KEY / MODULES

Key modules

Ongoing work in quantum compilation and high-performance computing. Each module addresses a specific problem with its own methods, implementation, and experimental results.

ArcTN contraction planning: convert the tensor network formed by A, B, C, and D into a binary contraction tree. Green highlights the merge of A and C.

MODULE / 01

ArcTN Rust-native tensor-network contraction planning

Find tensor-network contraction paths with lower computation and memory costs for large-scale quantum-circuit simulation.

Parallel TemperingRustTensor Network

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ArcQML training loop: encode data into a quantum circuit with parameters θ, compute loss L after measurement, and update circuit parameters using gradients.

MODULE / 02

ArcQML Rust-native quantum machine learning framework

Connect parameterized circuits, state simulation, automatic differentiation, and optimizers in a complete quantum machine-learning training loop.

AutogradSimulatorOptimizer

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MODULE / NEXT

More modules are coming

New work on circuit synthesis, hardware mapping, and quantum software will be added after validation.

In ResearchComing Soon

Ongoing development