Machine learning of noise-resilient quantum circuits

Abstract

Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers. In this work, we present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits. Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state. Given a task and a device model that captures information about the noise and connectivity of qubits in a device, NACL outputs an optimized circuit to accomplish this task in the presence of noise. It does so by minimizing a task-specific cost function over circuit depths and circuit structures. To demonstrate NACL, we construct circuits resilient to a fine-grained noise model derived from gate set tomography on a superconducting-circuit quantum device, for applications including quantum state overlap, quantum Fourier transform, and W-state preparation.

arXiv commentPublished version

References in corpus (27)

• A variational eigenvalue solver on a quantum processor
• The theory of variational hybrid quantum-classical algorithms
• Quantum Circuit Learning
• Error mitigation for short-depth quantum circuits
• Extending the computational reach of a noisy superconducting quantum processor
• A simple formula for the average gate fidelity of a quantum dynamical operation
• Efficient variational quantum simulator incorporating active error minimisation
• Quantum autoencoders for efficient compression of quantum data
• t$|$ket$⟩$ : A Retargetable Compiler for NISQ Devices
• Quantum-assisted quantum compiling
• Quantum generalisation of feedforward neural networks
• Quantum teleportation via a W state
• Learning the quantum algorithm for state overlap
• Variational Fast Forwarding for Quantum Simulation Beyond the Coherence Time
• Detecting crosstalk errors in quantum information processors
• Variational Quantum State Diagonalization
• Noise Resilience of Variational Quantum Compiling
• Efficient quantum algorithms for $GHZ$ and $W$ states, and implementation on the IBM quantum computer
• Basic circuit compilation techniques for an ion-trap quantum machine
• Variational Quantum Fidelity Estimation
• Bounding quantum gate error rate based on reported average fidelity
• Detecting and tracking drift in quantum information processors
• Simulating the performance of a distance-3 surface code in a linear ion trap
• Probing quantum processor performance with pyGSTi
• Variational Consistent Histories as a Hybrid Algorithm for Quantum Foundations
• Bootstrapping quantum process tomography via a perturbative ansatz
• Formal Constraint-based Compilation for Noisy Intermediate-Scale Quantum Systems

Cited by in corpus (63)

• The Variational Quantum Eigensolver: a review of methods and best practices
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• Challenges and Opportunities in Quantum Machine Learning
• Generalization in quantum machine learning from few training data
• Gate Set Tomography
• Artificial Intelligence and Machine Learning for Quantum Technologies
• Differentiable Quantum Architecture Search
• Theory for Equivariant Quantum Neural Networks
• Recent advances for quantum classifiers
• A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• Unified approach to data-driven quantum error mitigation
• Out-of-distribution generalization for learning quantum dynamics
• Variational Quantum Eigensolver with Reduced Circuit Complexity
• Suppressing quantum circuit errors due to system variability
• Robust quantum compilation and circuit optimisation via energy minimisation
• Robust resource-efficient quantum variational ansatz through evolutionary algorithm
• Training Saturation in Layerwise Quantum Approximate Optimisation
• Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
• Quantum Architecture Search: A Survey
• Constant-Depth Circuits for Dynamic Simulations of Materials on Quantum Computers
• Unifying and benchmarking state-of-the-art quantum error mitigation techniques
• Quantum Architecture Search with Meta-learning
• Hidden Inverses: Coherent Error Cancellation at the Circuit Level
• Variational Quantum Evolution Equation Solver
• A semi-agnostic ansatz with variable structure for quantum machine learning
• Generative machine learning with tensor networks: benchmarks on near-term quantum computers
• Analytical solution for nonadiabatic quantum annealing to arbitrary Ising spin Hamiltonian
• Efficient variational synthesis of quantum circuits with coherent multi-start optimization
• Error mitigation with Clifford quantum-circuit data
• Optimal quantum dataset for learning a unitary transformation
• Training robust and generalizable quantum models
• GSQAS: Graph Self-supervised Quantum Architecture Search
• Error mitigation on a near-term quantum photonic device
• Variational Quantum-Neural Hybrid Error Mitigation
• Learning How to Dynamically Decouple
• Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis
• Quantum Logic Gate Synthesis as a Markov Decision Process
• A Toffoli Gate Decomposition via Echoed Cross-Resonance Gates
• Deterministic and Entanglement-Efficient Preparation of Amplitude-Encoded Quantum Registers
• Benchmarking quantum gates and circuits
• QFactor: A Domain-Specific Optimizer for Quantum Circuit Instantiation
• The battle of clean and dirty qubits in the era of partial error correction
• Fermionic Simulators for Enhanced Scalability of Variational Quantum Simulation
• Extending the reach of quantum computing for materials science with machine learning potentials
• Exploring ab initio machine synthesis of quantum circuits
• Sequential optimal selection of a single-qubit gate and its relation to barren plateau in parameterized quantum circuits
• Quantum circuit debugging and sensitivity analysis via local inversions
• Quantum computer-enabled receivers for optical communication
• Numerical circuit synthesis and compilation for multi-state preparation
• Scalable Parameter Design for Superconducting Quantum Circuits with Graph Neural Networks
• Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity
• Improving the efficiency of learning-based error mitigation
• Cascaded variational quantum eigensolver algorithm
• Deep Learning for Low-Latency, Quantum-Ready RF Sensing
• Depth scaling of unstructured search via quantum approximate optimization
• Adaptive Circuit Learning of Born Machine: Towards Realization of Amplitude Embedding and Quantum Data Loading
• Peephole Optimization for Quantum Approximate Synthesis
• Implementation of Finite state logic machines via the dynamics of atomic systems
• Quantum fidelity kernel with a trapped-ion simulation platform
• Resilience-Runtime Tradeoff Relations for Quantum Algorithms
• Towards a quantum decision tree in a laser pumped four-level system
• Exploiting biased noise in variational quantum models
• Learning complexity gradually in quantum machine learning models