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IEEE/ACM International Symposium on Microarchitecture (MICRO)

A Case for Multi-Programming Quantum Computers

Poulami Das, Swamit Tannu, Prashant J. Nair, Moinuddin K. Qureshi

Published Oct 11, 2019• 129 citations
Publisher

Abstract

Existing and near-term quantum computers face significant reliability challenges because of high error rates caused by noise. Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) model of computing. As NISQ machines exhibit high error-rates, only programs that require a few qubits can be executed reliably. Therefore, NISQ machines tend to underutilize its resources. In this paper, we propose to improve the throughput and utilization of NISQ machines by using multi-programming and enabling the NISQ machine to concurrently execute multiple workloads.

DOI
10.1145/3352460.3358287

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arXiv:2004.12854•Apr 27, 2020•1 citation
Existing and near-term quantum computers face significant reliability challenges because of high error rates caused by noise. Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) model of computing. As NISQ machines exhibit high error-rates, only programs that require a few qubits can be executed reliably. Therefore, NISQ machines tend to underutilize its resources. In this paper, we propose to improve the throughput and utilization of NISQ machines by using multi-programming and enabling the NISQ machine to concurrently execute multiple workloads.
Today's quantum computing is performed on Noisy Intermediate-Scale Quantum (NISQ) hardware. The NISQ era allows for a limited number of qubits with several physical limitations. As NISQ devices have unavoidable noisy quantum operations, we can execute only small circuits on a quantum machine to get reliable results. But, this leads to the quantum hardware under-utilization issue. With the increase of hardware qubit number and the improvement of error rates, it becomes possible to execute multiple circuits on a quantum chip simultaneously, enhancing the throughput and the utilization of NISQ hardware. Thus, we address a timely problem on how to execute reliably multiple quantum programs on given quantum hardware. This is the first attempt to propose a complete multi-programming process flow for executing an optimal number of workloads in parallel, ensuring output fidelity by analyzing the hardware limitations.
Quantum computing promises exponential speedups for an important class of problems.While quantum computers with few dozens of qubits have been demonstrated, these machines suffer from a high rate of gate errors.Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) mode of computing where the output of the machine can be erroneous.In this talk, I will discuss some of our recent work that aims to improve the reliability of NISQ computers by developing software techniques to mitigate hardware errors.Our first work exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid qubits that are error-prone.Our second work looks at executing different versions of the programs each crafted to cause diverse mistakes so that the machine becomes less vulnerable to correlated errors.Our third work looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to perform the measurement in the stronger state.We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability.Finally, I will also briefly discuss the hardware aspect of designing largescale quantum computers, including cryogenic processor and cryogenic memory system.
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.
Noisy Intermediate-Scale Quantum (NISQ) computers are being increasingly used for executing early-stage quantum programs to establish the practical realizability of existing quantum algorithms. These quantum programs have uses cases in the realm of high-performance computing ranging from molecular chemistry and physics simulations to addressing NP-complete optimization problems. However, NISQ devices are prone to multiple types of errors, which affect the fidelity and reproducibility of the program execution. As the technology is still primitive, our understanding of these quantum machines and their error characteristics is limited. To bridge that understanding gap, this is the first work to provide a systematic and rich experimental evaluation of IBM Quantum Experience (QX) quantum computers of different scales and topologies. Our experimental evaluation uncovers multiple important and interesting aspects of benchmarking and evaluating quantum program on NISQ machines. We have open-sourced our experimental framework and dataset to help accelerate the evaluation of quantum computing systems.
The development of quantum computing relies heavily on the effective execution of quantum algorithms. However, current Noisy Intermediate-Scale Quantum (NISQ) computers are plagued by limitations such as short coherence times and significant error rates, posing substantial challenges to scheduling and execution. This paper introduces a novel dynamic resource-aware scheduling algorithm designed to mitigate these challenges. The core of the algorithm lies in its ability to adapt to the fluctuating noise levels and coherence times of the quantum hardware, intelligently prioritizing tasks based on their estimated computational cost and error probability. We demonstrate that this adaptive approach significantly improves the potential for successful quantum algorithm execution on NISQ devices, offering a pathway towards more efficient and reliable quantum computation. The algorithm's effectiveness is evaluated through theoretical analysis and conceptual modeling, focusing on the optimization of resource allocation and task prioritization within the constraints of NISQ technology. This research contributes to the growing field of quantum resource management and scheduling, providing a framework for tackling the complexities inherent in operating quantum computers.
For a specific quantum chip, multi-programming helps to improve overall throughput and resource utilization. However, the previous solutions for mapping multiple programs onto a quantum chip often lead to resource under-utilization, high error rate and low fidelity. In this paper, we propose a new approach to map concurrent quantum programs. Our approach has three critical components. The first one is the Community Detection Assisted Partition (CDAP) algorithm, which partitions physical qubits for concurrent quantum programs by considering both physical typology and the error rates, avoiding the waste of robust resources. The second one is the X-SWAP scheme that enables inter-program SWAP operations to reduce the SWAP overheads. Finally, we propose a compilation task scheduling framework, which dynamically selects concurrent quantum programs to be executed based on estimated fidelity, increasing the throughput of the quantum computer. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ20. Our work outperforms the previous solution on multi-programming in both fidelity and SWAP overheads by 12.0% and 11.1%, respectively.