We study approximate quantum low-density parity-check (QLDPC) codes, which are approximate quantum error-correcting codes specified as the ground space of a frustration-free local Hamiltonian, whose terms do not necessarily commute. Such codes generalize stabilizer QLDPC codes, which are exact quantum error-correcting codes with sparse, low-weight stabilizer generators (i.e. each stabilizer generator acts on a few qubits, and each qubit participates in a few stabilizer generators). Our investigation is motivated by an important question in Hamiltonian complexity and quantum coding theory: do stabilizer QLDPC codes with constant rate, linear distance, and constant-weight stabilizers exist?We show that obtaining such optimal scaling of parameters (modulo polylogarithmic corrections) is possible if we go beyond stabilizer codes: we prove the existence of a family of [[N, k, d, ε]] approximate QLDPC codes that encode k = Ω(N) logical qubits into N physical qubits with distance d = Ω(N) and approximation infidelity ε = O(1/ polylog(N)). The code space is stabilized by a set of 10-local noncommuting projectors, with each physical qubit only participating in O(polylog N) projectors. We prove the existence of an efficient encoding map and show that the spectral gap of the code Hamiltonian scales as Ω(N −3.09 ). We also show that arbitrary Pauli errors can be locally detected by circuits of polylogarithmic depth.Our family of approximate QLDPC codes is based on applying a recent connection between circuit Hamiltonians and approximate quantum codes (Nirkhe, et al., ICALP 2018) to a result showing that random Clifford circuits of polylogarithmic depth yield asymptotically good quantum codes (Brown and Fawzi, ISIT 2013). Then, in order to obtain a code with sparse checks and strong detection of local errors, we use a spacetime circuit Hamiltonian construction in order to take advantage of the parallelism of the Brown-Fawzi circuits.The analysis of the spectral gap of the code Hamiltonian is the main technical contribution of this work. We show that for any depth D quantum circuit on n qubits there is an associated spacetime circuit-to-Hamiltonian construction with spectral gap Ω(n −3.09 D −2 log −6 (n)). To lower bound this gap we use a Markov chain decomposition method to divide the state space of partially completed circuit configurations into overlapping subsets corresponding to uniform circuit segments of depth log n, which are based on bitonic sorting circuits. We use the combinatorial properties of these circuit configurations to show rapid mixing between the subsets, and within the subsets we develop a novel isomorphism between the local update Markov chain on bitonic circuit configurations and the edge-flip Markov chain on equal-area dyadic tilings, whose mixing time was recently shown to be polynomial (Cannon, Levin, and Stauffer, RANDOM 2017). Previous lower bounds on the spectral gap of spacetime circuit Hamiltonians have all been based on a connection to exactly solvable quantum spin chains and applied only to ...
Biased noise is common in physical qubits, and tailoring a quantum code to the bias by locally modifying stabilizers or changing boundary conditions has been shown to greatly increase error correction thresholds. In this work, we explore the challenges of using a specific tailored code, the XY surface code, for fault-tolerant quantum computation. We introduce an efficient and fault-tolerant decoder, belief-matching, which we show has good performance for biased circuit-level noise. Using this decoder, we find that for moderately biased noise, the XY surface code has a higher threshold and lower overhead than the square CSS surface code, however it performs worse when below threshold than the rectangular CSS surface code. We identify a contributor to the reduced performance that we call fragile boundary errors. These are string-like errors that can occur along spatial or temporal boundaries in planar architectures or during logical state preparation and measurement. While we make partial progress towards mitigating these errors by deforming the boundaries of the XY surface code, our work suggests that fragility could remain a significant obstacle, even for other tailored codes. We expect belief-matching will have other uses, and find that it increases the threshold of the surface code to 0.940(3)% in the presence of circuit-level depolarising noise, compared to 0.817(5)% for a minimum-weight perfect matching decoder.
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