Logical quantum processor based on reconfigurable atom arrays

Abstract

Suppressing errors is the central challenge for useful quantum computing1, requiring quantum error correction (QEC)2–6 for large-scale processing. However, the overhead in the realization of error-corrected ‘logical’ qubits, in which information is encoded across many physical qubits for redundancy2–4, poses substantial challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Using logical-level control and a zoned architecture in reconfigurable neutral-atom arrays7, our system combines high two-qubit gate fidelities8, arbitrary connectivity7,9, as well as fully programmable single-qubit rotations and mid-circuit readout10–15. Operating this logical processor with various types of encoding, we demonstrate improvement of a two-qubit logic gate by scaling surface-code6 distance from d = 3 to d = 7, preparation of colour-code qubits with break-even fidelities5, fault-tolerant creation of logical Greenberger–Horne–Zeilinger (GHZ) states and feedforward entanglement teleportation, as well as operation of 40 colour-code qubits. Finally, using 3D [[8,3,2]] code blocks16,17, we realize computationally complex sampling circuits18 with up to 48 logical qubits entangled with hypercube connectivity19 with 228 logical two-qubit gates and 48 logical CCZ gates20. We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical-qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling21,22. These results herald the advent of early error-corrected quantum computation and chart a path towards large-scale logical processors.

Computational capabilities and compiler development for neutral atom quantum processors—connecting tool developers and hardware experts

“…In the context of NAQC, individual tweezers holding qubits can be dynamically moved during computation without disrupting entanglement, as demonstrated in [8,9,75]. This capability offers an alternative to implementing SWAP operations at the virtual level, requiring three CX gates.…”

“…Instead of using shuttling only as a substitute for virtual SWAP and MOVE operations, the recently discussed Dynamically Field-programmable Qubit Arrays (DPQA or D-FPQA) [8,9,30,35] has emerged as a quantum processor design, which entirely focuses on shuttling. Compared to the previous discussion, this new computation architecture is not based on qubits placed on a regular grid.…”

“…To achieve computational advantages with Quantum Computers (QC), large-scale, high-fidelity qubit entanglement is required, posing a technologically challenging problem. In recent years, qubit systems based on Neutral Atoms (NA) [1,2] in combination with Rydberg interactions have established themselves as a promising candidate, due to their ability to perform high-fidelity long-range gates [3][4][5], native multi-qubit gates [4][5][6][7], and physical atom shuttling [8,9], combined with their scalability [10][11][12].…”

Activation of metrologically useful genuine multipartite entanglement

“…Superconducting circuits have also been used to create GHZ states [24,30] and implement error correction [103][104][105][106][107][108]. Error correction has recently been carried out in reconfigurable atom arrays [109]. Our proposal might offer a viable information processing scenario in the NISQ era [61], in which simple error mitigation technics are needed [110][111][112][113][114][115][116][117][118][119][120][121].…”

Complexity of life sciences in quantum and AI era

“…47 The neutral-atom quantum computer with 280 physical qubits, 99.5% two-qubit gate fidelities, arbitrary connectivity, as well as fully programmable single-qubit rotations and mid-circuit readout was recently realized. 48 It has been pointed out from an algorithmic perspective that current quantum algorithms exhibit promise in the fields of drug discovery 49,50 and biology. 51 Specifically, these algorithms are capable of processing ground state calculations 52,53 and linear systems 54 at a faster rate, which enables more precise predictions of drug-receptor interactions 55 and protein folding.…”