Hybridlane: A Software Development Kit for Hybrid Continuous-Discrete Variable Quantum Computing

TL;DR

Hybridlane introduces automatic wire type inference to distinguish qubits from qumodes without manual annotations, enabling compile-time validation of circuit correctness, and can describe wide and deep circuits with minimal memory consumption and without requiring simulation.

Abstract

Hybrid quantum computing systems that combine discrete-variable qubits with continuous-variable qumodes offer promising advantages for quantum simulation, error correction, and sensing applications. However, existing quantum software frameworks lack native support for expressing and manipulating hybrid circuits, forcing developers to work with fragmented toolchains or rely on simulation-coupled representations that limit scalability. We present Hybridlane, an open-source software development kit providing a unified frontend for hybrid continuous-discrete variable quantum computing. Hybridlane introduces automatic wire type inference to distinguish qubits from qumodes without manual annotations, enabling compile-time validation of circuit correctness. By decoupling gate semantics from matrix representations, Hybridlane can describe wide and deep circuits with minimal memory consumption and without requiring simulation. The framework implements a comprehensive library of hybrid gates and decompositions following established instruction set architectures, while remaining compatible with PennyLane's extensive qubit algorithm library. Furthermore, it supports multiple backends including classical simulation with Bosonic Qiskit and hardware compilation to Sandia National Laboratories' QSCOUT ion trap. We demonstrate Hybridlane's capabilities through bosonic quantum phase estimation and ion trap calibration workflows.

Figures (3)

• Figure 1: Basic example of defining a hybrid CV-DV quantum program in Hybridlane, following the standard PennyLane syntax. Type inference and circuit transforms allow targeting the quantum node to specific CV-DV devices.
• Figure 2: Overview of performing quantum phase estimation on a simple hybrid Hamiltonian in (\ref{['eq:dispersive_ham']}). (Left) Code block expressing the circuit. After using a Hybridlane template to prepare the test eigenstate $\ket{0,4}$, the PennyLane QPE template is applied, and finally the qubits are read out in their discrete computational basis ($Z$). The wire names are colored by type. (Right) Histogram of the eigenvalue distribution with 10 estimation qubits and 1024 shots. (Bottom) The decomposition of the circuit into the native gate set of the Bosonic Qiskit simulator.
• Figure 3: Overview of performing $CD$ gate calibration using Hybridlane. (Left) The calibration circuit maps the area of a loop in oscillator phase-space into a rotation on an ancilla qubit liu2026hybrid. Like in PennyLane, we specify the circuit instructions as a Python function to be reused across devices. (Center) Simulating the circuit using Bosonic Qiskit allows verification of its behavior prior to running on hardware. (Right) By switching Hybridlane devices, the same circuit is transformed to run on the Sandia QSCOUT ion trap. Because part of the decomposition process uses an auxiliary qubit (0) to realize the $D$ gates, the output is postselected on the auxiliary qubit ending in state $\ket{0}$.