Helpful quantum computer systems would require fault tolerant logical operations. Researchers are actively exploring many various quantum error correction (QEC) codes to allow this, enhancing the Logical Error Charges (LER) of Quantum Processing Items (QPUs). Whereas it’s effectively understood the way to run logical operations with floor codes (which belong to the topological code household) by way of lattice surgical procedure, they’re qubit environment friendly for reminiscence however suboptimal relative to different topological codes for performing fault-tolerant logical computation.
Quantum low-density parity-check (QLDPC) codes are one other sort of QEC scheme that require the least bodily qubits for reminiscence resulting from their fixed charges. Nonetheless, it’s nonetheless unclear how each Clifford and non-Clifford gates may be carried out effectively on the logical degree with QLDPC codes.
Shade Codes are one other sort of topological code which can be effectively understood. Though they require extra bodily qubits for reminiscence than floor codes to realize the identical goal logical failure charge, they’ll carry out logical gates extra effectively than floor codes resulting from their skill to do all Clifford gates transversally and the symmetry of their illustration leading to easier lattice surgical procedure operations. With quick and correct decoders, it’s doubtless that parameter regimes exist the place logic with colour codes could possibly be extra environment friendly than logic with floor codes.
On condition that decoding colour codes is way tougher than decoding floor codes, colour codes have traditionally been shelved resulting from a scarcity of quick and correct decoders, which allow customers to leverage these codes in actual time.
NVIDIA Ising Decoder ColorCode 1 Quick is designed to speed up and enhance the LER of colour code decoders, enabling greater than 347.7x higher LER, and seven.3x quicker runtime in contrast with the state-of-the-art colour code decoder Chromobius for d=31 and bodily error charge of 0.3%. Such outcomes convey colour codes again into focus for potential use in constructing and working actually helpful quantum computer systems.
The next video demonstrates the way to practice the Ising Decoder ColorCode 1:
Ising Decoding
Utilizing the Ising Decoding coaching pipeline, QPU builders, operators, and decoder builders can practice small 3D Convolutional Neural Community (CNN) AI-based pre-decoders designed for triangular Shade Codes. Actual-time operations that scale in each area and time assist enhance latency and LERs. These pre-decoders speed up and enhance decoder accuracy by dealing with a big amount of localized error syndromes. They’ll additionally scale to arbitrary code distances, serving to groups deploy quantum error correction decoders that may scale with their QPUs all the way in which to lattice surgical procedure.
Additional, since they predict full space-time corrections and are native (and thus impartial of the enter dimension and geometry), they can be utilized to carry out lattice surgical procedure in a parallel space-time blockwise decoding structure, which is important for decoding errors in actual time throughout the execution of a quantum algorithm.
Customers want solely outline their noise mannequin, the triangular colour code distance, and mannequin depth (deeper is extra correct). The coaching structure then makes use of the NVIDIA cuStabilizer library inside NVIDIA cuQuantum and NVIDIA PyTorch to generate artificial coaching information and practice a 3D CNN that optimizes decoding efficiency for the duty. Customers can even go for extra or fewer layers, which creates a runtime and accuracy trade-off. The perfect CNN mannequin is dependent upon the code distance, bodily error charge, international decoder effectiveness, and round-trip latency funds.
This may be evaluated in roughly 20 traces of python code outlined under.
1. Construct a distance-5 color-code reminiscence experiment: superdense syndrome extraction, Si1000 circuit-level noise at p = 0.3%
distance=5,
n_rounds=5,
foundation=”X”,
p_error=1e-3,
noise_model_family=”si1000″,
noise_instruction_semantics=”reference”,
).stim_circuit
2. Compile a Chromobius decoder from the circuit’s detector error mannequin
approximate_disjoint_errors=True,
ignore_decomposition_failures=True,
)
decoder = chromobius.compile_decoder_for_dem(dem)
3. Pattern noisy syndromes and decode
100_000, separate_observables=True
)
packed = np.packbits(dets.astype(np.uint8), axis=1, bitorder=”little”)
predictions = decoder.predict_obs_flips_from_dets_bit_packed(packed)
pred = np.unpackbits(predictions, axis=1, bitorder=”little”)[:, : circuit.num_observables]
4. Logical error charge
print(f”logical error charge: {ler:.2e} ({ler / 5:.2e} per spherical)”)
Operating this prints an LER round 3.6e-03 (7.3e-04 per spherical). From right here you possibly can sweep distances and error charges to hint out the edge curve code/scripts/color_code_threshold_chromobius.py does the total sweep in a single command, or drop the Ising-Decoder-ColorCode-1 pre-decoder in entrance of Chromobius to sparsify syndromes earlier than the ultimate decode.
Accuracy or velocity: Deciding on the appropriate mannequin
Ising Decoder ColorCode 1 Quick has 17 layers, a receptive discipline of 13, and is educated on enter volumes of dimension 13x13x19 (though arbitrary enter volumes can be utilized throughout inference). This mannequin has roughly 2,900,000 parameters. Because of its small dimension, it runs effectively on a GPU however supplies much less enchancment to the LER in comparison with a bigger mannequin.
Determine 1, under, reveals the worth Ising Decoder ColorCode 1 can add to a standard open supply colour code decoder like Chromobius. It reveals the best regime for deploying the pre-decoder mannequin given some bodily error charge and code distance.
Be taught extra about NVIDIA Ising Decoder ColorCode mannequin structure and discover a variety of study and outcomes.
Begin constructing with NVIDIA Ising open assets
The NVIDIA Ising mannequin household is totally open. Weights, coaching architectures, information, benchmarks, and recipes are offered to allow others to switch, deploy, practice, and fine-tune their very own fashions and variants for his or her particular QPUs.
Coaching Recipes
NVIDIA is releasing a whole coaching pipeline for Ising Decoder ColorCode 1 Quick that permits customers to generate artificial information with the NVIDIA cuQuantum library, NVIDIA cuStabilizer on the fly, whereas coaching with PyTorch. This structure permits builders to provide decoder fashions tailor-made to their particular QPU noise traits.
Get began
NVIDIA Ising is accessible with the next assets for getting began:
