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International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

FMCC: Flexible Measurement-based Quantum Computation over Cluster State

Yingheng Li, Aditya Pawar, Zewei Mo, Youtao Zhang, Jun Yang, Xulong Tang

Published Apr 27, 2024• 5 citations
Publisher

Abstract

Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that performs computation through "one-way" measurements on entangled quantum qubits. It is widely used in photonic quantum computing (PQC), where the computation is carried out on photonic cluster states (i.e., a 2-D mesh of entangled photons). In MBQC-based PQC, the cluster state depth (i.e., the length of one-way measurements) plays an important role in the overall execution time and circuit error. In this paper, we propose FMCC, a compilation framework that employs dynamic programming with heuristics to efficiently minimize the cluster state depth. Experimental results on six quantum applications show that FMCC achieves 51.7%, 57.4%, and 56.8% average depth reductions in small, medium, and large qubit counts compared to the state-of-the-art MBQC compilations.

DOI
10.1145/3622781.3674185

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Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that performs computation through "one-way" measurements on entangled quantum qubits. It is widely used in photonic quantum computing (PQC), where the computation is carried out on photonic cluster states (i.e., a 2-D mesh of entangled photons). In MBQC-based PQC, the cluster state depth (i.e., the length of one-way measurements) plays an important role in the overall execution time and circuit error. In this paper, we propose FMCC, a compilation framework that employs dynamic programming with heuristics to efficiently minimize the cluster state depth. Experimental results on six quantum applications show that FMCC achieves 51.7%, 57.4%, and 56.8% average depth reductions in small, medium, and large qubit counts compared to the state-of-the-art MBQC compilations.
Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that performs computation through ``one-way'' measurements on entangled quantum qubits. It is widely used in photonic quantum computing (PQC), where the computation is carried out on photonic cluster states (i.e., a 2-D mesh of entangled photons). In MBQC-based PQC, the cluster state depth (i.e., the length of one-way measurements) to execute a quantum circuit plays an important role in the overall execution time and error. Thus, it is important to reduce the cluster state depth. In this paper, we propose FMCC, a compilation framework that employs dynamic programming to efficiently minimize the cluster state depth. Experimental results on five representative quantum algorithms show that FMCC achieves 53.6%, 60.6%, and 60.0% average depth reductions in small, medium, and large qubit counts compared to the state-of-the-art MBQC compilation frameworks.
Quantum computing has rapidly evolved in recent years and has established its supremacy in many application domains. While matter-based qubit platforms such as superconducting qubits have received the most attention so far, there is a rising interest in photonic qubits lately, which show advantages in parallelism, speed, and scalability. Photonic qubits are best served by the paradigm of measurement-based quantum computation (MBQC). To deliver the promise of measurement-based photonic quantum computing (MBPQC), the photon cluster state depth and photon utilization are two of the most important metrics. However, little attention has been paid to optimizing the depth and utilization when mapping quantum circuits to the photon clusters. In this paper, we propose a compiler framework that achieves automatic and dynamic depth and utilization optimizations. Our approach consists of an MBPQC mapping mechanism that maps optimized measurement patterns on a cluster state and a cluster state pruning strategy that removes all possible redundancies without impacting the circuit functions. Experimental results on five quantum benchmark with three different qubit numbers indicate our approach achieves an average of 63.4% cluster depth reduction and 22.8% photon utilization improvements.
Quantum computers, leveraging the principles of quantum mechanics, hold the potential to surpass classical computers in numerous applications, with implications across various domains. Besides the well-known gate model, Measurement-based Quantum Computation (MBQC) is another promising computational approach to achieve universal quantum computation. In MBQC, large ensembles of qubits are prepared in a highly entangled cluster state, forming the basis for executing quantum computations through sequential measurements. Cluster states are realized using both continuous variables (CV) and discrete variables (DV) techniques. In the CV-based methods, Frequency Domain Multiplexing (FDM), Time Domain Multiplexing (TDM), Spatial Domain Multiplexing (SDM), and hybrid schemes are employed. This paper thoroughly discusses and compares these approaches, elucidating their strengths and limitations. Additionally, the generation of photonic cluster states in DV is explored and some recent results are reported. Some recent state-of-the-art advancements in photonic and superconducting qubits entanglement, which can potentially serve as cluster states, are also presented. Finally, we highlight the approach that exhibits the most promising characteristics for achieving efficient cluster state realization in the context of MBQC.
Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that carries out computation through one-way measurements on entangled photon qubits. Practical photonic hardware first generates a 2D mesh of resource states with each being a small number of entangled photon qubits and then exploits fusion operations to connect resource states to scale up the computation. Given that the fusion operation is highly error-prone, it is important to reduce the number of fusions for an MBQC circuit.
Photonic quantum computing provides a promising route toward quantum computation by naturally supporting the measurement-based quantum computation (MBQC) model. In MBQC, programs are executed through measurements on a pre-generated graph state, whose construction largely depends on probabilistic fusion operations. However, fusion operations in PQC are vulnerable to two major error sources: fusion failure and fusion erasure. As a result, MBQC compilation must account for both error mechanisms to generate reliable and efficient photonic executions. Prior state-of-the-art MBQC compilation, represented by OneAdapt, is designed for all-photonic architectures and mainly focuses on handling fusion failures. Nevertheless, it does not explicitly model fusion erasures induced by photon loss, which can be substantially more damaging than fusion failures. To mitigate fusion erasure errors, we introduce a new MBQC compilation scheme built upon the spin qubit quantum memory. We propose tree-encoded fusion, an encoding strategy that suppresses erasure errors during graph-state generation. We further incorporate this scheme into a compiler framework with algorithms that reduce the execution overhead of quantum programs. We evaluate the proposed framework using a realistic PQC simulator on six representative quantum algorithm benchmarks across multiple program scales. The results show that tree-encoded fusion achieves better robustness than alternative fusion-encoding strategies, and that our compiler provides exponential improvement over OneAdapt. In addition, we validate the feasibility of our approach through a proof-of-concept demonstration on real PQC hardware.
Measurement-based quantum computing (MBQC), a.k.a. one-way quantum computing (1WQC), is a universal quantum computing model, which is particularly well-suited for photonic platforms. In this model, computation is driven by measurements on an entangled state, which serves as an intermediate representation (IR) between program and hardware. However, compilers on previous IRs lacks the adaptability to the resource constraint in photonic quantum computers. In this work, we propose a novel IR with new optimization passes. Based on this, it realizes a resource-adaptive compiler that minimizes the required hardware size and execution time while restricting the requirement for fusion devices within an adaptive limit. Moreover, our optimization can be integrated with Quantum Error Correction (QEC) to improve the efficiency of photonic fault-tolerant quantum computing (FTQC).