memQ Releases memQ DQC: An Open-Source Framework for Distributed Quantum Computing

memQ Releases memQ DQC: An Open-Source Framework for Distributed Quantum Computing Quantum networking startup memQ Inc. has officially released memQ DQC (version 0.1.2-beta), an open-source, modality-agnostic software framework designed to compile, partition, and schedule quantum programs across multi-processor distributed quantum architectures. Accompanied by a technical preprint published on arXiv (arXiv:2609.15728), the Python library (GitHub Repository) bridges the gap between monolithic circuit compilers and network-level simulators, addressing software-hardware co-design for entanglement-based distributed quantum computing (DQC). As physical constraints limit the expansion of monolithic quantum processing units (QPUs), scaling beyond hundreds of physical qubits relies on interconnecting multiple QPUs using Einstein-Podolsky-Rosen (EPR) entangled pairs. However, distributing a quantum program introduces complex trade-offs: cross-QPU operations require either state teleportation (moving a qubit state between processors) or gate teleportation (executing a remote controlled gate via Cat-Entangler/Disentangler protocols). The memQ DQC framework allows researchers to define arbitrary inter-QPU network topologies (chains, rings, hubs, grids, and all-to-all) alongside intra-QPU qubit connectivity maps using an interactive Quantum Network Constructor (QNC). The compiler ingests standard OpenQASM circuits, partitions operations across network nodes based on hardware capacity constraints, routes remote gates, and outputs a distributed OpenQASM file. The framework includes a multi-algorithm compiler paired with both deterministic and discrete-event (stochastic) schedulers. The scheduler models Poisson-distributed photon arrival events for heralded remote entanglement generation, tracking link arbitration, gate execution times, and total schedule makespans. [ memQ DQC Software Architecture & Core Components ]Component / ModuleFunctional RoleAlgorithmic & Architectural DetailsQuantum Network ConstructorNetwork SpecificationGenerates JSON topology files defining intra-QPU layout, inter-QPU links, and fidelity parameters.Dynamic Interaction PartitionerSegmented Circuit PartitioningAdapts Kernighan-Lin graph partitioning over time segments to allow dynamic qubit relocation via remote SWAP.Hypergraph PartitionerGate Packet OptimizationUses KaHyPar hypergraph partitioning to maximize remote gate grouping (Cat-Entangler/Disentangler reuse).Verification EngineEquivalence CheckingVerifies measurement-outcome distribution equality against original monolithic circuits (Statevector/Sampling).Discrete-Event SchedulerHardware Timeline MappingAssigns timestamps under FIFO, Shortest-Duration, or Critical-Path link-arbitration policies. In benchmarking studies across algorithms like the Quantum Fourier Transform (QFT), Quantum Volume, and Adders, the authors demonstrated that compilation performance depends heavily on hardware topology: Intra-QPU Connectivity Impact: Switching intra-QPU connectivity assumptions from all-to-all (e.g., trapped-ion) to nearest-neighbor (e.g., superconducting) increased total EPR-pair consumption by more than 10× on an 18-qubit QFT circuit due to local SWAP routing breaking remote gate groups. Algorithm-Dependent Strategy: The Hypergraph partitioner reduced EPR cost on structured Adders by 83.7% compared to baselines, whereas the Dynamic Interaction partitioner proved superior for unstructured Quantum Volume circuits. Link Arbitration: Implementing contention-aware link arbitration in the scheduler reduced total execution completion time by ~18% over standard FIFO queuing on congested network links. Review the announcement on memQ Resources here, inspect the software repository on GitHub here, read the technical preprint on arXiv here, and view the API documentation on Read the Docs here. September 24, 2026 Mohamed Abdel-Kareem2026-09-24T21:44:16-07:00 Leave A Comment Cancel replyComment Type in the text displayed above Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.