@article {3404, title = {Fault-Tolerant Quantum Memory using Low-Depth Random Circuit Codes}, year = {2023}, month = {11/29/2023}, abstract = {

Low-depth random circuit codes possess many desirable properties for quantum error correction but have so far only been analyzed in the code capacity setting where it is assumed that encoding gates and syndrome measurements are noiseless. In this work, we design a fault-tolerant distillation protocol for preparing encoded states of one-dimensional random circuit codes even when all gates and measurements are subject to noise. This is sufficient for fault-tolerant quantum memory since these encoded states can then be used as ancillas for Steane error correction. We show through numerical simulations that our protocol can correct erasure errors up to an error rate of 2\%. In addition, we also extend results in the code capacity setting by developing a maximum likelihood decoder for depolarizing noise similar to work by Darmawan et al. As in their work, we formulate the decoding problem as a tensor network contraction and show how to contract the network efficiently by exploiting the low-depth structure. Replacing the tensor network with a so-called \&$\#$39;\&$\#$39;tropical\&$\#$39;\&$\#$39; tensor network, we also show how to perform minimum weight decoding. With these decoders, we are able to numerically estimate the depolarizing error threshold of finite-rate random circuit codes and show that this threshold closely matches the hashing bound even when the decoding is sub-optimal.

}, url = {https://arxiv.org/abs/2311.17985}, author = {Jon Nelson and Gregory Bentsen and Steven T. Flammia and Michael J. Gullans} } @article {2680, title = {Quantum coding with low-depth random circuits}, year = {2020}, month = {10/19/2020}, abstract = {

Random quantum circuits have played a central role in establishing the computational advantages of near-term quantum computers over their conventional counterparts. Here, we use ensembles of low-depth random circuits with local connectivity in D\≥1 spatial dimensions to generate quantum error-correcting codes. For random stabilizer codes and the erasure channel, we find strong evidence that a depth O(logN) random circuit is necessary and sufficient to converge (with high probability) to zero failure probability for any finite amount below the channel capacity for any D. Previous results on random circuits have only shown that O(N1/D) depth suffices or that O(log3N) depth suffices for all-to-all connectivity (D\→\∞). We then study the critical behavior of the erasure threshold in the so-called moderate deviation limit, where both the failure probability and the distance to the channel capacity converge to zero with N. We find that the requisite depth scales like O(logN) only for dimensions D\≥2, and that random circuits require O(N\−\−\√) depth for D=1. Finally, we introduce an \"expurgation\" algorithm that uses quantum measurements to remove logical operators that cause the code to fail by turning them into either additional stabilizers or into gauge operators in a subsystem code. With such targeted measurements, we can achieve sub-logarithmic depth in D\≥2 spatial dimensions below capacity without increasing the maximum weight of the check operators. We find that for any rate beneath the capacity, high-performing codes with thousands of logical qubits are achievable with depth 4-8 expurgated random circuits in D=2 dimensions. These results indicate that finite-rate quantum codes are practically relevant for near-term devices and may significantly reduce the resource requirements to achieve fault tolerance for near-term applications.\ 

}, url = {https://arxiv.org/abs/2010.09775}, author = {Michael Gullans and Stefan Krastanov and David A. Huse and Liang Jiang and Steven T. Flammia} } @article {1434, title = {Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators }, journal = {New Journal of Physics}, volume = {14}, year = {2012}, month = {2012/09/27}, pages = {095022}, abstract = { Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We prove two complementary results that confirm this intuition. First, we show that a low-rank density matrix can be estimated using fewer copies of the state, i.e., the sample complexity of tomography decreases with the rank. Second, we show that unknown low-rank states can be reconstructed from an incomplete set of measurements, using techniques from compressed sensing and matrix completion. These techniques use simple Pauli measurements, and their output can be certified without making any assumptions about the unknown state. We give a new theoretical analysis of compressed tomography, based on the restricted isometry property (RIP) for low-rank matrices. Using these tools, we obtain near-optimal error bounds, for the realistic situation where the data contains noise due to finite statistics, and the density matrix is full-rank with decaying eigenvalues. We also obtain upper-bounds on the sample complexity of compressed tomography, and almost-matching lower bounds on the sample complexity of any procedure using adaptive sequences of Pauli measurements. Using numerical simulations, we compare the performance of two compressed sensing estimators with standard maximum-likelihood estimation (MLE). We find that, given comparable experimental resources, the compressed sensing estimators consistently produce higher-fidelity state reconstructions than MLE. In addition, the use of an incomplete set of measurements leads to faster classical processing with no loss of accuracy. Finally, we show how to certify the accuracy of a low rank estimate using direct fidelity estimation and we describe a method for compressed quantum process tomography that works for processes with small Kraus rank. }, doi = {10.1088/1367-2630/14/9/095022}, url = {http://arxiv.org/abs/1205.2300v2}, author = {Steven T. Flammia and David Gross and Yi-Kai Liu and Jens Eisert} } @article {1430, title = {Direct Fidelity Estimation from Few Pauli Measurements}, journal = {Physical Review Letters}, volume = {106}, year = {2011}, month = {2011/6/8}, abstract = { We describe a simple method for certifying that an experimental device prepares a desired quantum state rho. Our method is applicable to any pure state rho, and it provides an estimate of the fidelity between rho and the actual (arbitrary) state in the lab, up to a constant additive error. The method requires measuring only a constant number of Pauli expectation values, selected at random according to an importance-weighting rule. Our method is faster than full tomography by a factor of d, the dimension of the state space, and extends easily and naturally to quantum channels. }, doi = {10.1103/PhysRevLett.106.230501}, url = {http://arxiv.org/abs/1104.4695v3}, author = {Steven T. Flammia and Yi-Kai Liu} } @article {1436, title = {Efficient quantum state tomography}, journal = {Nature Communications}, volume = {1}, year = {2010}, month = {2010/12/21}, pages = {149}, abstract = { Quantum state tomography, the ability to deduce the state of a quantum system from measured data, is the gold standard for verification and benchmarking of quantum devices. It has been realized in systems with few components, but for larger systems it becomes infeasible because the number of quantum measurements and the amount of computation required to process them grows exponentially in the system size. Here we show that we can do exponentially better than direct state tomography for a wide range of quantum states, in particular those that are well approximated by a matrix product state ansatz. We present two schemes for tomography in 1-D quantum systems and touch on generalizations. One scheme requires unitary operations on a constant number of subsystems, while the other requires only local measurements together with more elaborate post-processing. Both schemes rely only on a linear number of experimental operations and classical postprocessing that is polynomial in the system size. A further strength of the methods is that the accuracy of the reconstructed states can be rigorously certified without any a priori assumptions. }, doi = {10.1038/ncomms1147}, url = {http://arxiv.org/abs/1101.4366v1}, author = {Marcus Cramer and Martin B. Plenio and Steven T. Flammia and David Gross and Stephen D. Bartlett and Rolando Somma and Olivier Landon-Cardinal and Yi-Kai Liu and David Poulin} } @article {1432, title = {Quantum state tomography via compressed sensing}, journal = {Physical Review Letters}, volume = {105}, year = {2010}, month = {2010/10/4}, abstract = { We establish methods for quantum state tomography based on compressed sensing. These methods are specialized for quantum states that are fairly pure, and they offer a significant performance improvement on large quantum systems. In particular, they are able to reconstruct an unknown density matrix of dimension d and rank r using O(rd log^2 d) measurement settings, compared to standard methods that require d^2 settings. Our methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements, use fast convex optimization, are stable against noise, and can be applied to states that are only approximately low-rank. The acquired data can be used to certify that the state is indeed close to pure, so no a priori assumptions are needed. We present both theoretical bounds and numerical simulations. }, doi = {10.1103/PhysRevLett.105.150401}, url = {http://arxiv.org/abs/0909.3304v4}, author = {David Gross and Yi-Kai Liu and Steven T. Flammia and Stephen Becker and Jens Eisert} }