Skip to content

Quanta SDK — Features

Gate Set (31 Gates)

Gate Qubits Description
H 1 Hadamard — creates superposition
X 1 Pauli-X — bit flip (NOT)
Y 1 Pauli-Y — bit + phase flip
Z 1 Pauli-Z — phase flip
S 1 S gate — π/2 phase
T 1 T gate — π/4 phase
CX 2 CNOT — controlled NOT
CZ 2 Controlled-Z — controlled phase
CY 2 Controlled-Y
SWAP 2 Qubit exchange
CCX 3 Toffoli — double controlled NOT
RX(θ) 1 X-axis rotation
RY(θ) 1 Y-axis rotation
RZ(θ) 1 Z-axis rotation
P(θ) 1 Phase gate
U(θ,φ,λ) 1 General single-qubit unitary
I 1 Identity
SDG 1 S-dagger (−π/2 phase)
TDG 1 T-dagger (−π/4 phase)
SX 1 Square root of X
SXdg 1 SX-dagger
RXX(θ) 2 XX rotation (2-qubit)
RZZ(θ) 2 ZZ rotation (2-qubit)
RCCX 3 Relative-phase CCX
RC3X 4 Relative-phase C3X
ECR 2 Echoed cross-resonance (IBM Heron native)
iSWAP 2 Imaginary SWAP (Google Sycamore native)
CSWAP 3 Controlled-SWAP (Fredkin)
CH 2 Controlled-Hadamard
CP(θ) 2 Controlled-Phase
MS(θ) 2 Mølmer-Sørensen (IonQ trapped-ion native)

Custom Gates

from quanta import custom_gate
import numpy as np

# Define a custom √X gate
custom_gate("SqrtX", np.array([[0.5+0.5j, 0.5-0.5j],
                                [0.5-0.5j, 0.5+0.5j]]))

Broadcast Support

H(q)        # Apply H to all qubits
H(q[0])     # Apply only to q[0]
CX(q[0], q[1])  # Two-qubit gate

Compiler Optimizations

Pass What It Does Example
CancelInverses Cancels inverse gates H·H → (empty), X·X → (empty)
MergeRotations Merges rotations RZ(π/4)·RZ(π/4) → RZ(π/2)
TranslateToTarget Converts to target hardware gate set SWAP → 3×CX

Qubit Routing

Topology-aware SWAP insertion for hardware constraints:

Topology Use Case
Linear Ion trap, superconducting chains
Ring Circular connectivity
Grid 2D superconducting (IBM, Google)

Supported Hardware Gate Sets

Hardware Gate Set
IBM Heron {CX, RZ, SX, X}
Google Sycamore {CZ, RZ, RX, RY}
Quantinuum H-Series {CX, RZ, RY, RX}

Simulators

Simulator Max Qubits Features
Statevector 27 Tensor contraction, O(2^n)
Pauli Frame 50 Stabilizer tableau (Aaronson-Gottesman), O(n) per gate
Density Matrix 13 Mixed states, Kraus channels
Accelerated 27 Auto-detects JAX-GPU / CuPy

Noise Integration

Noise is a first-class citizen in the execution pipeline:

from quanta import run
from quanta.simulator.noise import NoiseModel, Depolarizing

result = run(bell, shots=1024, noise=NoiseModel().add(Depolarizing(0.01)))

Noise Models

Channel Description Parameter Hardware Ref
Depolarizing Random Pauli error p ∈ [0,1] —
BitFlip 0⟩↔ 1⟩ flip
PhaseFlip Phase error (Z) p ∈ [0,1] —
AmplitudeDamping Energy loss (T1 decay) γ ∈ [0,1] IBM: 100-300μs
T2Relaxation Pure dephasing (T2 decay) γ ∈ [0,1] IBM: 100-200μs
Crosstalk ZZ coupling between neighbors p ∈ [0,1] ~0.1-1% / gate
ReadoutError Measurement bit-flip p01, p10 IBM: 0.5-2%

Error Correction Codes

Code Notation Correctable Errors
BitFlip [[3,1,3]] 1 bit-flip
PhaseFlip [[3,1,3]] 1 phase-flip
Steane [[7,1,3]] 1 arbitrary single-qubit error
Surface Code [[d²,1,d]] ⌊(d-1)/2⌋ errors, stabilizer syndrome extraction
Color Code [[n,1,d]] Transversal Clifford gates, restriction decoder

QEC Decoders

Decoder Complexity Description
MWPM O(n³) Greedy minimum weight perfect matching
Union-Find O(n·α(n)) Near-linear cluster-based decoding

Algorithms (Layer 3)

Algorithm Function Description
Grover search() Unstructured search with quadratic speedup
QAOA optimize() Combinatorial optimization
VQE vqe() Variational eigensolver for molecular energy
Shor factor() Integer factoring via period finding
QSVM qsvm_classify() Quantum kernel SVM classification
Portfolio portfolio_optimize() Financial portfolio optimization
Hamiltonian evolve() Trotterized time evolution
Entity Resolution resolve() QAOA-based customer deduplication
Multi-Agent MultiAgentSystem Quantum decision modeling
Monte Carlo monte_carlo_price() Amplitude estimation for option pricing
Clustering cluster_data() Swap-test quantum distance + k-means
QML Classifier QuantumClassifier Variational quantum classification

QASM Support

Direction Version Description
Export QASM 3.0 Circuit → OpenQASM string
Import QASM 2.0/3.0 OpenQASM string → DAG

Benchmark Infrastructure

Tool Description
QASMBench 10 standard + 3 large (20-24 qubit) circuits
Benchpress Adapter Cross-SDK benchmarking API
Turnusol Test 8-test quality litmus test

Parameter Sweep

from quanta import sweep

results = sweep(my_circuit, params={"theta": [0, 0.5, 1.0, 1.5]})
for r in results:
    print(r.summary())

Visualization

  • Probability histogram: print(result)
  • Dirac notation: result.dirac_notation()
  • Statevector display: show_statevector(sv, n)

MCP Server (AI Integration — 23 Tools)

Quanta SDK includes 23 MCP (Model Context Protocol) tools for both local and cloud agents. Assistants like Claude, Gemini, and GPT can compose quantum circuits, estimate fault-tolerant costs, evaluate noise, and solve optimization problems autonomously.

Category Tools
Circuit Operations run_circuit, create_bell_state, list_gates, inspect_circuit
Quantum Algorithms grover_search, shor_factor, vqe_ground_state, quantum_clustering
Fault Tolerance & Noise simulate_noise (7 channels), estimate_fault_tolerant_cost (Google Willow / IBM Starling)
Optimization & Math qubo_solve, portfolio_optimization, entity_resolution_match

PyTorch & Biomorphic Quantum Engine (v1.1.0 — quanta.torch)

1. Differentiable QuantumLayer

  • Full torch.nn.Module integration.
  • Analytical Parameter-Shift Rule for exact vector-Jacobian products (VJPs) and autograd gradients.
  • Hardware-efficient parameterized ansatz templates: hardware_efficient, strong_entangling, reuploading, real_amplitudes.
  • Full device portability across CPU, CUDA, and Apple Silicon MPS (Metal).

2. Continuous Quantum Resonance (ContinuousResonator)

  • Continuous-time Hamiltonian evolution: \(U(t) = e^{-i H(x, \theta) t}\).
  • Daleckii-Krein Fréchet matrix exponential derivatives and Ehrenfest theorem time derivatives.
  • Lindblad phase-damping dissipators for open quantum neural systems.

3. Biomorphic Resonant Brain (BiomorphicResonantBrain)

  • Dual-Hemisphere Architecture: Resonance coupling between Left (analytical/logical) and Right (intuitive/pattern) hemispheres.
  • 4-Neuromodulator Dynamics:
  • Dopamine (\(DA\)): Value weighting and reward-driven plasticity.
  • Acetylcholine (\(ACh\)): Attention gating and learning rate scaling.
  • Serotonin (\(5\text{-}HT\)): Impulsivity dampening and patience regulation.
  • Norepinephrine (\(NE\)): Arousal and novelty-triggered exploratory dynamics.
  • Cerebral Oxygenation (\(sO_2\)): Metabolic budget and energy-constrained computation.
  • REM Sleep Continual Learning: Active memory consolidation preventing catastrophic forgetting.
  • NoisyHippocampalBuffer: Lindblad phase diffusion and Sharp-Wave Ripple (SWR) replay buffer.
  • DialecticalSynthesizer: Quantum Thesis-Antithesis conflict resolution engine.
  • CSFBiophysicalShield: Biophysical quantum phase shielding suppressing environmental decoherence.

Apple Silicon Metal / MLX Acceleration (v1.0.0)

  • 404x Speedup: Up to 404x faster execution on 26-qubit circuits compared to CPU on Apple M-series chips.
  • Unified Memory Utilization: 30+ qubit dense statevector simulations on 48 GB unified RAM.
  • Automatic Routing: Highest-priority backend selection on macOS ARM64 architectures.

Multi-Cloud Quantum Hardware Validation

  • IonQ Cloud: REST API v0.3 protocol, 29-qubit hardware emulation, and remote telemetry.
  • IBM Quantum: 156-qubit Heron r3 processors (ibm_torino, ibm_fez), native ISA transpilation, and IAM authentication.
  • Google Cirq: Sycamore native gate decomposition and Colab GPU environment support.

Deployment Options

Target Method Use Case
Local pip install quanta-sdk Fast development & testing
PyTorch / AI pip install "quanta-sdk[torch]" Deep learning & hybrid QNNs
Apple Metal pip install "quanta-sdk[metal]" Apple Silicon GPU acceleration
Claude / Gemini MCP Server Integration Autonomous AI workflows
Cloud Run / Docker Dockerfile.mcp + SSE Always-on remote quantum microservice