quantum-circuit-synthesis-gst

name

quantum-circuit-synthesis-gst

description

Generative quantum circuit synthesis from Gate Set Tomography (GST) data using diffusion models and set-vision transformers. Bypasses traditional two-step pipeline (GST characterization + unitary decomposition) by directly learning generative concept spaces from raw GST data. Use when synthesizing hardware-native quantum circuits, learning from gate characterization data, or building context-aware quantum compilation pipelines. Activation: quantum circuit synthesis, GST circuit generation, hardware-native compilation, quantum diffusion model, QMLC framework.

Quantum Circuit Synthesis from GST Data (QMLC Framework)

Overview

The QMLC (Quantum Machine Learning Control) framework learns a generative concept space directly from Gate Set Tomography (GST) data, enabling conditional synthesis of quantum circuits that account for real device noise (crosstalk, drift) without explicit characterization steps.

Core Architecture

Step 1: GST Germ Circuit Tokenization

Tokenize GST germ circuits into structured sequences:

• Each germ circuit → token sequence representing gate operations
• Curriculum learning: start with short circuits, progressively add longer ones
• Embed sequences into latent space

Step 2: Set-Vision Transformer with Permutation-Invariant Pooling

Process embedded circuit sequences:

Circuit tokens → Set-ViT → Permutation-invariant pooling → k-seed vectors

Key properties:

• Permutation invariance: Order of circuits doesn't affect representation
• Context awareness: Aggregates across multiple circuits
• Noise environment capture: Shared physical noise (crosstalk, drift) is encoded in latent representation

Step 3: Latent Concept Space

The k-seed vectors represent the learned concept space of the quantum device:

• Each seed vector encodes a "concept" about the device's behavior
• Captures correlations between gates that isolated metrics miss
• Enables conditional generation based on target distributions

Step 4: Diffusion Model for Circuit Generation

Unconditional sampling: Sample from the concept space Conditional generation:

1. User provides target measurement distribution
2. Diffusion model generates circuit producing that distribution
3. Output denoised via diffusion on conditional covariance matrix

Workflow

Raw GST Data → Tokenization → Curriculum Learning → Set-ViT Encoding
    → Concept Space (k seeds) → Diffusion Model → Circuit Synthesis

Advantages Over Traditional Pipeline

Aspect	Traditional	QMLC
Steps	2 (GST + decomposition)	1 (end-to-end)
Noise awareness	Single-gate metrics	Context-aware
Crosstalk modeling	Separate calibration	Built into latent space
Drift adaptation	Recalibrate	Latent space adapts

Implementation Patterns

Pattern 1: Curriculum Learning Strategy

# Phase 1: Short circuits (1-2 gates)
# Phase 2: Medium circuits (3-5 gates)
# Phase 3: Long circuits (6+ gates) with diverse statistics

Pattern 2: Conditional Covariance Denoising

# During inference:
# 1. Sample from diffusion model given target distribution
# 2. Denoise output using diffusion on conditional covariance
# 3. Result: circuit robust to device noise

Pattern 3: Set-Vision Transformer Architecture

# Input: Set of embedded circuit sequences (order doesn't matter)
# Architecture: Transformer with self-attention over set elements
# Pooling: Permutation-invariant (e.g., mean/max pooling)
# Output: Fixed-dimensional concept vectors

Target Applications

• NISQ devices: Near-term quantum devices with complex calibration
• Hardware-native compilation: Generate circuits respecting device topology
• Automated calibration: Reduce manual calibration overhead
• Noise-aware synthesis: Account for crosstalk and drift automatically

Key Insights

1. Context matters: Isolated gate metrics miss correlated noise
2. GST data is rich: Contains full device characterization information
3. Generative approach: Learn distribution over circuits, not single optimum
4. Diffusion for denoising: Ensure generated circuits are physically realizable

References

• QMLC paper: arxiv:2605.01367 (Yu, Sarkar, Hua, Rimbach-Russ, Ishihara, 2026)
• Gate Set Tomography: Blume-Kohout et al.
• Set Transformers: Lee et al. (2019)
• Diffusion Models: Ho et al. (2020)