HALO: A Fine-Grained Resource Sharing Quantum Operating System

HALO is a novel quantum operating system that enables fine-grained resource sharing through a hardware-aware qubit-sharing algorithm and a shot-adaptive scheduler, significantly increasing hardware utilization and throughput compared to existing fair-share scheduling methods.

The Gist

Imagine you are at a very popular, high-end restaurant. This restaurant has only one massive, expensive kitchen (the Quantum Computer), but there are thousands of hungry customers (the Users) waiting outside.

Currently, the restaurant operates with a very strict, old-fashioned rule: "One table, one entire kitchen." Even if a customer only wants a tiny cup of tea, they get the whole kitchen to themselves. No one else can use the stove or the fridge until that person is completely finished. This means the kitchen is mostly empty and wasted most of the time, while the line of hungry people outside grows longer and longer.

The researchers at UCLA, Tulane, and Berkeley have designed a new "Operating System" for this restaurant called HALO. HALO changes the rules to make the kitchen much more efficient.

Here is how HALO works, using three main "upgrades":

1. The "Shared Utensils" Strategy (Helper Qubit Sharing)

In quantum computing, most programs need "helper" tools (called ancilla qubits) to do their math. Think of these like spatulas or whisks.

• The Old Way: Every customer demands their own personal set of spatulas, even if they only use them for ten seconds. Most of the spatulas just sit on the counter, unused, while other customers are waiting.
• The HALO Way: HALO realizes that once one person is done whisking an egg, they don't need that whisk anymore. HALO cleans the whisk (a process called a Reset) and immediately hands it to the next person in line. By sharing these "helper tools," HALO can fit many more customers into the kitchen at the same time.

2. The "Smart Waiter" (Shot-Aware Scheduling)

Not every meal takes the same amount of time. Some people want a quick snack (a few "shots" or repetitions), while others want a 12-course feast.

• The Old Way: The restaurant serves people in the order they arrived (First-Come, First-Served). If a person ordering a 12-course feast is at the front, everyone else—even the people who just want a cracker—has to wait hours.
• The HALO Way: HALO acts like a super-smart waiter. It looks at the menu and says, "Okay, we have a group that needs a lot of space and a group that needs a lot of time. Let's group the 'quick snack' people together so we can clear them out of the kitchen fast, and let the 'big feast' people take up the remaining space." This keeps the kitchen constantly busy and moving.

3. The "Social Distancing" Rule (Crosstalk & Isolation)

The big risk with sharing a kitchen is a mess. If two chefs are working too close to each other, they might bump elbows, spill salt into someone else's soup, or accidentally use the same pan (this is called Crosstalk or Entanglement).

• The HALO Way: HALO is a master of organization. It maps out exactly where every chef stands. It ensures that while they are sharing the "utensils," their "ingredients" (the Data Qubits) are kept in separate, safe zones. It calculates the perfect distance to keep them close enough to be efficient, but far enough apart so they don't ruin each other's recipes.

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The Results: Why does this matter?

By implementing these changes, the researchers found that HALO is a game-changer:

• More Work Done: It can handle over 4 times as many tasks as the current systems.
• Less Waiting: Because the kitchen is used so much more efficiently, the "line" of users moves much faster.
• A Small Trade-off: Because the kitchen is more crowded, the food might not be quite as perfect as it would be in a private kitchen (a slight loss in Fidelity), but the researchers proved that the trade-off is well worth it because so many more people are getting fed!

In short: HALO turns a wasteful, one-person-at-a-time kitchen into a high-speed, professional, shared culinary powerhouse.

Technical Summary

Technical Summary: HALO: A Fine-Grained Resource Sharing Quantum Operating System

1. Problem Statement

As quantum computing transitions to the cloud era, a massive mismatch has emerged between high user demand and the scarcity of quantum hardware. Current cloud platforms (like IBM Quantum) primarily use an exclusive-use model, where a single job monopolizes the entire quantum processor, even if it only requires a small fraction of the available qubits or execution time. This leads to:

• Low Hardware Utilization: Many qubits remain idle during execution.
• High Latency: Users face long queues (minutes to days) for jobs that only take seconds to execute.
• Inefficient Resource Management: Existing virtualization frameworks (like HyperQ or QOS) either use static partitioning or time-sharing that ignores the specific nature of quantum resources, such as the temporary nature of helper (ancilla) qubits and the varying shot requirements (number of repetitions) of different algorithms.

2. Methodology

The authors propose HALO, the first quantum operating system designed for fine-grained resource sharing. HALO implements a three-stage scheduling pipeline to achieve both spatial and temporal multiplexing:

• A. Shot-Aware Batch Scheduler (Temporal Multiplexing):
  Unlike traditional schedulers that use FIFO, HALO reorders processes based on their "work volume" (a combination of circuit depth and shot count). It forms batches of processes that fit within a tunable data-qubit occupancy ratio ($\lambda$). It also optimizes the number of shots per batch to prevent long-running, high-shot jobs from monopolizing the hardware.

• B. Hardware-Aware Multi-Process Mapping (Spatial Multiplexing):
  HALO uses a simulated annealing algorithm to optimize a joint cost function for placing data qubits. This function balances three competing interests:

  1. Intra-process routing: Minimizing SWAP gates within a single circuit.
  2. Inter-process isolation: Maximizing the distance between data qubits of different processes to minimize crosstalk.
  3. Helper-qubit routing: Ensuring data qubits are placed near a "helper-qubit zone" to minimize the overhead of accessing shared ancilla qubits.

• C. Dynamic Helper-Qubit Sharing & Instruction Scheduling:
  HALO treats helper qubits as a shared, reusable pool. It employs a Round-Robin instruction scheduler that allows processes to compete for available helper qubits. To ensure isolation and prevent unintended entanglement between different users, HALO automatically inserts mandatory RESET operations whenever a helper qubit is reassigned from one process to another.

3. Key Contributions

• First Fine-Grained Quantum OS: Introduces a design that combines space-sharing (qubit-level concurrency) and time-sharing (shot-aware execution).
• Helper-Qubit Virtualization: Moves beyond static partitioning by allowing the OS kernel to dynamically allocate and deallocate ancilla qubits via system calls (alloc_helper, deallocate_helper).
• Unified Cost Function: Provides a mathematical framework to quantify the trade-off between hardware utilization, routing overhead, and crosstalk noise.
• Tunable Trade-off: Offers users a "knob" (the $\lambda$ parameter) to choose between high throughput (aggressive sharing) and high fidelity (conservative sharing).

4. Results

The system was evaluated on the IBM Torino quantum computer using helper-qubit-intensive benchmarks (QEC, Arithmetic, and Multi-CX circuits). Key findings include:

• Throughput & Utilization: Compared to the state-of-the-art HyperQ, HALO improved overall hardware utilization by up to 2.44× and increased throughput (processes per batch) by 4.44×.
• Fidelity: While sharing resources introduces noise (due to extra routing and crosstalk), HALO maintains fidelity loss within 33% compared to exclusive-use models.
• Ablation Insights:
  • Qubit Sharing: Enabling helper-qubit reuse alone increased throughput by 1.52×.
  • Shot-Awareness: Shot-adaptive scheduling improved space-time utility ($\eta$) by 2.87× to 4.04× without harming fidelity.
• Error Analysis: The authors demonstrated that fidelity degradation is directly correlated to the ratio of helper-qubit routing cost to total routing cost, providing a predictable way to manage noise.

5. Significance

HALO represents a paradigm shift in quantum resource management. By treating helper qubits as a dynamic, shared resource rather than static hardware components, it addresses the fundamental bottleneck of quantum cloud scalability. This work provides a blueprint for how future quantum operating systems can maximize the utility of NISQ (Noisy Intermediate-Scale Quantum) devices, making quantum computing more cost-effective and accessible to a larger user base.