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 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.
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.
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.
The Measurement Based Quantum Computation (MBQC) model achieves universal quantum computation by employing projective single qubit measurements with classical feedforward on a highly entangled multipartite cluster state. Rapid advances in improving scalability of quantum computing systems have enabled the generation of large cluster states for implementing MBQC on various platforms. This review focuses on three such efforts, each utilizing a different quantum computing technology viz., superconducting qubits, trapped ion qubits and squeezed photon states. MBQC is being increasingly employed on optical platforms which can generate large entangled resource states but lack the ability to perform deterministic entangling gates.
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.
Photonic cluster states are fundamental resource for measurement-based quantum computing (MBQC), which is a promising approach for scalable quantum computing. These highly entangled states enable computation with applying local measurements rather than gate operations, which makes them essential for fault-tolerant quantum computing. Generation of the photonic cluster states requires high-quality entanglement and stability, which can be done by making use of cavity quantum electrodynamics (cavity QED), which is embedding quantum dots (QDs) into photonic microcavities. This method enables controlled single-photon emission and the entanglement of them, helping with key challenges in generating photonic cluster states. Enhancing the generation of the photonic cluster state requires improvement in the quality factor (Q-factor) and therefore, cooperativity of cavity and QD systems. A high Q-factor helps with reduction in photon loss and decoherence, ensuring more stable and high-fidelity quantum states. It also enables better light-matter interactions, improving cluster state quality. Cooperativity, which is a measure for the light-matter coupling strength relative to dissipation, directly impacts photon emission efficiency. Optimization of these parameters improves entanglement generation, which is essential for producing high-quality photonic cluster states. Therefore, optimized cavities are essential for improving the generation of the photonic cluster states. Scalability is also an important parameter for the generation of the large-scale cluster states used in quantum computing. In this paper, after reviewing MBQC, cluster states, and the generation of the photonic cluster states, we investigate the deterministic generation of the photonic cluster state resource for the purpose of MBQC, and highlights the importance of the inverse design techniques for this goal.
Despite progress in quantum computing technology, the optimization of quantum circuit compilation for real-world quantum processing units (QPUs) [1] continues to pose a significant challenge. Current compilers use static gate-based models, which cannot adjust to changes in hardware limitations and noise fluctuations. In this study, we introduce Qukkos, a novel measurement-based quantum compilation system that optimizes resource utilization, improves circuit dependability, and achieves higher execution efficiency. To increase its adaptability for real-world quantum devices, Qukkos uses measurement-based quantum computing (MBQC) [2], [3], [4], [5], [6] to conduct dynamic circuit change. This system combines a quantum compiler with a quantum resource virtualization manager and standardized API interfaces to achieve seamless cross-platform execution alongside efficient task scheduling. Our experimental results across various QPU architectures show that Qukkos improves circuit depth reduction while increasing fidelity and speeding up compilation times relative to conventional methods. The modular design of Qukkos enables interoperability between superconducting qubits and trapped-ion systems as well as photonic quantum platforms and establishes it as a scalable solution for future quantum computing applications.