Product · TensorDual-VQC

The First Scalable Quantum Advantage.

TensorDual-VQC is the quantum-native variant of TensorHyper. It bridges GPU and QPU workloads through a unified tensor representation, resolves barren plateaus, and runs on real quantum hardware — today, on NISQ devices.

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Resolves the barren plateau

Residual optimization in the tensor-structured circuit keeps gradient variance stable as depth grows — making deep VQC training tractable.

Eliminates exponential parameter scaling

Tensor decomposition replaces dense parameterization, so circuit width and depth scale linearly with model capacity.

156-qubit IBM Heron validation

TensorDual-VQC runs on real superconducting hardware today, not just simulators. NISQ-era ready.

Unified GPU + QPU runtime

Workloads dispatch between GPU and QPU through a single tensor representation — no rewrite, no migration cost.

Execution path: Dispatch → Native

The same tensor model walks through four execution modes — starting classical, ending quantum-native — without a rewrite.

  1. Step 1

    Dispatch

    Classical execution with tensor representation

  2. Step 2

    Distributed

    Multi-device tensor coordination across GPUs

  3. Step 3

    Emulation

    QPU emulation layer for circuit validation

  4. Step 4

    Native

    Run directly on 156-qubit IBM Heron and beyond

Why this matters now

Quantum hardware is expected to remain in the NISQ era for the foreseeable future. The physical-to-logical qubit ratio is still high, and naive variational circuits hit barren plateaus long before they reach useful depth. TensorDual-VQC sidesteps both problems by embedding tensor structure into the circuit itself — making real quantum advantage available today, not in a decade.