Demonstration of quantum advantage in machine learning

Abstract

The main promise of quantum computing is to efficiently solve certain problems that are prohibitively expensive for a classical computer. Most problems with a proven quantum advantage involve the repeated use of a black box, or oracle, whose structure encodes the solution. One measure of the algorithmic performance is the query complexity, i.e., the scaling of the number of oracle calls needed to find the solution with a given probability. Few-qubit demonstrations of quantum algorithms, such as Deutsch-Jozsa and Grover, have been implemented across diverse physical systems such as nuclear magnetic resonance, trapped ions, optical systems, and superconducting circuits. However, at the small scale, these problems can already be solved classically with a few oracle queries, and the attainable quantum advantage is modest. Here we solve an oracle-based problem, known as learning parity with noise, using a five-qubit superconducting processor. Running classical and quantum algorithms on the same oracle, we observe a large gap in query count in favor of quantum processing. We find that this gap grows by orders of magnitude as a function of the error rates and the problem size. This result demonstrates that, while complex fault-tolerant architectures will be required for universal quantum computing, a quantum advantage already emerges in existing noisy systems

8 pages, including 4 figures and methods. 7 supplementary figures, 1 supplementary table

References in corpus (8)

• An introduction to quantum machine learning
• Simple pulses for elimination of leakage in weakly nonlinear qubits
• Detecting arbitrary quantum errors via stabilizer measurements on a sublattice of the surface code
• Complete universal quantum gate set approaching fault-tolerant thresholds with superconducting qubits
• Observation of quantum jumps in a superconducting artificial atom
• Protocols for optimal readout of qubits using a continuous quantum nondemolition measurement
• Robust Calibration of a Universal Single-Qubit Gate-Set via Robust Phase Estimation
• Frequency Division Multiplexing Readout and Simultaneous Manipulation of an Array of Flux Qubits

Cited by in corpus (68)

• A Quantum Engineer's Guide to Superconducting Qubits
• Superconducting Qubits: Current State of Play
• Parameterized quantum circuits as machine learning models
• Superconducting Quantum Computing: A Review
• Strawberry Fields: A Software Platform for Photonic Quantum Computing
• Demonstration of fidelity improvement using dynamical decoupling with superconducting qubits
• Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
• Circuit-Based Quantum Random Access Memory for Classical Data
• Using Gaussian Boson Sampling to Find Dense Subgraphs
• Fault-Tolerant Logical Gates in the IBM Quantum Experience
• 8x8 Reconfigurable quantum photonic processor based on silicon nitride waveguides
• Quantum reservoir computing with a single nonlinear oscillator
• Anomaly detection in high-energy physics using a quantum autoencoder
• Quantum Machine Learning: from physics to software engineering
• Analytical modeling of parametrically-modulated transmon qubits
• On the Quantum versus Classical Learnability of Discrete Distributions
• Hardware for Dynamic Quantum Computing
• Towards quantum enhanced adversarial robustness in machine learning
• Quantum kernels for real-world predictions based on electronic health records
• A perspective on protein structure prediction using quantum computers
• Algorithmic simulation of far-from-equilibrium dynamics using quantum computer
• Variational inference with a quantum computer
• Kinetic Inductance Traveling Wave Amplifiers For Multiplexed Qubit Readout
• Designing Quantum Router in IBM Quantum Computer
• Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
• Applications of Machine Learning in Cryptography: A Survey
• Machine learning \& artificial intelligence in the quantum domain
• Quantum-Noise-Driven Generative Diffusion Models
• Structural risk minimization for quantum linear classifiers
• Speeding-up the decision making of a learning agent using an ion trap quantum processor
• AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images
• Bootstrapping quantum process tomography via a perturbative ansatz
• Experimental quantum pattern recognition in IBMQ and diamond NVs
• Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
• Optimal Usage of Quantum Random Access Memory in Quantum Machine Learning
• Networked Quantum Services
• Quantum communication protocols as a benchmark for quantum computers
• Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms
• Sensor-assisted fault mitigation in quantum computation
• Extending qubit coherence by adaptive quantum environment learning
• Quantum analysis of second-order effects in superconducting travelling-wave parametric amplifiers
• Density functionals and Kohn-Sham potentials with minimal wavefunction preparations on a quantum computer
• Fabrication of the impedance-matched Josephson parametric amplifier and the study of the gain profile
• Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
• Experimental Quantum Learning of a Spectral Decomposition
• Hybrid quantum learning with data re-uploading on a small-scale superconducting quantum simulator
• Noise-tolerant parity learning with one quantum bit
• Satellite image classification with neural quantum kernels
• Binary Classification with Classical Instances and Quantum Labels
• Digital-analog quantum learning on Rydberg atom arrays
• Quantum inspired qubit qutrit neural networks for real time financial forecasting
• Nondestructive classification of quantum states using an algorithmic quantum computer
• From reasonable postulates to generalised Hamiltonian systems
• Material matters in superconducting qubits
• The role of coherence theory in attractor quantum neural networks
• Quantum Learning Boolean Linear Functions w.r.t. Product Distributions
• Entanglement generation via single-qubit rotations in a torn Hilbert space
• Quantum-classical reinforcement learning for decoding noisy classical parity information
• Simulation of open quantum systems on universal quantum computers
• Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images
• Non-local resources for error correction in quantum LDPC codes
• Supervised binary classification of small-scale digit images and weighted graphs with a trapped-ion quantum processor
• SuperGrad: a differentiable simulator for superconducting processors
• Quantum circuit-like learning: A fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning
• Generation of time-frequency entangled photon pairs propagating in separate waveguides in circuit QED setup
• The Virtual Quantum Device (VQD): A tool for detailed emulation of quantum computers
• Quantum feature-map learning with reduced resource overhead
• Real-time hybrid quantum-classical computations for trapped-ions with Python control-flow