The most impactful quantum algorithms are not purely quantum. Variational quantum eigensolvers alternate between quantum circuit evaluation and classical gradient optimization. Quantum approximate optimization algorithms require classical pre-processing to encode problem instances and classical post-processing to decode measurement statistics. Quantum machine learning pipelines embed quantum feature maps within larger classical neural network architectures. In every case, the performance bottleneck is not the QPU alone — it is the round-trip latency and data transfer overhead between quantum and classical resources. The k&z Hybrid Quantum + HPC platform eliminates this bottleneck by co-locating QPU Blocks with high-performance classical compute nodes in the same facility, connected by a purpose-built ultra-low-latency interconnect fabric.
Traditional approaches to hybrid quantum-classical computing treat the QPU as a remote accelerator accessed over the public internet or a wide-area network. Circuit parameters are prepared on a classical workstation, transmitted to a cloud QPU, executed, and results returned — a cycle that typically takes 500 milliseconds to several seconds per iteration, even with fast network connections. For iterative algorithms requiring thousands or millions of such cycles, this latency dominates total execution time and makes many hybrid algorithms impractical. The k&z Hybrid platform reduces this round-trip to under 100 microseconds by placing classical compute nodes within the same low-latency network fabric as the QPU control electronics, using RDMA-over-Converged-Ethernet (RoCE) connections with sub-microsecond kernel bypass.
The classical HPC component of the Hybrid platform is built on the latest generation of accelerated compute nodes. Each node features dual high-core-count x86 processors, 1-2 TB of DDR5 memory, and up to eight data-center GPUs with 80 GB of HBM3 memory each. These nodes are interconnected via a high-radix, non-blocking InfiniBand NDR fabric delivering 400 Gb/s per port, enabling distributed classical computations — such as tensor network contractions, gradient calculations, or neural network training — to scale across dozens of nodes without bandwidth bottlenecks. The same fabric extends seamlessly to the QPU control plane, creating a unified computing environment where quantum and classical resources appear as peers in a single job scheduler.
Orchestrating hybrid workflows across heterogeneous quantum and classical resources requires purpose-built software. The k&z Nexus orchestration layer provides a declarative workflow definition language that lets you specify quantum circuits, classical compute tasks, data dependencies, and iteration logic in a single program. Nexus compiles your workflow into an optimized execution graph, schedules quantum and classical tasks for maximum parallelism, manages data movement between QPU measurement results and classical memory, and handles error recovery and retry logic automatically. Nexus supports Python, C++, and Rust client libraries, integrates natively with popular quantum SDKs (Qiskit, Cirq, Pennylane), and exposes a REST API for custom integrations.
Key Capabilities
Ultra-Low-Latency Interconnect
QPU control electronics and classical HPC nodes share a unified RoCE/InfiniBand NDR fabric with sub-100-microsecond round-trip latency. This is 5,000x faster than typical cloud QPU access patterns, making iterative hybrid algorithms like VQE, QAOA, and quantum reinforcement learning practically executable for the first time at scale. Data transfers between QPU readout buffers and GPU memory occur via zero-copy RDMA, eliminating serialization overhead.
GPU-Accelerated Classical Compute
Each classical node delivers up to 640 GB of HBM3 GPU memory and 20 petaflops of mixed-precision compute. Use these resources for classical optimization loops, tensor network simulation of quantum circuits, training of quantum-classical neural networks, or post-processing of quantum measurement data. Scale from one node to 64 nodes within a single hybrid job with linear bandwidth scaling across the InfiniBand fabric.
Nexus Orchestration Engine
Define hybrid quantum-classical workflows declaratively using Nexus's Python-native DSL or YAML workflow specifications. Nexus automatically parallelizes independent tasks, pipelines data between quantum and classical stages, manages QPU calibration state, and implements circuit knitting and cutting techniques to partition large quantum circuits across multiple QPU Blocks when needed. Built-in checkpointing enables long-running workflows to resume from the last successful iteration after any interruption.
Quantum-Aware Job Scheduler
The k&z hybrid scheduler understands both classical resource requirements (CPU cores, GPU count, memory) and quantum resource requirements (qubit count, connectivity, coherence thresholds). It co-schedules quantum and classical tasks to minimize idle time on both resource types, automatically aligning QPU circuit batches with classical processing windows. Priority queues and preemption policies let you balance throughput and latency across multiple concurrent hybrid jobs.
Integrated Data Pipeline
Hybrid workflows generate massive volumes of intermediate data — measurement bitstrings, gradient vectors, parameter updates, convergence metrics. The k&z data pipeline provides high-throughput streaming from QPU readout to classical memory, real-time aggregation and statistical analysis of measurement outcomes, and persistent storage to a distributed object store for experiment reproducibility. All data remains within the secure facility perimeter; nothing traverses the public internet.
Circuit Knitting & Distribution
For quantum computations requiring more qubits than a single QPU Block provides, Nexus implements automatic circuit knitting — decomposing large circuits into subcircuits that execute on separate QPU Blocks, with classical post-processing to reconstruct the full result. This technique trades additional classical compute and shot overhead for the ability to simulate larger quantum systems, with Nexus optimizing the knitting strategy to minimize total overhead.
Technical Specifications
| Parameter | Specification |
|---|---|
| QPU Resources | 1–16 QPU Blocks (256–4,096 qubits), configurable per job |
| Classical Compute Nodes | 1–64 accelerated nodes per hybrid cluster |
| GPUs per Node | Up to 8 × 80 GB HBM3 data-center GPUs |
| CPU per Node | Dual 96-core x86 processors, 1–2 TB DDR5 RAM |
| Interconnect Fabric | InfiniBand NDR 400 Gb/s + RoCE (QPU ↔ classical) |
| QPU ↔ Classical Latency | < 100 μs round-trip (RDMA zero-copy) |
| Classical ↔ Classical Latency | < 2 μs (InfiniBand NDR, same rack) |
| Orchestration Engine | k&z Nexus (Python, C++, Rust SDKs; REST API) |
| Supported Quantum SDKs | Qiskit, Cirq, Pennylane, TKET, QASM 3.0 |
| Supported Classical Frameworks | PyTorch, JAX, TensorFlow, CUDA, MPI, OpenMP |
| Circuit Knitting | Automatic decomposition with overhead optimization |
| Persistent Storage | Distributed object store, 100+ TB per cluster |
| Checkpointing | Automatic workflow checkpointing with configurable intervals |
| Reservation Model | Hourly, daily, weekly, or dedicated long-term allocation |
Ideal For
- Variational quantum algorithm research — VQE, QAOA, quantum neural networks, and other parameterized quantum circuit methods that require tight iteration loops between quantum circuit evaluation and classical parameter optimization. The sub-100-microsecond interconnect latency makes thousands of optimization iterations per hour practical, compared to single-digit iterations on cloud-based setups.
- Quantum-classical machine learning — Hybrid architectures that embed quantum feature maps, quantum kernels, or quantum reservoir layers within classical deep learning pipelines. Train on GPU-accelerated classical nodes and evaluate quantum components on co-located QPU Blocks within the same training loop, with no serialization or network overhead between the quantum and classical stages.
- Large-scale quantum simulation — Simulations that exceed the capacity of a single QPU Block and require circuit knitting or distributed quantum computing techniques, combined with classical tensor network methods for verification and post-processing. The Hybrid platform provides both the quantum resources and the classical compute power needed for these demanding workflows.
- Quantum error correction development — QEC research requiring rapid syndrome decoding on classical hardware followed by conditional quantum operations, where the classical decoding latency directly impacts logical error rates. The co-located classical nodes provide the fast, deterministic decoding that real-time QEC demands.
- Quantum chemistry and materials science — Active-space quantum chemistry calculations embedded within larger classical DMRG, CCSD(T), or DFT workflows, where the quantum processor handles the strongly correlated subspace and classical HPC handles the weakly correlated environment. The Nexus orchestrator manages the embedding loop automatically.
- Benchmarking and algorithm comparison — Studies that compare quantum, classical, and hybrid approaches on the same problem instances require both QPU and HPC resources in the same environment. The Hybrid platform provides a controlled experimental setting for rigorous performance comparisons without confounding network latency variables.
The k&z Hybrid Quantum + HPC platform is designed for teams that understand that quantum advantage is not achieved by the QPU alone — it emerges from the intelligent integration of quantum and classical resources, orchestrated by software that understands the unique characteristics of both. Our platform provides the physical infrastructure, the interconnect fabric, and the orchestration software to make hybrid quantum-classical computing a practical, productive reality rather than a theoretical aspiration.
To configure a Hybrid cluster for your research program, contact our solutions architecture team. We will work with you to size the quantum and classical components based on your algorithmic requirements, design the interconnect topology for your workload patterns, and deliver a fully integrated system with Nexus pre-configured for your preferred quantum and classical frameworks.