c6002701b0
quantum_cutrank_metal (feature quantum_cutrank_metal, macOS) runs the cut-rank walk's passes on the platform's GPU: - each shot's table is 2^cut single-precision entries in one shared buffer, at most 2^31 a table, so every index fits in 32 bits; - each pass is one dispatch over (entry, shot), in the walk's order and with its arithmetic, done in f32; the passes are the same for every shot, and only each shot's weights (set by the outcomes it drew) differ, read from a small per-shot array; an amplitude is every pass encoded into one command buffer; - a sample's sums are reduced per threadgroup on the device (a SIMD-group sum, then the groups in order) and added on the host in f64, in order, for each shot; the outcome is drawn as the host's walk draws it (outcome_norm and draw, now shared by both); - a batch of shots is sampled side by side, so a qubit is one command buffer for the batch: the previous qubit's passes for each shot's outcome, then this qubit's sums; - each shot draws from a stream of its own, so a seed's samples are the same at any batch size. The kernels are compiled in the platform's safe math mode, so a run is the same bits on one device. Tests hold it to the double-precision walk on states of 10 to 34 qubits: amplitudes within 1e-5 of each amplitude's size and the same bits run to run; samples whose probabilities are their own and whose counts follow them; and the same samples one at a time and seven side by side. Without a device the tests skip; WAI_QUANTUM_CUTRANK_METAL_EXPECT makes that a failure, and the service workflow's macOS job sets it and checks both GPU tests by name. The engine's cutrank_single method runs a cutrank job on that table where the build has the feature and the host a GPU; auto never picks it. It samples up to 32 shots a batch, as many as 2 GiB beside its first table holds within the job's memory, and the plan counts them. Its amplitudes carry the circuit's global phase, found on the same table (catalog::cutrank_phase_with takes the amplitude function). The job service, the command line's metal feature and the desktop app's metal feature build it in. The service's schemas list it, and it marks the method's energy figure partial, since the counter does not read the GPU. examples/cutrank_gpu times one state's amplitudes and samples on the host's tables and on the GPU, and each kernel's pass alone. On a shared desktop machine, each device at its quietest (either side ran up to five times slower under other work), seconds for one amplitude of a random graph state: widest cut host, double host, single GPU 23 bits 0.027 0.024 0.005 25 bits 0.057 0.045 0.019 27 bits 0.18 0.17 0.046 29 bits 1.03 0.54 0.34 and for 64 samples, 16 a batch, the host at about half its quietest speed: widest cut host, single GPU, one at a time GPU, 16 a batch 16 bits 0.52 0.62 0.036 20 bits 1.97 0.73 0.067 23 bits 3.87 0.91 0.34 25 bits 7.48 1.82 1.20 A pass runs at about 550 GB/s on an idle device. Co-Authored-By: Claude Opus 5.5 <[email protected]> (cherry picked from commit f7c7160a9ab5a6255c28d34af57ee2fbc017b055)
422 lines
18 KiB
TOML
422 lines
18 KiB
TOML
[package]
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name = "wai-quantum"
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version = "0.6.12"
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edition = "2024"
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rust-version = "1.97"
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license = "Apache-2.0"
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description = "A deterministic quantum stack in pure Rust: byte-exact circuit simulation (statevector / stabilizer / tensor-network MPS / sparse-Pauli backends), sparse Pauli dynamics at utility scale (arbitrary angles, 1024 qubits), belief-propagation tensor networks on the hardware graph, error mitigation, qLDPC decoding, noise learning, circuit-equivalence proofs, a phasor interference-ML layer, information-theoretic limits, noisy channels and state tomography, and signed energy-accounted receipts. No QPU, no cloud, no system libraries in the default features — identical results native, in the browser, and as a WASI component at the edge; the opt-in WebGPU member reaches the platform's GPU interface."
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repository = "https://git.transaction.science/Transaction-Science/open-standards"
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homepage = "https://wai.transaction.science/quantum-ide"
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documentation = "https://docs.rs/wai-quantum"
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keywords = ["quantum", "simulation", "wasm", "deterministic", "qec"]
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categories = ["science", "simulation", "wasm", "mathematics"]
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readme = "README.md"
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# Holds a file outside this package (quantum-conformance/fabric/expected.json) to
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# the crate; it runs in the repository only.
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exclude = ["tests/fabric_expected.rs"]
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# docs.rs builds with DEFAULT features only, which would document 3 of 21
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# modules and hide the entire operational + ML layers. Document everything.
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[package.metadata.docs.rs]
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all-features = true
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[dependencies]
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# Hashes for circuit / statevector / receipt identity. Required: the base
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# `quantum` feature binds every reconstruction to a BLAKE3 hash.
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blake3 = "1"
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# Canonical receipt JSON (sorted keys, byte-stable round-trip).
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serde_json = { version = "1", features = ["preserve_order"] }
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# The engine's request and plan types (feature quantum_engine).
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serde = { version = "1", features = ["derive"], optional = true }
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# Receipt signatures. Optional — only the sealing features need it.
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ed25519-dalek = { version = "2", optional = true }
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# The WebGPU member of the fabric: the gate kernel in WGSL through the platform's
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# GPU interface natively, and through a browser's WebGPU on wasm32-unknown-unknown.
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# Absent on every other target; wasm32-wasip2 has no GPU interface.
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[target.'cfg(not(target_arch = "wasm32"))'.dependencies]
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wgpu = { version = "30.0.1", optional = true, default-features = false, features = ["std", "wgsl", "metal", "vulkan", "dx12"] }
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# The CUDA member: the driver and the runtime compiler are loaded when first used,
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# so a build with the feature needs no CUDA installed and finds no member where
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# there is none.
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cudarc = { version = "0.19.10", optional = true, default-features = false, features = ["std", "driver", "nvrtc", "dynamic-loading", "cuda-12080"] }
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# The OpenCL member: the ICD loader is opened when first used, so a build with the
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# feature needs no OpenCL installed and finds no member where there is none.
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opencl3 = { version = "0.12.3", optional = true, default-features = false, features = ["dynamic"] }
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# The neural-processor member: the platform's model runtime on macOS, its
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# framework loaded by the system; absent on every other target.
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[target.'cfg(target_os = "macos")'.dependencies]
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objc2 = { version = "0.6.5", optional = true }
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block2 = { version = "0.6.2", optional = true }
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objc2-foundation = { version = "0.3.2", optional = true, default-features = false, features = ["std", "NSArray", "NSDictionary", "NSError", "NSProcessInfo", "NSString", "NSURL", "NSValue"] }
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objc2-core-ml = { version = "0.3.2", optional = true, default-features = false, features = [
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"std", "block2", "MLModel", "MLModel_MLModelCompilation", "MLModelConfiguration", "MLMultiArray", "MLDictionaryFeatureProvider",
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"MLFeatureProvider", "MLFeatureValue", "MLComputePlan", "MLComputePlanDeviceUsage", "MLComputeDeviceProtocol",
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"MLNeuralEngineComputeDevice", "MLModelStructure", "MLModelStructureProgram", "MLModelStructureProgramFunction",
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"MLModelStructureProgramBlock", "MLModelStructureProgramOperation",
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] }
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# The Metal member: the platform's GPU interface, its kernels compiled at run
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# time by the system's own compiler.
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objc2-metal = { version = "0.3.2", optional = true, default-features = false, features = [
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"std", "MTLAllocation", "MTLBuffer", "MTLCommandBuffer", "MTLCommandEncoder", "MTLCommandQueue", "MTLComputeCommandEncoder",
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"MTLComputePipeline", "MTLDevice", "MTLLibrary", "MTLPipeline", "MTLResource", "MTLTypes",
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] }
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[target.'cfg(all(target_arch = "wasm32", target_os = "unknown"))'.dependencies]
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wgpu = { version = "30.0.1", optional = true, default-features = false, features = ["std", "wgsl", "webgpu"] }
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[features]
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default = ["quantum", "quantum_toolchain", "quantum_receipt"]
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# Internal: signature support, pulled in by every sealing feature.
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# (Mirrors the `world` feature in the parent `wai` crate.)
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_signing = ["dep:ed25519-dalek"]
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# --- core simulation -------------------------------------------------------
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quantum = []
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quantum_toolchain = ["quantum"]
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quantum_mps = ["quantum"]
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quantum_stabilizer = ["quantum"]
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quantum_tableau = []
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# Exact amplitudes, probabilities and samples of a Clifford circuit measured in a
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# product basis, at a cost set by its cut-rank (src/quantum_cutrank.rs).
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quantum_cutrank = ["quantum_tableau", "quantum_sv"]
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# OpenQASM 3 (and the OpenQASM 2 forms in common use) read to one program and
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# lowered to each method's input (src/qasm.rs).
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quantum_qasm = ["quantum", "quantum_sv", "quantum_spd", "quantum_tableau"]
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quantum_mpsim = ["quantum_sv", "quantum_mps"]
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# The job service's engine: a request planned onto a method, run, and answered
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# as the service's JSON, for every host that runs jobs (src/engine/). Numbers
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# are read correctly rounded, so a result a client sends back reads as the
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# double it was.
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quantum_engine = ["quantum_qasm", "quantum_tn", "quantum_cutrank", "quantum_mpsim", "quantum_noisy", "dep:serde", "serde_json/float_roundtrip"]
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quantum_pauli = ["quantum"]
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# Sparse Pauli dynamics at utility scale: arbitrary-angle Pauli rotations on up
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# to 1024 qubits, reproducible bit for bit, with a rigorous truncation bound.
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# Independent of the dyadic core; `from_dyadic` appears when `quantum` is on.
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quantum_spd = []
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# Signed, energy-accounted receipts for it, re-checkable bit for bit anywhere.
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quantum_spd_receipt = ["quantum_spd", "_signing"]
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# Tensor-network states on the hardware graph: simple-update evolution, belief-
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# propagation environments; exact on trees, an independent check on loopy graphs.
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quantum_bptn = ["quantum_spd"]
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# Fault-tolerant resource estimation: logical layout, code distance, exhaustive
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# T-factory search and the space-time frontier, reproducible bit for bit.
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quantum_resource = []
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# Signed, energy-accounted receipts for it, re-checkable bit for bit anywhere.
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quantum_resource_receipt = ["quantum_resource", "_signing"]
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# Noisy Clifford circuits at QEC scale: bit-packed Pauli-frame sampling, detector
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# error models by backward sensitivity, and exact minimum-weight matching.
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quantum_frame = []
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# Tensor-network contraction: hyper-optimised contraction trees, slicing, and
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# amplitudes of circuits far past the state vector, reproducible bit for bit.
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quantum_tn = []
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# Gaussian boson sampling, exactly: hafnians, torontonians, lossy Gaussian states,
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# pattern probabilities, exact samples and the marginals any sampler must match.
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quantum_gbs = []
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# The dense state vector at any angle: gate fusion, diagonal fast paths, threads,
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# the same bits everywhere.
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quantum_sv = []
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# Architecture-level estimation: yoked storage, magic-state cultivation, CCZ
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# factories and reaction-limited time, assembled into whole-machine estimates.
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quantum_arch = []
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# qLDPC memory at circuit level: bivariate-bicycle codes, their depth-8 syndrome
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# cycle, and BP-OSD decoding of the circuit's error model.
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quantum_ldpc = ["quantum_frame"]
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# Quantum-inspired optimisation: simulated bifurcation (ballistic, discrete,
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# heated), simulated annealing and an exact referee, on Ising and Max-Cut.
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quantum_qio = []
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# Union-find decoding in near-linear time over the matching graph.
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quantum_uf = ["quantum_frame"]
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# Noisy circuits: quantum trajectories on the state vector, an exact density-
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# matrix referee, and cross-entropy fidelity against the digital error model.
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quantum_noisy = ["quantum_sv"]
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# Ground states of spin chains by two-site DMRG, held to exact diagonalisation
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# and to the free-fermion solution of the transverse-field Ising chain.
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quantum_dmrg = []
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# Time evolution of spin chains by the time-dependent variational principle,
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# held to exact evolution and to the free-fermion solution of a quench.
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quantum_tdvp = ["quantum_dmrg"]
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# Molecules by DMRG: FCIDUMP integrals, the second-quantised Hamiltonian as an
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# exact MPO, and full configuration interaction as the referee.
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quantum_chem = ["quantum_dmrg"]
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# Neural quantum states: a restricted Boltzmann machine trained by variational
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# Monte Carlo with stochastic reconfiguration, held to exact diagonalisation
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# and DMRG on chains and square lattices.
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quantum_nqs = ["quantum_dmrg"]
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# Mixed states and the observables that distinguish quantum theory from a classical
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# account: density matrices, the partial trace, exact Pauli expectations, CHSH.
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quantum_info = ["quantum"]
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# Qubits AND qutrits: the Weyl-Heisenberg group, the Fourier gate over Z_d, and
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# the controlled sum. Exact for d = 2 and 3; other dimensions are refused.
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quantum_qudit = ["quantum"]
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# Quantum communication and security: BB84 key distribution with an
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# intercept-resend eavesdropper, and the no-cloning theorem priced.
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quantum_comm = ["quantum"]
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# What quantum theory says about encoding and transport: von Neumann entropy, the
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# Schumacher limit, and the Holevo bound that refuses classical compression.
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quantum_source = ["quantum", "quantum_info"]
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# Transport itself: Kraus channels, and tomography to see what arrived.
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quantum_channel = ["quantum", "quantum_info", "quantum_source"]
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# QIR (LLVM-based quantum IR) export — the compiler-facing sibling of OpenQASM.
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quantum_qir = ["quantum", "quantum_toolchain"]
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# --- receipts & operations -------------------------------------------------
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quantum_receipt = ["quantum", "_signing"]
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quantum_ops = ["_signing"]
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quantum_cal = ["quantum_ops"]
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quantum_mitigate = ["quantum_ops"]
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quantum_qec = ["quantum_ops"]
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quantum_control = ["quantum_cal"]
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quantum_noise = ["quantum_cal"]
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quantum_compile = ["quantum_ops"]
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quantum_atom = ["quantum_ops"]
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quantum_qbom = ["quantum_ops"]
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# The fabric: members, parity against the CPU reference, certification,
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# placement by joules, and a signed receipt naming where a result ran. With the
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# fixed-point core in 32-bit lanes, the kernel an integer-only fabric runs.
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quantum_fabric = ["quantum", "quantum_ops", "quantum_toolchain"]
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# The WebGPU member: one WGSL kernel on Metal, Vulkan, DX12 or a browser's
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# WebGPU, certified bit-exact against the CPU reference on each adapter. Opt-in,
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# and not in `full`: it is the one feature that reaches a platform interface.
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quantum_fabric_webgpu = ["quantum_fabric", "dep:wgpu"]
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# The CUDA member of the fabric (an NVIDIA GPU, native targets only).
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quantum_fabric_cuda = ["quantum_fabric", "dep:cudarc"]
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# The HIP member of the fabric (a GPU the HIP runtime drives, Linux): the CUDA kernel's text, built by HIP's runtime compiler; both libraries loaded at run time.
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quantum_fabric_hip = ["quantum_fabric"]
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quantum_fabric_opencl = ["quantum_fabric", "dep:opencl3"]
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# The simulated FPGA member: Verilator, invoked at run time, compiles the datapath.
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quantum_fabric_fpga = ["quantum_fabric"]
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# The neural-processor member: exact fp16 limb products on the platform's neural processor.
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quantum_fabric_npu = ["quantum_fabric", "dep:objc2", "dep:block2", "dep:objc2-foundation", "dep:objc2-core-ml"]
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quantum_fabric_metal = ["quantum_fabric", "dep:objc2", "dep:objc2-foundation", "dep:objc2-metal"]
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# The dense state vector in single precision on the platform's GPU (macOS).
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quantum_sv_metal = ["quantum_sv", "dep:objc2", "dep:objc2-foundation", "dep:objc2-metal"]
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# The cut-rank table in single precision on the platform's GPU (macOS).
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quantum_cutrank_metal = ["quantum_cutrank", "dep:objc2", "dep:objc2-foundation", "dep:objc2-metal"]
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# The npu-cores member: the gate's arithmetic on a neural processor's compute core, programmed directly (Linux): the program built by the tile array's open toolchain, the host side compiled against the device runtime, both at run time.
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quantum_fabric_npu_cores = ["quantum_fabric"]
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# The tensor-engine member: the gate's products as exact bf16 limb convolutions on a tensor processor's matrix engine, through its kernel toolchain's Python run as a helper.
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quantum_fabric_tensor_engine = ["quantum_fabric"]
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# The matrix-extension member: the gate's arithmetic in the processor's streaming vector mode (SME).
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quantum_fabric_sme = ["quantum_fabric"]
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# The DSP vector-unit member: the gate's arithmetic in the signal processor's vector unit.
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quantum_fabric_dsp = ["quantum_fabric"]
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# The CPU vector-unit members: the gate's arithmetic in the processor's vector extension (avx2, sve, rvv).
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quantum_fabric_vector = ["quantum_fabric"]
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# The dataflow member: the gate's arithmetic on a dataflow accelerator's RISC-V cores, through a device library loaded at run time.
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quantum_fabric_dataflow = ["quantum_fabric"]
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# --- interference / ML layer ----------------------------------------------
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quantum_vml = []
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quantum_phasor = []
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quantum_qfhrr = ["quantum_vml"]
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quantum_kernel = []
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quantum_qdata = []
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quantum_phasor_meter = ["_signing"]
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# Everything.
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full = [
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"quantum", "quantum_toolchain", "quantum_mps", "quantum_stabilizer", "quantum_tableau", "quantum_cutrank", "quantum_qasm", "quantum_engine", "quantum_mpsim", "quantum_pauli", "quantum_spd", "quantum_spd_receipt", "quantum_bptn", "quantum_resource", "quantum_resource_receipt", "quantum_frame", "quantum_tn", "quantum_gbs", "quantum_sv", "quantum_arch", "quantum_ldpc", "quantum_qio", "quantum_uf", "quantum_noisy", "quantum_dmrg", "quantum_tdvp", "quantum_chem", "quantum_nqs", "quantum_qir", "quantum_info", "quantum_qudit", "quantum_comm", "quantum_source", "quantum_channel",
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"quantum_receipt", "quantum_ops", "quantum_cal", "quantum_mitigate", "quantum_qec",
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"quantum_control", "quantum_noise", "quantum_compile", "quantum_atom", "quantum_qbom", "quantum_fabric",
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"quantum_vml", "quantum_phasor", "quantum_qfhrr", "quantum_kernel", "quantum_qdata",
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"quantum_phasor_meter",
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]
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[[test]]
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name = "webgpu_member"
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required-features = ["quantum_fabric_webgpu"]
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[[test]]
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name = "cuda_member"
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required-features = ["quantum_fabric_cuda"]
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[[test]]
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name = "cuda_tensor"
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required-features = ["quantum_fabric_cuda"]
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[[test]]
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name = "metal_member"
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required-features = ["quantum_fabric_metal"]
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[[test]]
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name = "metal_matrix"
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required-features = ["quantum_fabric_metal"]
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[[test]]
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name = "metal_tensor_ops"
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required-features = ["quantum_fabric_metal"]
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[[test]]
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name = "cuda_sv"
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required-features = ["quantum_fabric_cuda", "quantum_sv"]
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[[test]]
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name = "hip_wmma"
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required-features = ["quantum_fabric_hip"]
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[[test]]
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name = "hip_member"
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required-features = ["quantum_fabric_hip"]
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[[test]]
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name = "hip_sv"
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required-features = ["quantum_fabric_hip", "quantum_sv"]
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[[test]]
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name = "npu_cores_member"
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required-features = ["quantum_fabric_npu_cores"]
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[[test]]
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name = "tensor_engine_member"
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required-features = ["quantum_fabric_tensor_engine"]
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[[test]]
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name = "opencl_member"
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required-features = ["quantum_fabric_opencl"]
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[[test]]
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name = "opencl_sv"
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required-features = ["quantum_fabric_opencl", "quantum_sv"]
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[[test]]
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name = "fpga_member"
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required-features = ["quantum_fabric_fpga"]
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[[test]]
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name = "npu_member"
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required-features = ["quantum_fabric_npu"]
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[[test]]
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name = "sme_member"
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required-features = ["quantum_fabric_sme"]
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[[test]]
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name = "dsp_member"
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required-features = ["quantum_fabric_dsp"]
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[[test]]
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name = "vector_member"
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required-features = ["quantum_fabric_vector"]
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[[test]]
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name = "dataflow_member"
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required-features = ["quantum_fabric_dataflow"]
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[[test]]
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name = "fabric_expected"
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required-features = ["quantum_fabric"]
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[[example]]
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name = "stablehlo_gate"
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required-features = ["quantum_fabric"]
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[[example]]
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name = "rv32_listing"
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required-features = ["quantum_fabric_dataflow"]
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[[example]]
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name = "tn_cost"
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required-features = ["quantum_tn"]
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[[example]]
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name = "patch_xeb"
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required-features = ["quantum_sv"]
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[[example]]
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name = "cutrank_qasm"
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required-features = ["quantum_cutrank"]
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[[example]]
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name = "cutrank_gpu"
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required-features = ["quantum_cutrank_metal"]
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[[example]]
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name = "npu_onnx"
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required-features = ["quantum_fabric"]
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[[example]]
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name = "qio_gset"
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required-features = ["quantum_qio"]
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[[example]]
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name = "tdvp_quench"
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required-features = ["quantum_tdvp"]
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[[example]]
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name = "nqs_ground"
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required-features = ["quantum_nqs"]
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[[example]]
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name = "chem_dmrg"
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required-features = ["quantum_chem"]
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[[example]]
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name = "dmrg_chain"
|
|
required-features = ["quantum_dmrg"]
|
|
|
|
[[example]]
|
|
name = "uf_bench"
|
|
required-features = ["quantum_uf"]
|
|
|
|
[[example]]
|
|
name = "ldpc_memory"
|
|
required-features = ["quantum_ldpc"]
|
|
|
|
[[example]]
|
|
name = "arch_factoring"
|
|
required-features = ["quantum_arch"]
|
|
|
|
[[example]]
|
|
name = "sv_run"
|
|
required-features = ["quantum_sv"]
|
|
|
|
[[example]]
|
|
name = "sv_bench"
|
|
required-features = ["quantum_sv"]
|
|
|
|
[[example]]
|
|
name = "noisy_bench"
|
|
required-features = ["quantum_noisy"]
|
|
|
|
[[example]]
|
|
name = "gbs_check"
|
|
required-features = ["quantum_gbs"]
|
|
|
|
[[example]]
|
|
name = "tn_path"
|
|
required-features = ["quantum_tn"]
|
|
|
|
[[example]]
|
|
name = "qec_threshold"
|
|
required-features = ["quantum_frame"]
|
|
|
|
[[example]]
|
|
name = "qec_frame"
|
|
required-features = ["quantum_frame"]
|
|
|
|
[[example]]
|
|
name = "utility"
|
|
required-features = ["quantum_spd"]
|
|
|
|
[[example]]
|
|
name = "utility_mz"
|
|
required-features = ["quantum_spd"]
|
|
|
|
[[example]]
|
|
name = "utility_bptn"
|
|
required-features = ["quantum_bptn"]
|
|
|
|
[[example]]
|
|
name = "bell"
|
|
required-features = ["quantum", "quantum_toolchain"]
|
|
|
|
[[example]]
|
|
name = "threshold"
|
|
required-features = ["quantum_qec"]
|
|
|
|
[[example]]
|
|
name = "receipt"
|
|
required-features = ["quantum_toolchain", "quantum_receipt"]
|