Amazon Braket: Cloud Quantum Computing

• Amazon Braket is a cloud-based quantum computing service that provides access to diverse hardware including trapped-ion, superconducting, and neutral-atom QPUs.
• It features a unified SDK that enables researchers to construct, transpile, execute, and benchmark quantum algorithms across heterogeneous platforms with distinct error models and connectivity.
• The platform allows rigorous performance evaluation through metrics like state fidelity and supports analog simulation via QuEra Aquila for large-scale many-body experiments.

Amazon Braket is a cloud-based quantum computing service providing programmatic access to a spectrum of quantum backends, including trapped-ion, superconducting, and neutral-atom quantum processors as well as high-performance classical simulators. Through a unified software development kit (SDK), researchers can construct, transpile, execute, and benchmark quantum algorithms on heterogeneous hardware platforms, each with distinct connectivity graphs, error models, and physical constraints. Braket additionally exposes QuEra’s 256-qubit neutral-atom analog quantum simulator (Aquila), allowing specification of programmable Hamiltonian evolution for large-scale quantum many-body and optimization experiments. The platform supports rigorous evaluation of algorithmic performance through quantitative metrics such as state fidelity, CHSH violation, and resource counts, enabling detailed studies of the influence of device topology and noise in the NISQ regime (Oralkhan et al., 7 Jan 2026, Wurtz et al., 2023).

1. Quantum Hardware Backends and Architectures

Amazon Braket provides direct access to a diverse set of quantum processing units (QPUs), each exemplifying a distinct hardware modality:

• Trapped-Ion QPUs (IonQ Aria-1, IonQ Forte-1): Feature all-to-all qubit connectivity (complete graph), coherence times $T_1$, $T_2$ in the 10–100 ms range, and hardware-native gates comprising arbitrary single-qubit rotations $R(\theta,\phi)$ and Molmer–Sørensen $XX(\chi)$ entangling gates.
• Superconducting QPUs: Encompass IQM Garnet (20 qubits, 1D/2D nearest-neighbor topology, $T_1,T_2\sim 20$–80 μs, native $CZ$ or $\sqrt{iSWAP}$ gates) and Rigetti Ankaa-3 (84 qubits, 2D grid with tunable couplers, $T_1,T_2\sim 20$–50 μs, $CZ$ and single-qubit $R_x/R_z$).
• State-Vector Simulator (SV1): Noise-free ideal simulation up to 34 qubits, supplying a performance reference.
• QuEra Aquila Neutral-Atom QPU: A 256-qubit field-programmable qubit array (FPQA) operated as a user-configurable analog Hamiltonian simulator. Trapping, coherent drive, and measurement are enabled via an ultra-high vacuum platform with optical tweezers and programmable laser control (Wurtz et al., 2023).

This hardware heterogeneity introduces a testbed for precisely studying architectural factors such as qubit connectivity, native gate set, and coherence on algorithmic performance (Oralkhan et al., 7 Jan 2026).

2. Software Workflow and Transpilation Methodology

Programming on Braket proceeds via cross-framework circuit specification (PennyLane, Qiskit-style QASM) and device selection in the Braket SDK. The platform executes device-specific transpilation to map logical quantum circuits to hardware-native operations:

• Gate Decomposition: All logical gates are decomposed into the device’s supported gate set.
• Qubit Mapping and Routing: Logical-to-physical qubit assignments are chosen to minimize routing cost subject to hardware connectivity. Non-adjacent two-qubit gates require insertion of SWAPs: for a logical CNOT at graph distance $d$, approximately $3(d-1)$ native gates are incurred.
• Circuit Depth Inflation: Each SWAP inflates the physical circuit depth, with the total hardware depth scaling as $${\rm Depth}_{\rm phys} \approx {\rm Depth}_{\rm logical} + 2 \sum_{{\rm CNOT}} (d(i,j)-1)$$.

The routing policy uses shortest-path heuristics. Error accumulation from routing is modeled by

$$\varepsilon_{\rm swap\_total} \simeq 1-(1-\varepsilon_2)^{3(d-1)}$$

for two-qubit gate error rate $\varepsilon_2$ (Oralkhan et al., 7 Jan 2026).

For analog devices like Aquila, the programming model directly specifies time-dependent Hamiltonian parameters and atomic geometry via the Braket SDK's AnalogHamiltonian class and associated parameter structures (Wurtz et al., 2023).

3. Algorithm Benchmarking and Performance Evaluation

Amazon Braket supports quantitative benchmarking using a standardized set of quantum algorithms and performance metrics. These include:

• State Fidelity: $F = {\rm Tr}(\rho_{\rm ideal}\,\rho_{\rm exp})$.
• CHSH Bell Violation: $S = E(a,b)+E(a,b')+E(a',b)-E(a',b')$, with classical limit $S \leq 2$ and quantum bound $S \leq 2\sqrt{2}$.
• Success Probability (Grover): $$P_{\rm success}(k) = \big|\langle w | G^k | \psi\rangle\big|^2$$.
• Circuit Depth and Gate Counts: Enumerated post-transpilation.

Key findings from benchmark studies (Oralkhan et al., 7 Jan 2026):

• Bell States: Trapped-ion and neutral-atom backends achieve near-ideal $S$, with IonQ Aria-1 measured at $S_{\rm exp}=2.90\pm0.14$, while superconducting platforms show greater deviation due to gate error and depth inflation.
• GHZ States ($n=6, 10$): Trapped-ion QPUs maintain high fidelity ($F_{\rm exp}=0.955$ for $n=6$, $0.835$ for $n=10$), while superconducting QPUs' fidelity degrades sharply ($\lesssim0.12$ for $n=10$) as routing overhead accumulates.
• Quantum Fourier Transform: Fidelity on IonQ Aria-1 remains robust even at $n=10$ ($F_{\rm exp}=1.000$), matching the simulator, in contrast to significantly lower fidelity on Rigetti and IQM platforms for the same $n$ due to limited connectivity and increased depth.
• Grover's Search: Small problems ($n=4$) run effectively on all hardware, but success probability and iteration-scaling are more predictable and higher on trapped-ion and simulator platforms.
• QAOA: For $p=1$ and $n=10$, IonQ Forte-1 achieves higher approximation ratio and feasibility rates than superconducting backends.

These results highlight the pronounced effect of device topology and noise; with all-to-all connectivity and long coherence, trapped-ion QPUs excel at depth-intensive, non-local circuits, while superconducting QPUs are optimal only for shallow and locally connected circuits.

4. Neutral-Atom Analog Simulation with QuEra Aquila

Aquila, accessible through Braket, is a 256-qubit neutral-atom FPQA utilizing $^{87}$Rb atoms and programmable Rydberg interactions (Wurtz et al., 2023). Unlike gate-model QPUs, Aquila implements analog quantum simulation governed by a time-dependent control Hamiltonian:

$$H(t) = \sum_i \left[\frac12 \Omega(t) \left(e^{+i\varphi(t)}|g_i\rangle\langle r_i| + e^{-i\varphi(t)}|r_i\rangle\langle g_i| \right) - \Delta(t) n_i\right] + \sum_{i<j} V_{ij} n_i n_j$$

where $\Omega(t)$, $\varphi(t)$, and $\Delta(t)$ are user-controlled Rabi frequency, phase, and detuning, and $V_{ij} = C_6/|x_i-x_j|^6$ encodes tunable van der Waals interactions.

Programming involves: device selection, specification of time grid and controls, atom placement geometry, job submission (run), and retrieval of measurement sets and diagnostic images. Notable device-level benchmarks include Ramsey $T_2 \approx 5.8~\mu$s, $256$-qubit array size (4 μm spacing), and average shot rates up to $\sim10$ per second. Dominant errors arise from laser phase/amplitude fluctuations, atom position jitter, and imperfect fluorescence readout.

Aquila enables diverse analog protocols, including:

• Single- and Few-Qubit Dynamics: Rabi, Ramsey, and spin-echo protocols.
• Blockaded Many-Body States: Z$_2$ ordering, checkerboard, and striated 2D patterns.
• Combinatorial Optimization: Maximum independent set algorithms on unit-disk graphs, with post-processing for constraint enforcement. For a 16x16 King’s graph with 30% dropout, a hybrid quantum-classical protocol can yield true MIS solutions in $\sim$4.5% of runs, though purely classical greedy algorithms can closely approach the same benchmarks.
• Quantum Many-Body Scar Dynamics: Persistent revivals in Neél overlap seen in up to 13-site chains.

5. Influence of Device Topology, Noise, and Routing on Algorithm Performance

Architecture imposes fundamental limits on transpilation overhead and noise accumulation. For superconducting QPUs with lattice topology, non-nearest-neighbor gates require SWAP-based routing with overhead scaling as $3(d-1)$ native gates per logical two-qubit operation at distance $d$. The accumulated error per routed two-qubit gate is given by

$$\varepsilon_{\rm route}\approx 1-(1-\varepsilon_{\rm CNOT})^{3(d-1)}.$$

In contrast, trapped-ion and neutral-atom systems (with all-to-all or dynamically reconfigurable connectivity) eliminate routing overhead ($d=1$), directly translating to superior performance for circuits with highly non-local entanglement structure, such as global GHZ or QFT. Superconducting devices retain an advantage only for shallow local circuits with minimal inter-qubit distance (Oralkhan et al., 7 Jan 2026).

6. Best Practices for Backend Selection and Experimental Protocols

Optimal backend selection is dictated by circuit structure and resource requirements:

• Short-Depth, Low-Qubit Circuits: Any backend suffices; trapped-ion QPUs yield highest fidelities.
• Medium–High Depth with Non-Local Entanglement (QFT, Large GHZ, QAOA): Trapped-ion or neutral-atom (Aquila) platforms are preferred.
• Analog Hamiltonian Simulation and Combinatorial Optimization: Aquila enables programmable many-body dynamics at up to 256 qubits, within specified geometry and pulse/evolution constraints (Wurtz et al., 2023).
• Pre-tuning and Algorithm Verification: SV1 simulator provides a noise-free reference and supports parameter sweeps before hardware execution.

For Aquila, shot parallelization on duplicate blocks, post-selection on filled arrays (using pre-sequence imaging), and protocol design favoring adiabatic evolution all enhance robustness. Protocols should minimize atom placement near exact blockade and optimize $\Omega$ to reduce total runtime relative to decoherence rates.

7. Summary and Research Outlook

Amazon Braket synthesizes programmable access to diverse quantum hardware, unified transpilation and compilation methodology, and device-agnostic benchmarking paradigms. It enables systematic investigation of how connectivity, native gate set, and noise sources manifest in the performance of canonical and emerging quantum algorithms across hardware modalities. The integration of state-of-the-art neutral-atom analog quantum simulation (Aquila) within Braket expands the accessible regime for quantum dynamics, many-body experiments, and optimization beyond the reach of near-term gate-based quantum devices. These capabilities support the design of architecture-aware algorithms, enable cross-platform verification, and provide a foundation for empirical analysis of quantum advantage as devices scale and mature (Oralkhan et al., 7 Jan 2026, Wurtz et al., 2023).