“Building useful quantum applications benefits from state-of-the-art access to quantum hardware. AWS customers can now access our most advanced processor to date, Aspen-8, based on our 32-qubit node technology. By delivering access to our systems in collaboration with AWS we will be able to reach a broader market and help accelerate the growth of this emerging industry.”
Chad Rigetti, Founder and CEO of Rigetti Computing
Rigetti Computing builds and deploys integrated quantum computing systems leveraging superconducting qubit technology. These systems enable organizations to augment existing computational workflows with quantum processors. Rigetti serves customers in finance, insurance, pharmaceuticals, defense, and energy with custom software and full-stack solutions focused on simulation, optimization, and machine learning applications. The company is headquartered in California, with offices in Washington, DC, Australia, and the UK.
Rigetti QPU Performance
Visit Rigetti’s QPU page for system and performance information, including gate fidelities, and coherence times.
Rigetti Superconducting Quantum Processors
Rigetti quantum processors are universal, gate-model machines based on superconducting qubits. The Rigetti Aspen series chips feature tileable lattices of alternating fixed-frequency and tunable superconducting qubits within a system architecture that is scalable to large qubit counts. Parametric entangling logic gates on these chips also offer fast gate times and program execution rates. The Aspen series quantum chips are fabricated in Rigetti’s dedicated device foundry using state-of-the-art manufacturing techniques for superconducting circuits. The result is a combination of precision, scale, and speed.
Rigetti processors consist of three principal subsystems. First, user programs are optimized into machine-native instructions through an efficient compiler tool chain. Then, a low-latency hardware controller sequences these instructions as calibrated electrical signals. Finally, qubits that are made from coherent superconducting circuit elements transduce these electrical signals logically as digital quantum gates and measurement instructions.
Universal, gate-based quantum computers enable applications in areas including chemical simulation, combinatorial optimization, and machine learning. Because coherent superconducting qubits operate on the same quantum mechanical principles that govern nature, they can be used to efficiently simulate and understand the behavior of biochemical mechanisms, for example photosynthesis and protein folding. In addition, quantum computers can be used to navigate exponentially more state space to find more optimal solutions among an incalculable set of possibilities, for example in global logistics management. Early application demonstrations on Rigetti processors include the first simulation of an atomic nucleus as well as the largest implementation of a linear system solver on quantum hardware.
The Aspen chip connectivity graph is octagonal with 3-fold (2-fold for edges) connectively. The Rigetti quilc compiler maps an abstract quantum algorithm onto this network of physical connections. SWAP gates shuttle quantum information across the Aspen processor to link non-nearest neighbor qubits. Because these can be costly operations, the quilc compiler is highly optimized for the “layout problem.” Specifically, the compiled depths for programs on Aspen graphs are often estimated to be less than or approximately equal to an all-to-all, serial program construction.
Rigetti’s two-qubit entangling gate mechanism (typically a controlled-phase gate or “CZ” gate) is actuated by electrical frequency modulation (FM) control. For these “parametric gates” to execute this instruction, the radio dial of one qubit is flipped into resonance with a nearest-neighbor qubit for a precisely defined duration. On the Aspen graph, this scheme requires that at least half of the qubits are FM-tunable. Within the integrated circuit, through-silicon vias and a flip-chip bonded superconducting shield minimizes spurious control signal crosstalk on the chip. To estimate the effect of decoherence on an algorithm, the lifetimes of the qubits (~25-50 μs) should be compared to the gate duration (~50-200 ns) multiplied by the circuit depth of the algorithm. Performance metrics for current Rigetti systems can be found on Rigetti’s QPU page.