| name | method-qcs |
| description | Use when a quantum circuit simulation, VQE, TensorCircuit-NG, JAX backend, contraction path, statevector, MPS circuit, gradient, or circuit-performance reproduction needs method-level route and tool selection. |
Method QCS
Quantum circuit simulation is the differentiable-circuit and performance track. Use it to choose representation and software stack before tuning backend settings.
Sources
- Methodology reference (reproduction-grade algorithm, parameters, validation, gap analysis):
references/qcs-methodology.md
- Track README:
tracks/qcs/README.md
- Interview notes:
docs/qcs/interview.html
- Review notes:
docs/qcs/review.html
- Challenge brief:
docs/qcs/backup.md
- Tool skills:
/using-tensorcircuit-ng, /using-jax
Route
- Choose representation first: tensor-network contraction, full statevector, or MPS circuit. This determines memory and whether the result is exact or approximate.
- Recommend
/using-tensorcircuit-ng for large/deep differentiable VQE, energy+gradient profiling, contraction tuning, batching, scan/checkpointing, and the QCS track benchmark.
- Invoke
/using-jax for backend, CPU/GPU, precision, device, and compile/warm-runtime setup.
- If the paper requires Qiskit, PennyLane, Yao, cuQuantum, or quimb specifically, offer official code / web search or a new tool skill instead of silently forcing TensorCircuit-NG.
- Keep compile time, path-search time, warm runtime, and optimizer-loop time separate in any proposal or report.
Tool Handoff
Invoke /using-tensorcircuit-ng after the route is chosen. /using-tensorcircuit-ng owns representation-specific parameters, contraction/path settings, observable format, gradients, validation, and time estimate. Invoke /using-jax for backend setup.
Details
Classical simulation of quantum circuits, including variational circuit optimization (VQE) and general circuit contraction. In this harness, the default route uses TensorCircuit-NG on a JAX backend with automatic differentiation, JIT compilation, and tensor-network contraction controls. It is not a hardware-execution workflow.
This card is generic methodology. Paper-specific benchmarks, hardware layouts, and figure protocols belong in /reproduce-paper protocols or run specs, not here.
Scope
Use this card for:
- Parameterized circuit ansatz construction.
- Hamiltonian expectation values from dense, sparse, Pauli-sum, or MPO-like representations.
- Reverse-mode gradients through circuit contractions.
- JIT-compiled value-and-gradient kernels.
- Runtime, compile-time, and peak-memory profiling of differentiable VQE steps.
- Contraction-path, slicing, batching, scan, checkpointing, and precision studies.
Do not use this card for:
- Hardware execution, cloud submission, readout mitigation, or QPU calibration.
- QAOA or generic quantum algorithms unless they are treated as VQE-like variational energy minimization.
- TensorCircuit-NG paper-specific benchmark reproduction.
- General quantum-circuit simulation without a variational energy / differentiable objective.
Notation
theta: variational parameters.- Ansatz: parameterized circuit preparing
|psi(theta)>.
H: Hamiltonian represented as Pauli terms, sparse matrix, dense matrix, or tensor-network operator.- Forward pass: compute
E(theta).
- Backward pass: compute
grad_theta E(theta) by reverse-mode AD.
- JIT compile time: first traced/compiled execution cost.
- Steady runtime: execution time after compilation/cache warmup.
- Peak memory: maximum device or host memory during forward/backward execution.
- Contractor: tensor-network contraction path and execution strategy.
Pitfalls
- Dense Hamiltonian blowup: prefer sparse, Pauli-sum, or MPO-like representations when a dense matrix would dominate memory. The representation choice is part of the method, not only a runtime tweak.
Verification
- Small-system ED check: compare VQE energy to ED for the same Hamiltonian and boundary condition.
- Gradient check: finite-difference a small parameter subset and compare with AD gradients.
- Energy bound: VQE energy should not fall below the exact ground-state energy for a Hermitian Hamiltonian.
- Seed/depth stability: run multiple seeds and a depth sweep before claiming ansatz convergence.
Citations
.knowledge/literature/quantum-circuit-simulation/tensorcircuit-tensorcircuit-ng.md - official TensorCircuit-NG repository and documentation entry..knowledge/literature/quantum-circuit-simulation/2602.14167_tensorcircuit-ng-a-universal-composable-and-scalable-platfor.md - TensorCircuit-NG software reference..knowledge/literature/quantum-circuit-simulation/2205.10091_tensorcircuit-a-quantum-software-framework-for-the-nisq-era.md - TensorCircuit software and differentiable-circuit reference..knowledge/literature/quantum-circuit-simulation/2002.01935_hyper-optimized-tensor-network-contraction.md - cotengra contraction-path reference.
