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×.

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.

Nowadays, Graphics Processing Units (GPUs) dominate in a wide spectrum of computing realms and multi-task is increasingly applied in various complicated applications. To gain higher performance, multi-task programs require cumbersome programming efforts to take advantage of inter-kernel concurrency at source-code level. Although there exist works automatically scheduling kernels to enable inter-kernel concurrency, they all inevitably introduce new programming frameworks and some even bring significant performance downgrade compared to the expertise-based optimizations. To address this issue, we propose KeSCo, a compiler-based scheduler to expose kernel level concurrency in multi-task programs with trivial code modification. In compilation, KeSCo applies a strategy to schedule kernels in task queues, accounting for both load balance and synchronization cost. Also, KeSCo utilizes a customized algorithm designed for computational flow to remove redundant synchronizations. The design is further extended to support multi-process scenario, where multiple GPU processes are sharing a single context. Evaluations on representative benchmarks show that the proposed approach gains a 1.28× average speedup for multi-task scenario (1.22× for multi-process). Even with lessened programming efforts, our proposed design outperforms two state-of-the-arts GrSched and Taskflow by 1.31× and 1.16× on average, respectively.

Arithmetic operators are now used in a wide spectrum of domains, including artificial intelligence, data analytics and scientific computing. Meanwhile, specialized hardware components to enable low-precision computing are increasingly deployed in GPUs and accelerators. Whereas promising to boost performance, accelerating the operators on the hardware necessitates manually tuning the mixed-precision knobs to balance the performance and accuracy, which can be extremely challenging in real practices.

Code size is an increasing concern on resource constrained systems, ranging from embedded devices to cloud servers. To address the issue, lowering memory occupancy has become a priority in developing and deploying applications, and accordingly compiler-based optimizations have been proposed to reduce program footprint. However, prior arts are generally dealing with source codes or intermediate representations, and thus are very limited in scope in real scenarios where only binary files are commonly provided. To fill the gap, this paper presents a novel code-size optimization RollBin to reroll loops at binary level. RollBin first locates the unrolled loops in binary files, and then probes to decide the unrolling factor by identifying regular memory address patterns. To reconstruct the iterations, we propose a customized data dependency analysis that tackles the challenges brought by shuffled instructions and loop-carry dependencies. Next, the recognized iterations are rolled up through instruction removal and update, which are generally reverting the normal unrolling procedure. The evaluations on standard SPEC2006/2017 and MiBench demonstrate that RollBin effectively shrinks code size by 1.7% and 2.2% on average (up to 7.8%), which respectively outperforms the state-of-the-arts by 31% and 38%. In addition, the use cases of representative realistic applications manifest that RollBin can be applicable in practices.