Quantum circuit optimization for multiple QPUs using local structure

Tham, Edwin; Khait, Ilia; Brodutch, Aharon

doi:10.48550/arxiv.2206.09938

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Circuit knitting would require locating processing bottlenecks through profiling and accordingly distributing the tasks on multiple quantum processing units (QPUs), ensuring the tasks' parallelism with load balancing, which would result in the speeding up of the whole computation. In fact, this is the path IBM takes in realizing their near-term hardware development by combining multiple QPUs [48][49][50] through circuit knitting techniques.…”

Section: Simulated Results and Discussionmentioning

confidence: 99%

Enhancing scalability and accuracy of quantum poisson solver

Saha,

Robson,

Howington

et al. 2024

Quantum Inf Process

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The Poisson equation has many applications across the broad areas of science and engineering. Most quantum algorithms for the Poisson solver presented so far either suffer from lack of accuracy and/or are limited to very small sizes of the problem and thus have no practical usage. In this regard, our previous work (Robson in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), 2022) showed a proof-of-concept demonstration in advancing quantum Poisson solver algorithm and validated preliminary results for a simple case of $$3\times 3$$ 3 × 3 problem. In this work, we delve into comprehensive research details, presenting the results on up to $$15\times 15$$ 15 × 15 problems that include step-by-step improvements in Poisson equation solutions, scaling performance, and experimental exploration. In particular, we demonstrate the implementation of eigenvalue amplification by a factor of up to $$2^8$$ 2 8 , achieving a significant improvement in the accuracy of our quantum Poisson solver and comparing that to the exact solution. Additionally, we present success probability results, highlighting the reliability of our quantum Poisson solver. Moreover, we explore the scaling performance of our algorithm against the circuit depth and width, demonstrating how our approach scales with larger problem sizes and thus further solidifies the practicality of easy adaptation of this algorithm in real-world applications. We also discuss a multilevel strategy for how this algorithm might be further improved to explore much larger problems with greater performance. Finally, through our experiments on the IBM quantum hardware, we conclude that though overall results on the existing NISQ hardware are dominated by the error in the CNOT gates, this work opens a path to realizing a multidimensional Poisson solver on near-term quantum hardware.

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Section: Simulated Results and Discussionmentioning

confidence: 99%

Enhancing scalability and accuracy of quantum poisson solver

Saha,

Robson,

Howington

et al. 2024

Quantum Inf Process

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show abstract

“…We have already implemented this and discuss more about it later in the experimental section; (2) Combining our algorithm structure to an iterative method [20] that would reduce the qubits usage while improving the computational speedup by optimizing the repeated measurements; (3) Adapting a circuit knitting technique [48], which allows to break larger circuits into smaller pieces to run on a quantum computer, and then knit the results back together using a classical computer. In fact, this is the path IBM takes in realizing their near term hardware development by combining multiple quantum processing units (QPUs) through circuit knitting techniques [49,50].…”

Section: Simulated Results and Discussionmentioning

confidence: 99%

Advancing Algorithm to Scale and Accurately Solve Quantum Poisson Equation on Near-term Quantum Hardware

Saha¹,

Robson²,

Howington³

et al. 2022

Preprint

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“…Suppose one aims to run a circuit that requires N qubits but only has access to M -qubit devices with M < N . Assuming these devices can exchange quantum information using a quantum interconnect, it is possible to recompile the circuit [15] such that it uses the interconnect n i times. To quantify the benefits of a quantum * ilia@entanglednetworks.com 2 .…”

mentioning

confidence: 99%

Variational Quantum Eigensolvers in the Era of Distributed Quantum Computers

Khait¹,

Tham²,

Segal³

et al. 2023

Preprint

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The computational power of a quantum computer is limited by the number of qubits available for information processing. Increasing this number within a single device is difficult; it is widely accepted that distributed modular architectures are the solution to large scale quantum computing. The major challenge in implementing such architectures is the need to exchange quantum information between modules. In this work, we show that a distributed quantum computing architecture with limited capacity to exchange information between modules can accurately solve quantum computational problems. Using the example of a variational quantum eignesolver with an ansatz designed for a two-module (dual-core) architecture, we show that three inter-module operations provide a significant advantage over no inter-module (or serially executed) operations. These results provide a strong indication that near-term modular quantum processors can be an effective alternative to their monolithic counterparts.

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Quantum circuit optimization for multiple QPUs using local structure

Cited by 3 publications

References 22 publications

Enhancing scalability and accuracy of quantum poisson solver

Enhancing scalability and accuracy of quantum poisson solver

Advancing Algorithm to Scale and Accurately Solve Quantum Poisson Equation on Near-term Quantum Hardware

Variational Quantum Eigensolvers in the Era of Distributed Quantum Computers

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