Photonic Quantum Computing Advances With Fault Tolerant Protocols. New Research From PsiQuantum

The pursuit of scalable, fault-tolerant quantum computation necessitates innovative approaches to error correction, moving beyond established methods like surface codes. Recent research explores a novel construction utilising code concatenation and transversal gates, yielding protocols applicable to photonic fusion-based quantum computation (FBQC). This technique presents families of circuits characterised by low-weight stabilizer measurements, achieving high erasure thresholds with comparatively small resource state costs. Daniel Litinski, from PsiQuantum in Palo Alto, and colleagues detail this work in their article, “Blocklet concatenation: Low-overhead fault-tolerant protocols for fusion-based quantum computation”, demonstrating potential improvements in footprint scaling for logical qubits and outlining techniques for practical implementation in photonic hardware. FBQC, a method of quantum computation utilising photons and ‘fusion’ operations to create entangled states, offers a promising architecture for building large-scale quantum computers. The research suggests these protocols may also be adaptable to other quantum computing platforms beyond photonics.

Fault-tolerant quantum computation necessitates effective error correction, and blocklet codes represent a developing strategy for protecting quantum information via modular, scalable architectures. These codes function by encoding a single logical qubit, the fundamental unit of quantum information, into multiple physical qubits, thereby distributing the risk of error. Current research concentrates on characterising the minimum-weight errors within these codes, which directly influences their performance and establishes the crucial code distance. The code distance represents the minimum number of physical qubit errors required to create an erroneous logical state, and thus dictates the number of correctable errors. Researchers propose a conjecture concerning the code distance of an [n, k, d]L protocol, where ‘n’ denotes the total number of physical qubits, ‘k’ represents the number of encoded logical qubits, ‘d’ is the product code distance, and ‘L’ signifies the number of layers in the blocklet code construction. The conjecture posits that the code distance scales with the product of the product code distance and the inner code distance raised to the power of L-2.

Numerical simulations support this proposed relationship between minimum-weight errors and code distance, demonstrating subthreshold scaling consistent with theoretical predictions. Subthreshold scaling refers to the ability of the code to correct errors even when the physical error rate is below a certain threshold, crucial for practical quantum computation. These simulations validate the conjectured scaling behaviour, providing confidence in the code’s potential. The study details techniques for performing logical operations, the fundamental building blocks of quantum algorithms, decoding, the process of identifying and correcting errors, and implementing these protocols in photonic hardware. Photonic qubits utilise photons, particles of light, to represent quantum information, offering advantages in coherence and connectivity. This broadens the potential applicability of blocklet codes beyond solely fault-tolerant quantum computation.

Researchers demonstrate protocol families employing 8, 10, and 12-qubit resource states, achieving erasure thresholds of 13.8%, 19.1%, and 11.5% respectively. The erasure threshold represents the maximum physical error rate at which the code can reliably recover the encoded quantum information. These results showcase the practical viability of these codes, indicating they can function effectively with realistic error rates. Importantly, the footprint, or resource cost per logical qubit, scales favourably, suggesting a potentially advantageous resource utilisation compared to other established approaches, such as surface codes, which require a large number of physical qubits to encode a single logical qubit. This efficient resource utilisation is critical for building large-scale, practical quantum computers.