⚛️ quantum physics

EmuPlat: A Framework-Agnostic Platform for Quantum Hardware Emulation with Validated Transpiler-to-Pulse Pipeline

EmuPlat is a framework-agnostic quantum hardware emulation platform that bridges the interoperability gap between high-level programming frameworks and pulse control systems by providing a unified, validated transpiler-to-pulse pipeline capable of achieving high-fidelity circuit execution across diverse quantum ecosystems.

Original authors: Jun Ye, Jun Yong Khoo

Published 2026-04-01

📖 4 min read🧠 Deep dive

CC BY 4.0

Original authors: Jun Ye, Jun Yong Khoo

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to bake a complex cake. You have a wonderful recipe book (the Quantum Algorithm) written in a language you love, like "Cupcakes in Python." But the oven you want to use (the Quantum Hardware) only understands a very specific, ancient dialect of instructions called "Pulse Language."

Currently, if you want to bake this cake, you have to hire a different translator for every single oven. If you switch ovens, you have to rewrite your entire recipe. This is the problem in the world of quantum computing today: there are too many different software "languages" and hardware "ovens" that don't talk to each other well.

Enter EmuPlat.

Think of EmuPlat as a universal smart kitchen adapter. It doesn't matter if your recipe is written in Qiskit, CUDA-Q, or any other fancy language. EmuPlat sits in the middle, translating your high-level recipe into the exact, tiny electrical pulses needed to make the oven work, all while simulating the cooking process so you know if the cake will burn before you actually turn on the heat.

Here is how it works, broken down into simple steps:

1. The Translator (The Transpiler)

Imagine your recipe says, "Mix the batter." In the quantum world, "Mix" is a complex instruction. EmuPlat's translator breaks this down into the smallest, most basic moves the oven can actually do.

The Magic Trick: It uses a clever shortcut called "Virtual Z."
- Analogy: Imagine you need to rotate a spinning top to a new angle. Usually, you'd have to physically push it (which takes time and energy). But with Virtual Z, EmuPlat just tells the top, "Hey, pretend you rotated," without actually pushing it. It changes the "phase" (the timing) of the next step instead. This saves a huge amount of time and energy, making the process much faster and more efficient.

2. The Traffic Cop (The Compiler)

Once the recipe is broken down into basic moves, EmuPlat acts as a traffic cop.

The Problem: In a quantum computer, some qubits (the "ingredients") are connected, and others aren't. If you need to mix two ingredients that are in different rooms, you have to move them.
The Solution: EmuPlat automatically figures out the best path to move things around (inserting "SWAP" moves) so nothing crashes into each other. It then turns those moves into precise electrical pulses—tiny bursts of energy that last for nanoseconds (billionths of a second).

3. The Simulator (The Digital Twin)

Before you actually run the experiment on a real, expensive quantum computer, EmuPlat lets you run it in a perfect digital twin.

The Reality Check: Real quantum computers are messy. They have "noise" (like a drafty kitchen or a shaky hand). EmuPlat simulates this messiness. It asks: "If I run this recipe with the real-world noise, will the cake still taste good?"
The Result: The paper shows that EmuPlat is incredibly accurate. When they simulated making a "Bell State" (a special quantum link between two particles), the digital simulation was 99.958% perfect. It matched what you'd expect from a real machine almost exactly.

Why Does This Matter?

Right now, if a scientist wants to test a new idea, they might get stuck because their software doesn't work with their hardware, or they can't predict if their idea will fail due to noise.

EmuPlat changes the game by:

Connecting the Dots: It lets researchers use their favorite software tools (like Qiskit or CUDA-Q) to talk to any hardware system without rewriting code.
Saving Time: By simulating the "noise" and the "pulses" perfectly, scientists can fix their recipes in the computer before they ever touch the expensive hardware.
Future-Proofing: It's built like a set of Lego blocks. If a new type of quantum computer (like one using trapped ions instead of superconducting circuits) comes out, you can just snap a new block onto EmuPlat, and it will work.

The Bottom Line

EmuPlat is the universal translator and test kitchen for the quantum world. It bridges the gap between the abstract ideas of computer scientists and the messy, physical reality of quantum machines. It ensures that when we finally build the "Quantum Internet" or solve complex problems like drug discovery, our software can actually talk to the hardware, and we won't be burning our cakes in the process.

1. Problem Statement

The quantum computing ecosystem suffers from severe fragmentation and interoperability issues. High-level software development kits (SDKs) like Qiskit and CUDA-Q operate in isolated stacks, while low-level hardware control systems (e.g., Qibolab) and physics simulators (e.g., QuTiP) lack seamless integration.

The Gap: Researchers cannot easily design an algorithm in a high-level framework, apply hardware-specific compilation optimizations (like Virtual Z gates), and simulate the result with accurate pulse-level noise models in a single environment.
The Cost: This fragmentation creates a significant "interoperability cost," leading to platform lock-in, increased development effort, and a lack of standardized metrics for comparing simulation engines or transpilation performance.
The Need: There is a critical need for a unified infrastructure that bridges abstract quantum circuits with hardware-specific pulse control, enabling accurate, hardware-aware simulation for Noisy Intermediate-Scale Quantum (NISQ) devices, particularly superconducting transmons.

2. Methodology and System Architecture

EmuPlat is a framework-agnostic emulation platform designed to bridge high-level programming with low-level pulse control. It employs a Clean Architecture design pattern with strict dependency rules and a modular Adapter Pattern to ensure extensibility.

A. Core Architecture

The system is organized into five layers:

Core: Abstract interfaces and contracts.
Domain: Business logic and quantum physics models.
Application: Orchestration of use cases.
Infrastructure: External integrations (e.g., QuTiP, Qibolab).
Config: System configuration.

B. The Transpiler-to-Pulse Pipeline

EmuPlat implements a validated, four-stage transformation pipeline:

Preprocessing & Placement: Optimizes circuits and maps logical qubits to physical qubits using configurable algorithms (Random, ReverseTraversal).
Routing: Uses connectivity-aware strategies (ShortestPaths, Sabre) with automatic SWAP insertion to handle hardware connectivity constraints.
Gate Decomposition (Unrolling): Recursively decomposes high-level gates into a minimal native set:
$G_{native} = \{I, Z, RZ(\theta), GPI2(\phi), CZ, M\}$
- Optimization: Virtual Z (VZ) optimization is applied, tracking phase shifts without generating physical pulses, reducing pulse counts by 30–50%.
Pulse Compilation: Deterministically maps native gates to calibrated pulse sequences:
- Z/RZ: Converted to zero-duration VirtualZ operations.
- GPI2: Mapped to 40ns DrivePulse operations.
- CZ: Mapped to 96ns parametric DrivePulse sequences.
- M (Measurement): Mapped to 1000ns ReadoutPulse operations.

C. Simulation and Integration

Simulation Engine: Uses an adapter pattern to integrate QuTiP for solving the Lindblad master equation. It models coherent dynamics (Transmon Hamiltonian with anharmonicity) and incoherent noise (T1/T2 decoherence, dephasing).
Framework Agnosticism: Supports multiple entry points (CUDA-Q kernels, OpenQASM strings, Qibo circuits). It translates CUDA-Q to OpenQASM 2.0, then to Qibo circuits, allowing cross-framework compatibility.

3. Key Contributions

Unified Infrastructure: The first platform to provide a complete, validated pipeline from high-level algorithm specification to hardware-accurate pulse-level simulation, bridging CUDA-Q, Qiskit, and Qibolab.
Validated Transpiler-to-Pulse Pipeline: A systematic approach that includes recursive gate decomposition, connectivity-aware routing, and deterministic pulse compilation with Virtual Z optimization.
Modular Adapter Pattern: A novel architectural design that decouples domain logic from specific simulation engines (e.g., QuTiP vs. CUDA-Q Dynamics), enabling easy benchmarking and engine switching.
Hardware-Aware Noise Modeling: Accurate simulation of superconducting transmon physics, including multi-level state projections and realistic noise models derived from actual device parameters.

4. Experimental Results

The platform was validated using parameters from Anyon Technologies superconducting quantum processors (2-qubit and 4-qubit configurations with $T_1 \approx 24 \mu s$ and $T_2 \approx 33 \mu s$ ).

A. Bell State Preparation

Task: Preparation of the Bell state $|\Psi^+\rangle = \frac{1}{\sqrt{2}}(|00\rangle + |11\rangle)$ .
Process: The circuit was decomposed into native gates and compiled into a 7-pulse sequence (1272 ns total duration).
Result: Achieved 99.958% fidelity against the ideal Bell state when simulating with the QuTiP master equation solver.
Statistics: Measurement shots (1000) showed population distributions of 51.4% ( $|00\rangle$ ) and 46.8% ( $|11\rangle$ ), closely matching theoretical expectations, with minor deviations attributed to decoherence during idle periods.

B. Quantum Fourier Transform (QFT)

Task: Implementation of 2-qubit and 4-qubit QFT circuits to test scalability and phase accuracy.
Scaling:
- 2-qubit: 624 ns execution time, 10 drive pulses.
- 4-qubit: 3328 ns execution time, 76 drive pulses (40 GPI2, 36 CZ).
Performance: The platform successfully handled the $O(n^2)$ gate complexity of QFT. The 4-qubit simulation tracked dynamics across a 16-dimensional computational space (and an 81-dimensional Hilbert space including higher transmon levels).
Outcome: The simulation demonstrated correct interference patterns and convergence to equal-amplitude superpositions, validating the transpiler's ability to handle complex phase accumulation via Virtual Z gates.

5. Significance and Impact

Accelerated Co-Design: EmuPlat enables hardware-software co-design by allowing algorithm developers to prototype and optimize algorithms with realistic noise models before deploying on actual hardware.
Interoperability Standard: It addresses the "interoperability cost" by providing a standardized interface for cross-framework integration, reducing platform lock-in.
Educational and Research Value: It serves as a tool for understanding the full quantum stack (from gates to pulses) and for validating transpiler optimizations.
Future Scalability: While currently limited to ~10 qubits due to density matrix scaling, the architecture is designed to incorporate tensor network methods and GPU acceleration (via CUDA-Q Dynamics) to support larger systems.

In conclusion, EmuPlat establishes a critical foundation for the next generation of hybrid quantum-classical computing, offering a production-ready, framework-agnostic environment for high-fidelity hardware emulation.