerant quantum gates
• Quantum learning robust to noise
• Programmable Superpositions of Ising Configurations

Cited by in corpus (59)

• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum convolutional neural network for classical data classification
• The Bitter Truth About Quantum Algorithms in the NISQ Era
• The prospects of quantum computing in computational molecular biology
• A divide-and-conquer algorithm for quantum state preparation
• Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicator
• Resilience of quantum random access memory to generic noise
• The theory of the quantum kernel-based binary classifier
• Quantum classification of the MNIST dataset with Slow Feature Analysis
• Linear-depth quantum circuits for multiqubit controlled gates
• Circuit-based quantum random access memory for classical data with continuous amplitudes
• Genuine quantum networks: superposed tasks and addressing
• Resonant Quantum Principal Component Analysis
• Quantum Clustering with k-Means: a Hybrid Approach
• Toward Trainability of Quantum Neural Networks
• Configurable sublinear circuits for quantum state preparation
• Optimal (controlled) quantum state preparation and improved unitary synthesis by quantum circuits with any number of ancillary qubits
• Double sparse quantum state preparation
• Quantum circuit for the fast Fourier transform
• Quantum state preparation protocol for encoding classical data into the amplitudes of a quantum information processing register's wave function
• Quantum Capsule Networks
• Low-rank quantum state preparation
• Quantum random access memory via quantum walk
• Compact quantum kernel-based binary classifier
• Approximate complex amplitude encoding algorithm and its application to data classification problems
• Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers
• Parallel quantum trajectories via forking for sampling without redundancy
• Efficient MPS representations and quantum circuits from the Fourier modes of classical image data
• Black-Box Quantum State Preparation with Inverse Coefficients
• Robust quantum classifier with minimal overhead
• QRAM: A Survey and Critique
• Constant-depth circuits for Boolean functions and quantum memory devices using multi-qubit gates
• Characterization, synthesis, and optimization of quantum circuits over multiple-control $\text{Z}$-rotation gates: A systematic study
• Quantum computing for genomics: conceptual challenges and practical perspectives
• Quantum random access memory with transmon-controlled phonon routing
• Quantum algorithm for the Vlasov simulation of the large-scale structure formation with massive neutrinos
• Quantum density peak clustering
• Quantum Algorithm for Smoothed Particle Hydrodynamics
• Classification and reconstruction for single-pixel imaging with classical and quantum neural networks
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications
• QFCNN: Quantum Fourier Convolutional Neural Network
• A quantum random access memory (QRAM) using a polynomial encoding of binary strings
• Quantum Multiplexer Simplification for State Preparation
• Quantum-classical reinforcement learning for decoding noisy classical parity information
• Quantum Graph Convolutional Neural Networks
• Polynomial T-depth Quantum Solvability of Noisy Binary Linear Problem: From Quantum-Sample Preparation to Main Computation
• $T$-depth-optimized Quantum Search with Quantum Data-access Machine
• The Quantum Monadology
• Comparing Algorithms for Loading Classical Datasets into Quantum Memory
• Refined Criteria for QRAM Error Suppression via Efficient Large-Scale QRAM Simulator
• Sublinear Classical-to-Quantum Data Encoding using $n$-Toffoli Gates
• Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study
• Quantum Algorithms for Unsupervised Machine Learning and Neural Networks
• Detailed Account of Complexity for Implementation of Some Gate-Based Quantum Algorithms
• Schmidt quantum compressor
• A Quantum Convolutional Neural Network for Image Classification
• Quantum-Enhanced Neural Exchange-Correlation Functionals
• Fast variational knowledge graph embedding
• Tucker iterative quantum state preparation