Libra
QuEra's first useful, fault tolerant quantum computer
256 logical qubits, Megaquop logical performance
Arriving in 2028, Libra will be Amazon Braket's first fault tolerant quantum computer. With any-to-any connectivity, room temperature operation, and high performance QEC built in, Libra redefines what is possible.
Early Access & Availability
Arriving in 2028 on Amazon Braket
The first fault tolerant quantum computer on Amazon Braket, Libra will be available to select AWS customers in an environment they know and trust. With hybrid workflows and on-demand HPC, Amazon Braket supports production-ready deployments.
FTQC Founders Circle
Direct access to Libra will initially be limited to the founding cohort of the FTQC Founders Circle. Founders Circle members have exclusive access and input into the development of Libra product features and will be day-one ready with co-developed applications optimized for Libra.
Industry-Leading Logical Capability
→ 256 logical qubits
→ 10-6 logical error rate
→ Megaquop performance
→ >10,000 physical qubits
Efficient Time to Solution
→ Compatible with high-encoding-rate QEC codes enabled by any-to-any connectivity
→ Hundreds of parallel operations
→ Transversal logical operations
Reconfigurable Architecture
→ Multi-zone architecture
→ Qubit loss detection & continuous qubit loading
Deployable in Existing HPC Infrastructure
→ Room temperature operation
→ <40 kW power consumption
→ <1,000 sq ft, rack-mounted form factor
Explore Libra’s Fault Tolerant Architecture
Drawing on previously demonstrated research, Libra combines all the building blocks required for continuous, flexible, logical quantum operation.
1. Physical Qubits
Libra will leverage the same zoned architecture available in QuEra’s current, commercially available system. Without the need for networking, a single node will scale to many thousands of physical qubits.
2. Logical Encoding
Libra uses high-rate codes to encode up to 256 logical qubits at a 10-6 logical error rate, making it QuEra’s first Megaquop system capable of a million error-free quantum operations. Customers access this logical layer to run commercially and scientifically relevant applications.
3. Logical Operation
Libra’s massive parallelism enables parallel shuttling and transversal operations, even at the logical layer. Critically, this reduces QEC time, a key factor in whether applications can run practically on Megaquop systems.
4. Hybrid Decoding
Libra’s QEC architecture depends on frequent syndrome measurements, which collect ancillary data about the system’s state. Classical machine learning uses this data to determine which errors occurred so they can be corrected. Libra’s massive parallelism makes syndrome extraction highly efficient, addressing both latency and QEC time challenges.
5. Atom Loss Detection & Reloading
Syndrome data also reveals atom loss. Fresh qubits are then loaded mid-circuit from Libra’s reloading reservoir, enabling the continuous operation that deep circuits require.
6. Logical Teleportation
After rounds of syndrome extraction and qubit reloading, the logical information is teleported to a new, abstracted layer. This removes entropy from Libra’s logical layer and lets the error-corrected logical information feed forward throughout the circuit.
7. QEC & Continuous Reloading Cycle Repeats
These steps (logical operation, syndrome extraction, and atom loss detection and reloading) repeat until the circuit is fully executed.
8. Post-Processing & Application Completion
At the end of the circuit, Libra measures the logical qubits, collapsing the quantum information into classical data. This data is then post-processed in Libra’s quantum error correction engine to produce the final, error-corrected result.
Example Applications Within Reach
Our upcoming systems are designed to enable enterprise, government and academic organizations to execute quantum-advantage-capable, commercially relevant applications.
1. Materials Science
The earliest fault tolerant systems may deliver their first commercial signal in materials science. Strongly correlated many-body systems of sufficient scale to make accurate predictions of material properties sit beyond classical reach but inside a quantum computer's natural operating regime. They are built out of interactions on lattices, and their quantum computational resource cost grows gently with system size. Cracking them opens both scientifically and commercially relevant applications.
High-temperature superconductor design
Battery materials discovery
Magnetic materials and spintronics
Novel phases of matter exploration
Quantum chemistry
→ Spin-lattice Hamiltonians
Heisenberg, XY, Ising, and related lattice models for quantum magnetism and frustrated systems. Foundational benchmarks where classical methods (DMRG, QMC) hit walls.→ Single-band Fermi-Hubbard
The canonical model of interacting electrons on a square L×L lattice, with on-site repulsion U and nearest-neighbor hopping t. Solving for lattice sizes up to 10×10 is within capability bounds.
→ Single-orbital cuprate model
Adds second- and third-nearest-neighbor hopping to Fermi-Hubbard. Solving for lattice sizes up to 20×20 is within capability bounds.→ Two-orbital pnictide model
Captures iron-based superconductors with intra- and inter-orbital Coulomb interactions across two orbitals. Solving for lattice sizes up to 10×10 is within capability bounds.→ Twisted / nano-graphene Hamiltonians
Moiré superlattices and magic-angle systems are within capability bounds.
2. Nuclear & Quantum Dynamics
Ab-initio simulation of nuclear physics is a long-standing high-value frontier of classical scientific computing — limited today by the exponential cost of tracking many-body nucleon correlations. A Gigaquop-class quantum computer is the first machine credibly positioned to push past classical reach on real nuclear-structure problems and adjacent quantum dynamics workloads.
Fusion energy
Nuclear medicine
Advanced reactor design
Fundamental physics
→ Nuclear dynamics simulation
Quantum dynamics of nucleon systems (many-body nuclear structure, response functions, scattering kernels) via Quantum Phase Estimation-based methods.
3. Chemistry
Solving ground state energy problems for small-to-medium-scale active spaces of strongly correlated molecules, with ab-initio models tailored to quantum computing hardware through classical pre-processing, is a likely candidate for the first valuable chemistry applications to run on a system on QuEra’s roadmap. A Gigaquop-class system is the first machine credibly positioned to deliver quantitatively accurate mechanistic insights — beyond classical reach — into complex chemical processes.
Oxygen evolution catalysis
Metalloprotein active site chemistry
Drug modeling
Industrial catalysis
→ Ground state energy estimation of strongly correlated molecules
Ab-initio molecular Hamiltonians at small-to-medium-scale active spaces, tailored to quantum computing hardware through classical pre-processing. Within capability bounds for first quantitatively accurate mechanistic insights into metalloprotein active sites and transition metal chemistry.
Technical Specifications
Quantum Modality
Neutral atoms
Physical Architecture
Qubit Type
Rubidium
Number of Qubits
>10,000 physical qubits
Qubit Connectivity
Any-to-any
Number of Zones
Four zones:
Storage: Stores qubits with a long coherence time
Entanglement: Performs physical qubit gate operations
Measurement: Non-destructive syndrome measurements
Reloading: Stores a reservoir of atoms for reloading
Logical Performance
Logical Error Rate
10-6
Logical Qubits
256
Transversal Operation
Logical
Operation Mode
Logical
Yes
Bloqade™ Digital-Mode
Emulator
Yes, GPU-accelerated
Circuit-level Interface
QASM
Hardware-level Interface
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Hosting Requirements
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