Quantum feedback makes the execution of dynamic quantum circuits possible and is widely used in quantum algorithms.However, due to the inherent computation and transmission cost, the latency of the quantum feedback becomes a considerable burden on the current quantum algorithm.The dynamic property of the feedback also makes the gates blocked until the feedback is finished.In this paper, we propose ARTERY, which uses branch prediction to support instruction pre-execution and speed up the feedback.ARTERY integrates historical statistics of branches and a real-time readout pulse analysis to predict the branch.With this idea, we build up a reconciled branch predictor that concatenates the historical statistics of branches and a real-time branch circuit speculation obtained from the readout-pulse trajectory predictor.We further explore the implementation of peripheral hardware for feedback, including a scalable inter-FPGA connection via the backplane, a feedback trigger mechanism for dynamic instruction timing, and an adaptive pulse sampling technique to maximize the hardware bandwidth.ARTERY accelerates quantum feedback process by 2.07× compared to the state-of-the-art method, with over 90% prediction accuracy, achieving 1.24× fidelity improvement.

We present AdaptDQC, an adaptive compiler framework for optimizing distributed quantum computing (DQC) under diverse performance metrics and inter-chip communication (ICC) architectures. AdaptDQC leverages a novel spatial-temporal graph model to describe quantum circuits, model ICC architectures, and quantify critical performance metrics in DQC systems, yielding a systematic and adaptive approach to constructing circuit-partitioning and chip-mapping strategies that admit hybrid ICC architectures and are optimized against various objectives. Experimental results on a collection of benchmarks show that AdaptDQC outperforms state-of-the-art compiler frameworks: It reduces, on average, the communication cost by up to 35.4% and the latency by up to 38.4%.

Quantum pulse serves as the machine language of superconducting quantum devices, which needs to be synthesized and calibrated for precise control of quantum operations. However, existing pulse control systems suffer from the dilemma between long synthesis latency and inaccuracy of quantum control systems. compute-in-CPU synthesis frameworks, like IBM Qiskit Pulse, involve massive redundant computation during pulse calculation, suffering from a high computational cost when handling large-scale circuits. On the other hand, field-programmable gate array (FPGA)-based synthesis frameworks, like QuMA, faces inaccurate pulse control problem. In this article, we propose both compute-in-CPU and all-in-FPGA solutions to collaboratively solve the latency and inaccuracy problem. First, we propose QPulseLib, a novel compute-in-CPU library with reusable pulses that can directly provide the pulse of a circuit pattern. To establish this library, we transform the circuit and apply convolutional operators to extract reusable patterns and precalculate their resultant pulses. Then, we develop a matching algorithm to identify such patterns shared by the target circuit. Experiments show that QPulseLib achieves$158.46\times $and$16.03\times $speedup for pulse calculation, compared to Qiskit Pulse and AccQOC. Moreover, we extend the design as a fast and precise all-in-FPGA pulse control approach using near-quantum cache design, SmartQCache. To be specific, we employ a two-level cache to hold reusable pulses of frequently-used circuit patterns. Such a design enables pulse prefetching in near-quantum peripherals, dramatically reducing the end-to-end synthesis latency. To achieve precise pulse control, SmartQCache incorporates duration optimization and pulse sequence calibration to mitigate the execution errors from imperfect hardware, crosstalk, and time shift. Experimental results demonstrate that SmartQCache achieves$294.37\times $and$145.43\times $speedup in pulse synthesis compared to Qiskit Pulse and AccQOC. It also reduces the pulse inaccuracy by$1.27\times $compared to QuMA.

Quantum circuit serves as a popular programming model that describes the computation using a set of quantum gates, which requires generating a sequence of pulses that collect the operation of each gate for superconducting quantum devices. However, existing quantum synthesis frameworks, like IBM OpenPulse [1], involve massive redundant computation during pulse generation, suffering from a high computational cost when handling large-scale circuits. In this paper, we propose QPulseLib, a novel library with reusable pulses that can directly provide the pulse of a circuit block. To establish this library, we transform the circuit and apply convolutional operators to extract reusable patterns and pre-calculate their resultant pulses. Then, we develop a matching algorithm to identify such patterns shared by the target circuit. Experiments show that QPulseLib achieves 158.46 × and 16.03 × speedup for pulse generation, compared to OpenPulse and AccQOC [2].

In the current Noisy Intermediate-Scale Quantum era, quantum circuit analysis is an essential technique for designing high-performance quantum programs. Current analysis methods exhibit either accuracy limitations or high computational complexity for obtaining precise results. To reduce this tradeoff, we propose QuCT, a unified framework for extracting, analyzing, and optimizing quantum circuits. The main innovation of QuCT is to vectorize each gate with each element, quantitatively describing the degree of the interaction with neighboring gates. Extending from the vectorization model, we propose two representative downstream models for fidelity prediction and unitary decomposition. The fidelity prediction model performs a linear transformation on all gate vectors and aggregates the results to estimate the overall circuit fidelity. By identifying critical weights in the transformation matrix, we propose two optimizations to improve the circuit fidelity. In the unitary decomposition model, we significantly reduce the search space by bridging the gap between unitary and circuit via gate vectors. Experiments show that QuCT improves the accuracy of fidelity prediction by 4.2 × on 5-qubit and 18-qubit quantum devices and achieves 2.5 × fidelity improvement compared to existing quantum compilers [19, 55]. In unitary decomposition, QuCT achieves 46.3 × speedup for 5-qubit unitary and more than hundreds of speedup for 8-qubit unitary, compared to the state-of-the-art method [87].