Measurement-based Quantum Computing (MBQC) achieves universal quantum computing by applying measurements on the photonic architectures. While it has many advantages, such as long qubit decoherence time and strong scalability, the success rate of MBQC execution is constrained by imperfect photon control, measurement, and fusion operations. Both fusion failure and photon loss necessitate the re-execution of the entire quantum circuit, leading to significant overhead in terms of additional execution time and increased consumption of resource state layers. Recent studies mainly focus on mitigating fusion failures and little attention has been paid to photon loss. In this paper, we propose STMC (Small-Tile Multiple-Copy) compilation framework to reduce the re-execution overhead caused by both the fusion failure and photon loss. Specifically, STMC first transforms a quantum circuit into a fusion graph and partitions the fusion graph into subgraphs. Then, STMC generates compact subgraph mappings that are appropriate for the size of a subportion in the resource state layer, referred to as a tile. Finally, STMC employs multiple copies of each subgraph when mapping to tiles, duplicates the execution of tiles in parallel, and finishes the whole circuit execution in order. The experimental results demonstrate that STMC achieves an average execution time speedup of 65.68× for successfully executing the circuit under a 75% fusion success rate, compared to prior work. Additionally, STMC reduces the number of resource state layers by three orders of magnitude and decreases the number of resource states by an average of 36.40×.

The photonic quantum computer (PQC) is an emerging and promising quantum computing paradigm that has gained momentum in recent years.In PQC, computations are executed by performing measurements on photons in graph states (i.e., a collection of entangled photons).The graph state generation process is fulfilled by applying a sequence of quantum gates to quantum emitters, referred to as the "generation sequence".In a generation sequence, i) the time required to complete the generation sequence, ii) the number of quantum emitters used, and iii) the number of CZ gates performed between emitters greatly affect the fidelity of the generated graph state.In this paper, we propose RLGS (Reinforcement Learningguided Graph State generation), a novel compilation framework to identify optimal generation sequences that optimize the three fidelity metrics.Experimental results show that RLGS achieves an average reduction in generation time of 31.1%,49.6%, and 57.5% for small, medium, and large graph states compared to the baseline.Additionally, the reductions in the number of quantum emitters are 13.9%, 16.7%, and 17.5%, whereas the reductions in the number of CZ gates are 37.7%, 53.4%, and 57.8%, respectively.

Low-temperature (LT) conditions can potentially lead to lower power consumption and enhanced performance in circuit operations by reducing the transistor leakage current, increasing carrier mobility, reducing wear-out, and reducing interconnect resistance. We develop PROCEED-LT, a pathfinding framework to co-optimize devices and circuits over a wide performance range. Our results demonstrate that circuit operations at LT (−196 °C) reduce power compared to room temperature (RT, 85 °C) by$15\times $to over$23.8\times $depending on performance level. Alternatively, LT improves performance by$2.4\times $(high-power, high-performance)$- 7.0\times $(low-power, low-performance) at the same power point. These gains are further improved in low-activity circuits and when using multivoltage configurations. Meanwhile, we highlight the need for improvement in$V_{\text {th}}$variation to leverage benefits at cryogenic temperatures.

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.

Quantum computing has recently emerged as a promising computing paradigm for many application domains. However, the size of quantum circuits that can be run with high fidelity is constrained by the limited quantity and quality of physical qubits. Recently proposed schemes, such as wire cutting and qubit reuse, mitigate the problem but produce sub-optimal results as they address the problem individually. In addition, gate cutting, an alternative circuit-cutting strategy that is suitable for circuits computing expectation values, has not been fully explored in the field.

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.

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.

To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many processing elements (PEs) are needed. However, designing such complex systems with multiple objectives can be challenging due to the huge associated design space and long evaluation times. To optimize such systems, we propose a new multi-objective design space exploration framework called MOELA that combines the benefits of evolutionary-based search with a learning-based local search to quickly determine PE and communication link placement to optimize multiple objectives (e.g., latency, throughput, and energy) in 3D NoC enabled heterogeneous manycore systems. Compared to state-of-the-art approaches, MOELA increases the speed of finding solutions by up to 128×, leads to a better Pareto Hypervolume (PHV) by up to 12.14× and improves energy-delay-product (EDP) by up to 7.7% in a 5-objective scenario.