Why Parallelize?

Why Parallelization Matters

In a quantum datacenter with CPUs, GPUs, and QPUs all available, the worst thing you can do is use one resource at a time. Quantum algorithms are inherently parallel — they generate large batches of circuits that can and should be distributed across every available backend simultaneously.

Quantum Algorithms Generate Many Circuits

Most practical quantum algorithms don’t run a single circuit. They run hundreds or thousands:

Algorithm	Why It Generates Many Circuits
VQE	Each optimization iteration evaluates the Hamiltonian across multiple measurement bases
QAOA	Parameter optimization requires repeated circuit evaluations; gradient methods double the count
Time Evolution	Multiple Trotter steps, ensemble averaging for QDrift, trajectories across time points
Circuit Cutting	A single large circuit is split into many smaller sub-circuits

Running these sequentially — one circuit after another on one device — wastes the very infrastructure a quantum datacenter provides.

Hardware Is the Bottleneck

Today’s quantum hardware introduces delays at every step:

• Queue times: Cloud QPUs can have minutes-long wait times per job
• Calibration drift: Hardware properties change, so faster execution means more reliable results
• Limited qubits: No single device can handle the full problem — partitioning is necessary

The only way to overcome these constraints is to saturate all available resources in parallel.

What Parallelization Looks Like

Qoro’s platform parallelizes at every level:

1. Problem decomposition — Large optimization problems are partitioned into sub-problems that fit on available hardware
2. Circuit batching — Hundreds of circuits from a single algorithm iteration are dispatched simultaneously
3. Gradient parallelism — Parameter-shift gradient circuits are generated and executed in parallel, not sequentially
4. Multi-backend distribution — Circuits are routed to simulators, GPUs, and QPUs concurrently based on their characteristics
5. Classical overlap — Classical post-processing and parameter updates happen while the next batch of circuits is already running

The Result

Instead of a sequential loop that waits for each circuit to complete before submitting the next, Qoro’s stack keeps every resource busy:

  flowchart LR
    A["Algorithm generates 200 circuits"] --> B["Batch dispatcher"]
    B --> C["Maestro (GPU sim)"]
    B --> D["IBM QPU"]
    B --> E["IQM QPU"]
    B --> F["Local simulator"]
    C --> G["Result aggregation"]
    D --> G
    E --> G
    F --> G
    G --> H["Next optimization iteration"]
    H --> A

This is why Divi, Composer, and Maestro exist — to turn a collection of heterogeneous quantum and classical resources into a single, high-throughput execution engine.