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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)

Quanta SDK can be used as an MCP (Model Context Protocol) server, allowing AI assistants like Claude to perform quantum simulations.

Tool Description
run_circuit Execute arbitrary quantum circuits
create_bell_state Quick entanglement demonstration
grover_search Grover's search algorithm
shor_factor Shor's integer factoring
simulate_noise Noisy circuit simulation (7 channels)
list_gates Available gate reference
explain_result Interpret measurement results 
# Local (Claude Desktop)
fastmcp install quanta/mcp_server.py --name "Quanta Quantum SDK"

# Remote (Cloud Run)
python -m quanta.mcp_server --transport sse --port 8080

Deployment

Target Method Use Case
Local pip install quanta-sdk Development, research
Claude Desktop fastmcp install AI-assisted simulation
Cloud Run Dockerfile.mcp + CI/CD Always-on MCP server
Lambda/Functions Lightweight package Serverless computation
CI/CD Pipeline pip install quanta-sdk Automated QC testing

Lightweight advantage: Pure Python + NumPy only. No heavy framework dependencies. Ideal for serverless, edge computing, and embedding in CI/CD pipelines.