Distributed quantum computing (DQC) is a promising approach to extending the computational power of near-term quantum hardware. However, the non-local quantum communication between quantum nodes is much more expensive and error-prone than the local quantum operation within each quantum device. Previous DQC compilers focus on optimizing the implementation of each non-local gate and adopt similar compilation designs to single-node quantum compilers. The communication patterns in distributed quantum programs remain unexplored, leading to a far-from-optimal communication cost. In this paper, we identify burst communication, a specific qubit-node communication pattern that widely exists in various distributed quantum programs and can be leveraged to guide communication overhead optimization. We then propose AutoComm, an automatic compiler framework to extract burst communication patterns from input programs and then optimize the communication steps of burst communication discovered. Compared to state-of-the-art DQC compilers, experimental results show that our proposed AutoComm can reduce the communication resource consumption and the program latency by 72.9% and 69.2% on average, respectively.

1. Bibliographic Information

• Title: AutoComm: A Framework for Enabling Efficient Communication in Distributed Quantum Programs
• Authors: Anbang Wu, Hezi Zhang, Gushu Li, Alireza Shabani, Yuan Xie, and Yufei Ding.
• Affiliations: University of California, Santa Barbara (CS and ECE Departments) and Cisco Research.
• Journal/Conference: The provided document is a research paper, but the specific conference or journal (e.g., ASPLOS, MICRO, ISCA) is not mentioned in the header. The quality of the work and the affiliations of the authors suggest it would be suitable for a top-tier venue in computer architecture or programming languages.
• Publication Year: Not explicitly stated in the provided text, but the citations and content suggest it is a contemporary work from the early 2020s.
• Abstract: The paper addresses a key bottleneck in Distributed Quantum Computing (DQC): expensive and error-prone communication between quantum nodes. While prior DQC compilers optimize individual non-local gates, they overlook broader communication patterns. The authors identify a prevalent pattern called burst communication—a group of continuous remote gates between a single qubit and a remote node. They propose AutoComm, a compiler framework that automatically extracts these burst patterns and optimizes their execution. The framework uses a hybrid communication scheme and an adaptive scheduling strategy. Experimental results show that AutoComm significantly outperforms state-of-the-art DQC compilers, reducing communication resource consumption by 72.9% and program latency by 69.2% on average.
• Original Source Link: /files/papers/68f88cb281e5ec727a02b0a8/paper.pdf. This appears to be a local file path; the paper is available publicly through academic search engines. It is a formally published research paper.

2. Executive Summary

• Background & Motivation (Why):

  • Core Problem: Scaling up quantum computers to solve practical problems requires a large number of high-quality qubits. Building a single, massive quantum processor is extremely difficult due to hardware challenges like crosstalk and fabrication limits. Distributed Quantum Computing (DQC), which links multiple smaller quantum processors (nodes), is a promising alternative.
  • Key Challenge: The primary bottleneck in DQC is inter-node communication. Transmitting quantum information between nodes is far slower (5-100x) and more error-prone (up to 40x fidelity degradation) than local operations within a node. This communication relies on consuming a precious resource: pre-distributed entangled qubit pairs, known as EPR pairs.
  • Gap in Prior Work: Previous DQC compilers treated remote operations like a single-node compiler, optimizing the implementation of each non-local gate in isolation. This "sparse communication" approach has low throughput and fails to exploit higher-level patterns, leading to suboptimal communication costs.
  • Fresh Angle: This paper introduces the concept of burst communication, which is a more "information-intensive" unit of communication than a single gate. Instead of optimizing one gate at a time, the authors propose optimizing an entire group of gates exchanged between a qubit and a node.

• Main Contributions / Findings (What):

  • Identification of Burst Communication: The paper is the first to identify and formalize burst communication as a common and optimizable pattern in DQC programs. This pattern consists of a sequence of remote two-qubit gates between a single qubit on one node and multiple qubits on another node.
  • AutoComm Framework: The authors develop AutoComm, the first compiler framework designed to systematically exploit burst communication. It consists of three novel stages:
    1. Communication Aggregation: A pass that uses gate commutation rules to automatically find and group scattered remote gates into larger burst communication blocks.
    2. Hybrid Communication Assignment: A strategy that analyzes the pattern of each burst block (e.g., unidirectional vs. bidirectional) and assigns the most resource-efficient communication protocol (Cat-Comm or TP-Comm).
    3. Adaptive Communication Scheduling: An optimization pass that reduces program latency by maximizing parallelism between communication blocks (through alignment) and fusing sequential blocks to reduce overhead.
  • Significant Performance Improvement: The paper demonstrates that AutoComm drastically reduces the two most critical costs in DQC: the number of EPR pairs consumed (resource cost) and the total program execution time (latency). The reported average reductions are 72.9% for resources and 69.2% for latency compared to state-of-the-art baselines.

3. Prerequisite Knowledge & Related Work

This section explains the foundational concepts needed to understand the paper, aimed at a beginner.

• Foundational Concepts:

  • Qubit and Quantum Gates: A qubit is the basic unit of quantum information, which can exist in a superposition of 0 and 1. Quantum gates are operations that manipulate qubits, analogous to logic gates in classical computers. A common two-qubit gate is the CX (Controlled-NOT) gate.
  • Distributed Quantum Computing (DQC): A paradigm where multiple, smaller quantum computers (nodes) are networked together to function as a single, more powerful machine. This is a practical approach to scaling up beyond the limits of a single chip.
  • Quantum No-Cloning Theorem: A fundamental principle stating that it is impossible to create an identical, independent copy of an arbitrary unknown quantum state. This means you can't simply "copy-paste" a qubit's state to another node like classical data.
  • EPR Pair: Named after Einstein, Podolsky, and Rosen, an EPR pair is a set of two qubits that are quantumly entangled. Even when physically separated (e.g., on different nodes), a measurement on one instantaneously affects the other. These "remote EPR pairs" are the fundamental resource that enables inter-node quantum communication.
  • Communication Protocols: The paper focuses on two primary methods for using EPR pairs:

    • Cat-Comm (Cat-entangler/disentangler Communication): As shown in Figure 2(a), this scheme uses one EPR pair to effectively execute a controlled operation remotely. It's very efficient for a single remote CX gate but less flexible for more general operations.

    • TP-Comm (Teleportation Communication): Based on the quantum teleportation protocol, this scheme (Figure 2(b)) uses EPR pairs to "teleport" the state of a qubit from one node to another. A simple remote CX requires two teleportations (one to send the state, one to return it to free up resources), consuming two EPR pairs. Its advantage is generality—it can facilitate any remote operation.

      Figure 2: Implementations of a single remote CX gate. (a) The Cat-Comm version uses one EPR pair. (b) The TP-Comm version, including the return trip, uses two EPR pairs.

• Previous Works:

  • Compilers like those from Ferrari et al. and Baker et al. focused on a "gate-by-gate" or "sparse" communication strategy. They either implemented each remote CX using Cat-Comm or used remote SWAP gates (swapping two qubits between nodes) implemented with TP-Comm. While an improvement over naive approaches, their information throughput per EPR pair was fundamentally limited by the information content of a single two-qubit gate.
  • Diadamo et al. explored optimizing larger blocks, but their method was restricted to specific controlled-unitary gate structures and could not be applied to general-purpose quantum programs that are decomposed into basic gates.

• Differentiation: AutoComm's innovation is to shift the optimization focus from the "height" (number of qubits in a gate) to the "width" (number of consecutive gates in a pattern). By identifying and optimizing burst communication as a single unit, it can pack much more information into each communication event, dramatically improving efficiency. It is also more general than previous block-based optimizers.

4. Methodology (Core Technology & Implementation)

AutoComm is a back-end compiler framework that optimizes communication after an initial qubit mapping has been decided. It transforms a program with inefficient, scattered remote gates into one with highly optimized, consolidated communication blocks.

Figure 1: The AutoComm framework. It takes a mapped quantum program, which has sparse communication, and applies three stages (Aggregation, Assignment, Scheduling) to produce a program with efficient burst communication.

A. Stage 1: Communication Aggregation

The goal is to find and group remote CX gates into burst communication blocks. This is non-trivial because commutable gates can be interleaved, hiding the pattern.

• Principle: The framework leverages gate commutation rules to reorder the program's gates and bring related remote operations together. Figure 7 shows examples of these rules, which allow CX gates to be moved past other CX gates or single-qubit gates under certain conditions.

  Figure 7: A sample of the gate commutation rules used by AutoComm to reorder gates and expose burst communication patterns.

• Steps & Procedures: The aggregation process, illustrated in Figure 8, follows a three-step heuristic:

  1. Identify Potential Bursts: The algorithm first scans the program to find the qubit-node pair involved in the most remote gates (e.g., qubit $q3$ and Node A in Figure 8). It then identifies all consecutive remote gates for this pair, initially resulting in several small, isolated blocks.

  2. Linear Merge: Using Algorithm 1, the framework attempts to merge these isolated blocks. It greedily tries to move a block past the intervening gates to join with the next block. If the intermediate gates are commutable, the merge succeeds. This process is applied linearly through the identified blocks.

  3. Iterative Refinement: Steps 1 and 2 are repeated for other qubit-node pairs until no more merge opportunities can be found. The result is a program where scattered remote gates have been grouped into a few large burst communication blocks.

     Figure 8: The aggregation process for an example program. (a) Initially, small blocks are identified. (b) The linear merge combines some of them. (c) The final output after iterative refinement has larger, consolidated burst blocks.

B. Stage 2: Communication Assignment

Once burst blocks are formed, the next step is to choose the most efficient protocol (Cat-Comm or TP-Comm) to execute each one.

• Principle: The choice depends on the block's internal gate pattern. Cat-Comm is like a "read-only" share, while TP-Comm is like migrating the data itself for "read-write" access.

• Pattern Analysis:

  • Unidirectional Pattern (Figure 9a, 9c): One qubit ($q1$) consistently acts as either the control or the target for all gates in the block. If any interfering single-qubit gates on $q1$ can be moved out, this pattern is a controlled-unitary block. It can be implemented with a single call to Cat-Comm, using only one EPR pair. This offers the highest potential savings.

  • Bidirectional Pattern (Figure 9b): The shared qubit ($q1$) acts as both a control and a target within the block. This pattern cannot be implemented with a single Cat-Comm call. TP-Comm is more efficient here: one teleportation to send $q1$ to the remote node, and one to send it back, costing two EPR pairs total. This is still a huge saving compared to implementing each of the $n$ gates individually.

    Figure 9: Primitive communication patterns. (a) and (c) are unidirectional, ideal for Cat-Comm. (b) is bidirectional, better suited for TP-Comm.

• Assignment: The framework analyzes each block. If it fits the unidirectional pattern and is "clean" (no unmovable gates in the way), it's assigned to Cat-Comm. Otherwise (bidirectional or "dirty" unidirectional), it's assigned to TP-Comm. Figure 11(a) shows the result of this assignment for the running example.

C. Stage 3: Communication Scheduling

The final stage aims to minimize the program's total execution time (latency). The primary source of latency is the preparation of EPR pairs, which is very slow.

• Latency Model: The paper uses a latency model where EPR preparation is the most expensive operation, as shown in the transcribed Table I.

  Table I (Manual Transcription): THE QUANTITATIVE DATA OF OPERATIONS IN DQC, EXTRACTED FROM [25], [26]. LATENCIES ARE NORMALIZED TO CX COUNTS.

  Operation	Variable Name	Latency
  Single-qubit gates	t1q	~0.1 CX
  CX and CZ gates	t2q	1 CX
  Measure	tms	5 CX
  EPR preparation	tep	~12 CX
  One-bit classical comm	t	~1 CX

• Techniques for Parallelism and Fusion:

  • More Block-Level Parallelism: Even if blocks share nodes or qubits, they can sometimes run in parallel if they are commutable. AutoComm employs a greedy scheduler to execute as many blocks as possible concurrently.

  • TP-Comm Alignment (Figure 13): For multiple parallel TP-Comm blocks, instead of executing each one's full round-trip sequentially, the scheduler aligns their initial teleportations to happen at the same time. This hides the latency of the local operations (t_block) and the second teleportation (t_tele) for all but the last block, leading to significant time savings.

    Figure 13: TP-Comm scheduling. (b) Aligned teleportation is much faster than (a) independent execution.

  • TP-Comm Fusion (Figure 14): For a sequence of TP-Comm blocks where the same qubit is passed between multiple nodes (e.g., A → B, then B → C), the scheduler fuses the teleportations into a cycle (A → B → C → A). This eliminates redundant return trips, saving both EPR pairs and preparation time (t_ep). This cleverly solves the "token passing" problem.

    Figure 14: Fusing sequential TP-Comm blocks. (b) The cyclic teleportation path is more efficient than (a) the series of independent round-trips.

The final output is a highly optimized schedule, as shown for the example in Figure 11(b), which achieves a 58.3% latency reduction.

Figure 11: (a) Communication assignment for the example. (b) The final, optimized schedule after applying AutoComm's scheduling techniques.

5. Experimental Setup

• Datasets/Benchmarks: The evaluation uses a diverse set of quantum programs from IBM Qiskit and RevLib, divided into two categories as shown in the transcribed Table II.

  • Building Blocks: Elementary but crucial quantum functions like multi-controlled gates (MCTR), arithmetic (adder, mult), and Quantum Fourier Transform (QFT).

  • Real-World Applications: Algorithms with near-term potential, including Bernstein-Vazirani (BV), Quantum Approximate Optimization Algorithm (QAOA), and Unitary Coupled Cluster (UCCSD) for chemistry simulations.

    Table II (Manual Transcription): Details of the benchmark programs.

    Type	Name	# qubit	# node	# gate	# CX	# REM CX by SOEE
    Building-Blocks
    	Multi-ControlledGate (MCTR)	100	10	10640	4560	1680
    		200	20	21840	9360	3568
    		300	30	33040	14160	5632

  (Note: The provided text for Table II is incomplete and cuts off. The transcribed data reflects what was available.)

• Evaluation Metrics:

  1. Total number of consumed EPR pairs: Measures the total communication resource overhead. A lower value is better.
  2. Peak # REM CX: The maximum number of remote CX gates executed with a single effective EPR pair. It is calculated as max(#REM_CX_in_CatComm_block, #REM_CX_in_TPComm_block / N), where $N$ is typically 2 for a standard TP-Comm block but can become 1 if fusion is applied. This metric measures communication throughput; a higher value is better.
  3. improv. factor: Measures the resource savings relative to a baseline. $$\text{improv. factor} = \frac{\text{\# total EPR pairs by baseline}}{\text{\# total EPR pairs by AutoComm}}$$ A higher value indicates greater improvement.
  4. LAT-DEC factor: Measures the latency reduction relative to a baseline. $$\text{LAT-DEC factor} = \frac{\text{program latency by baseline}}{\text{program latency by AutoComm}}$$ A higher value indicates a greater speedup.

• Baselines:

  • GP-Cat: A state-of-the-art compiler that implements each remote CX gate using the Cat-Comm scheme. It represents a "sparse communication" approach.
  • GP-TP: A state-of-the-art compiler that implements non-local operations by swapping qubits between nodes using TP-Comm.
  • Both baselines use the same advanced qubit mapping strategy (SOEE) and a greedy scheduler, ensuring a fair comparison focused on communication optimization.

6. Results & Analysis

(Note: The provided text cuts off before presenting Table III and the full results section. The analysis below is based on the abstract, the introductory claims, and the available figures.)

• Core Results: The abstract claims that, compared to state-of-the-art baselines, AutoComm reduces EPR pair consumption by 72.9% and program latency by 69.2% on average. These are dramatic improvements, confirming that optimizing burst communication is far superior to gate-by-gate optimization.

• Burst Communication Statistics (Figure 15): This figure provides strong evidence for the prevalence of burst communication.

  Figure 15: The cumulative probability that a single EPR pair can support at least X remote CX gates. Higher and right-shifted curves are better.

  This plot shows the effectiveness of AutoComm's aggregation. For example, in plot (a) for the $adder_8$ benchmark, there is a nearly 100% probability ($Pr[X]=1$) that an EPR pair can handle at least 2 remote CX gates, and a significant probability it can handle 4 or more. In contrast, for baselines like GP-Cat, this probability would drop to zero for $X > 1$. This visually confirms that AutoComm successfully finds and leverages large burst blocks, increasing communication throughput.

7. Conclusion & Reflections

• Conclusion Summary: The paper makes a compelling case for a paradigm shift in DQC compilation. By identifying burst communication, the authors move beyond the limitations of single-gate optimizations. The proposed AutoComm framework provides a concrete and effective solution with three key stages: aggregating remote gates into burst blocks, intelligently assigning the best communication protocol (Cat-Comm or TP-Comm) to each block, and scheduling their execution with novel parallelism and fusion techniques. The resulting significant reductions in resource cost and latency are crucial for making DQC a more viable technology.

• Limitations & Future Work (from paper):

  • The current work focuses on burst communication between one qubit and one node. The authors suggest that extending this to node-to-node burst communication is a promising direction for future work, especially as hardware with more communication qubits becomes available.
  • The evaluation assumes a simple network topology (all-to-all connectivity) and uniform node capabilities. Applying these techniques to systems with limited connectivity or heterogeneous nodes is another area for future research.

• Personal Insights & Critique:

  • Novelty and Impact: The central idea of burst communication is highly intuitive and powerful, drawing a clear parallel to burst memory access in classical computer architecture. This abstraction is the paper's most significant contribution and is likely to influence future DQC compiler design.
  • Pragmatism: The hybrid use of Cat-Comm and TP-Comm is a pragmatic engineering choice that acknowledges there is no one-size-fits-all solution. The framework's ability to analyze patterns and choose the best tool for the job is a key strength.
  • Potential Weakness: The communication aggregation step relies on a greedy linear merge algorithm. While effective, this heuristic may not find the globally optimal grouping of gates, leaving some performance on the table. A more exhaustive, albeit computationally expensive, search could potentially yield even better results.
  • Transferability: The principles of identifying high-level patterns and optimizing them as a single unit are broadly applicable. This "pattern-centric" optimization philosophy could be transferred to other areas of quantum compilation, such as error correction or pulse-level control. Overall, AutoComm represents a major step forward in making distributed quantum computing more efficient and practical.