A Case for Multi-Programming Quantum Computers

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

doi:10.1145/3352460.3358287

Cited by 82 publications

(75 citation statements)

References 21 publications

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“…3. With four dimensions we can also see the limitations of applying amplitude encoding for large datasets in NISQ era devices which are known for the presence of noise and limited quantum volume [7], [19]. In this work, although amplitude encoding only needs three qubits, the number of gates, or circuit depth, applied to create the state |ψ grew roughly ten fold when compared to the angle encoding method.…”

Section: A Architecture and Designmentioning

confidence: 86%

TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance

2020

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Learning methodologies on quantum devices have shown that there are advantages in utilizing quantum properties. A requirement for using quantum computing in machine learning techniques is the data representation as quantum states. In Quantum Machine Learning, quantum state preparation is paramount to attain a functional pipeline in a model. One state preparation method, amplitude encoding, allows a dataset to be mapped or encoded more robustly and enhances the learning of quantum models. Albeit more densely represented, a dataset which has been prepared by amplitude encoding provides a more learnable input to a model. The two main advantages from using amplitude encoding are an increase in classification accuracy and reduced variability of learning epoch to epoch. In this paper, we compare the basic implementations of TensorFlow Quantum's Quantum Convolutional Neural Network and a hybrid quantum-classical network using angle encoding, with a third network of our design that utilizes amplitude encoding for enriched state preparation. Our results show there is a direct benefit in performing amplitude encoding before training a TensorFlow Quantum hybrid quantum-classical model. In the best case scenario, amplitude encoding made classifying the samples 8.9% more accurate.

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Section: A Architecture and Designmentioning

confidence: 86%

TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance

2020

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“…The baseline is the policy proposed in [2], which generates initial mapping for concurrent quantum programs with FRP strategy and generates mapping transition with the enhanced noise-aware SABRE. With our design, CDAP creates a reliable and close interconnected initial mapping; X-SWAP reduces the compilation overheads.…”

Section: Discussionmentioning

confidence: 99%

“…Our approach works well in practice. The experimental results show that our approach outperforms the latest solution [2] by 12.0% on fidelity and 11.6% on compilation overheads. Furthermore, we design a scheduler for selecting compilation tasks for the best practice of multi-programming, avoiding the performance degradation caused by randomly selected workloads.…”

Section: Introductionmentioning

confidence: 91%

A New Qubits Mapping Mechanism for Multi-programming Quantum Computing

Dou

Liu

2020

Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques

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For a specific quantum chip, multi-programming helps to improve the overall throughput and resource utilization. However, previous solutions for mapping multiple programs 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 physical typology and the error rate, avoiding the waste of robust resources. The second one is the X-SWAP scheme that enables inter-program SWAPs and prioritizes SWAPs associated with critical gates to reduce the SWAP overheads. Finally, we propose a compilation task scheduler, which dynamically selects concurrent quantum programs to be compiled and executed together based on estimated fidelity for the best practice. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ50. Our work outperforms the state-of-the-art work for multi-programming on fidelity and compilation overheads by 12.0% and 11.6%, respectively.

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“…Instead of indiscriminately inverting all outputs, the circuits are run several times to predict the appropriate qubit inversions. Similarly, QCC can benefit from efficient utilization of quantum hardware resources to increase system throughput by running multiple quantum algorithms concurrently on a NISQ computer [47].…”

Section: Algorithm 7 a Simple Dynamic Algorithmmentioning

confidence: 99%

Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

Kusyk

Saeed

2021

IEEE Trans. Quantum Eng.

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Computationally expensive applications, including machine learning, chemical simulations and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum architecture. Quantum circuit compilation (QCC) aims to generate feasible mappings such that a quantum circuit can be executed in a given hardware platform with acceptable confidence in outcomes. Physical constraints of a NISQ computer change frequently, requiring QCC process to be repeated often. When a circuit cannot directly be executed on a quantum hardware due to its physical limitations, it is necessary to modify the circuit by adding new quantum gates and auxiliary qubits, increasing its space and time complexity. An inefficient QCC may significantly increase error rate and circuit latency for even the simplest algorithms. In this survey, we present AI and heuristic based methods recently reported in the literature that attempt to address these QCC challenges. We group them based on underlying techniques that they implement, such as AI algorithms including genetic algorithms, genetic programming, ant colony optimization and AI planning, and heuristics methods employing greedy algorithms, satisfiability problem solvers, dynamic and graph optimization techniques. We discuss performance of each QCC technique and evaluate its potential limitations.

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A Case for Multi-Programming Quantum Computers

Cited by 82 publications

References 21 publications

TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance

TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance

A New Qubits Mapping Mechanism for Multi-programming Quantum Computing

Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

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