Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era

Abstract

NISQ devices have several physical limitations and unavoidable noisy quantum operations, and only small circuits can be executed on a quantum machine to get reliable results. This leads to the quantum hardware under-utilization issue. Here, we address this problem and improve the quantum hardware throughput by proposing a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously. This approach can also reduce the total runtime of circuits. We first introduce a parallelism manager to select an appropriate number of circuits to be executed at the same time. Second, we present two different qubit partitioning algorithms to allocate reliable partitions to multiple circuits - a greedy and a heuristic. Third, we use the Simultaneous Randomized Benchmarking protocol to characterize the crosstalk properties and consider them in the qubit partition process to avoid the crosstalk effect during simultaneous executions. Finally, we enhance the mapping transition algorithm to make circuits executable on hardware using a decreased number of inserted gates. We demonstrate the performance of our QuMC approach by executing circuits of different sizes on IBM quantum hardware simultaneously. We also investigate this method on VQE algorithm to reduce its overhead.

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Cited by in corpus (19)

• Review of Distributed Quantum Computing. From single QPU to High Performance Quantum Computing
• Simultaneous execution of quantum circuits on current and near-future NISQ systems
• Effects of Dynamical Decoupling and Pulse-level Optimizations on IBM Quantum Computers
• Solving Larger Maximum Clique Problems Using Parallel Quantum Annealing
• Accelerating the variational quantum eigensolver using parallelism
• Networked Quantum Services
• Noise Dynamics of Quantum Annealers: Estimating the Effective Noise Using Idle Qubits
• Crosstalk Attacks and Defence in a Shared Quantum Computing Environment
• Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data
• A Design Framework for the Simulation of Distributed Quantum Computing
• Quantum annealer accelerates the variational quantum eigensolver in a triple-hybrid algorithm
• Cross-platform hardware benchmark of style-based quantum GANs for data augmentation on superconducting and trapped-ion processors
• Quantum Tunneling: From Theory to Error-Mitigated Quantum Simulation
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