Accelerating Variational Quantum Algorithms Using Circuit Concurrency
arXiv:2109.01714
Abstract
Variational quantum algorithms (VQAs) provide a promising approach to achieve quantum advantage in the noisy intermediate-scale quantum era. In this era, quantum computers experience high error rates and quantum error detection and correction is not feasible. VQAs can utilize noisy qubits in tandem with classical optimization algorithms to solve hard problems. However, VQAs are still slow relative to their classical counterparts. Hence, improving the performance of VQAs will be necessary to make them competitive. While VQAs are expected perform better as the problem sizes increase, increasing their performance will make them a viable option sooner. In this work we show that circuit-level concurrency provides a means to increase the performance of variational quantum algorithms on noisy quantum computers. This involves mapping multiple instances of the same circuit (program) onto the quantum computer at the same time, which allows multiple samples in a variational quantum algorithm to be gathered in parallel for each training iteration. We demonstrate that this technique provides a linear increase in training speed when increasing the number of concurrently running quantum circuits. Furthermore, even with pessimistic error rates concurrent quantum circuit sampling can speed up the quantum approximate optimization algorithm by up to 20x with low mapping and run time overhead.
References in corpus (16)
- Quantum Computing in the NISQ era and beyond
- A variational eigenvalue solver on a quantum processor
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- A Quantum Approximate Optimization Algorithm
- Characterizing Quantum Supremacy in Near-Term Devices
- Noise-Induced Barren Plateaus in Variational Quantum Algorithms
- Elucidating Reaction Mechanisms on Quantum Computers
- Towards Practical Quantum Variational Algorithms
- Noise tailoring for scalable quantum computation via randomized compiling
- Experimental Comparison of Two Quantum Computing Architectures
- QAOA for Max-Cut requires hundreds of qubits for quantum speed-up
- Low-distance Surface Codes under Realistic Quantum Noise
- Fast quantum logic gates with trapped-ion qubits
- Quantum computing and the entanglement frontier
- Parallel low-loss measurement of multiple atomic qubits
- For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances