Enabling High Performance Debugging for Variational Quantum Algorithms using Compressed Sensing
arXiv:2308.03213 · doi:10.1145/3579371.3589044
Abstract
Variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an ad-hoc manner today, as there are no software tools to configure and tune the VQA hyperparameters. In this paper, we present OSCAR (cOmpressed Sensing based Cost lAndscape Reconstruction) to help configure: 1) correct initialization, 2) noise mitigation techniques, and 3) classical optimizers to maximize the quality of the solution on NISQ hardware. OSCAR enables efficient debugging and performance tuning by providing users with the loss function landscape without running thousands of quantum circuits as required by the grid search. Using OSCAR, we can accurately reconstruct the complete cost landscape with up to 100X speedup. Furthermore, OSCAR can compute an optimizer function query in an instant by interpolating a computed landscape, thus enabling the trial run of a VQA configuration with considerably reduced overhead.
arXiv comment13 pages, 13 figures. KL and TH contributed equally to this work
References in corpus (35)
- A variational eigenvalue solver on a quantum processor
- On the difficulty of training Recurrent Neural Networks
- Barren plateaus in quantum neural network training landscapes
- A Quantum Approximate Optimization Algorithm
- Dynamical suppression of decoherence in two-state quantum systems
- Error mitigation for short-depth quantum circuits
- Demonstration of Two-Qubit Algorithms with a Superconducting Quantum Processor
- Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
- Extending the computational reach of a noisy superconducting quantum processor
- Efficient variational quantum simulator incorporating active error minimisation
- Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
- Mitigating measurement errors in multi-qubit experiments
- Warm-starting quantum optimization
- Error-mitigated digital quantum simulation
- Low-cost error mitigation by symmetry verification
- Q#: Enabling scalable quantum computing and development with a high-level domain-specific language
- Exploiting symmetry in variational quantum machine learning
- Group-Invariant Quantum Machine Learning
- Three Qubit Randomized Benchmarking
- Statistical Assertions for Validating Patterns and Finding Bugs in Quantum Programs
- Theory for Equivariant Quantum Neural Networks
- Symmetry-adapted variational quantum eigensolver
- Bugs in Quantum Computing Platforms: An Empirical Study
- ADAPT: Mitigating Idling Errors in Qubits via Adaptive Dynamical Decoupling
- Symmetry enhanced variational quantum spin eigensolver
- Exploiting Symmetry Reduces the Cost of Training QAOA
- Perturbation impact of spectators on a cross-resonance gate in a tunable coupling superconducting circuit
- Quantum algorithms with local particle number conservation: noise effects and error correction
- Error Mitigation for Deep Quantum Optimization Circuits by Leveraging Problem Symmetries
- Testing platform-independent quantum error mitigation on noisy quantum computers
- Characterizing Error Mitigation by Symmetry Verification in QAOA
- Accelerating Variational Quantum Algorithms Using Circuit Concurrency
- Symmetry adaptation in quantum chemistry calculations on a quantum computer
- EQC : Ensembled Quantum Computing for Variational Quantum Algorithms
- Efficient recovery of variational quantum algorithms landscapes using classical signal processing