In the presence of physical noise of all platforms for quantum computation, quantum error correction (QEC) becomes a critical way to realize quantum algorithms with large quantum volumes. In order to understand the influence of quantum noise on QEC codes and further improve the performance of logical circuits, the noises should be accurately analyzed with proper models. Here we focus on the trapped-ion system. Fundamentally, we start from the laser pulses of the quantum gates in the circuits and extract the noise components from the complete evolution of the quantum states, beyond the standard depolarizing model and other simplified models. Our simulations indicate that the logical performance under real noises is significantly better than that predicted by previous models. Meanwhile, the advantage of QEC is shown in the levels of one, two, and more logical qubits. Moreover, we can increase the logical fidelity by the method of ion mapping, which is based on knowledge of the specific noise distribution of different ions. Some powerful evidence from the numerical results demonstrates the possibility to access fault-tolerant quantum computation with the trapped-ion system.
In the presence of physical noise of all platforms for quantum computation, quantum error correction (QEC) becomes a critical way to realize quantum algorithms with large quantum volumes. In order to understand the influence of quantum noise on QEC codes and further improve the performance of logical circuits, the noises should be accurately analyzed with proper models. Here we focus on the trapped-ion system. Fundamentally, we start from the laser pulses of the quantum gates in the circuits and extract the noise components from the complete evolution of the quantum states, beyond the standard depolarizing model and other simplified models. Our simulations indicate that the logical performance under real noises is significantly better than that predicted by previous models. Meanwhile, the advantage of QEC is shown in the levels of one, two, and more logical qubits. Moreover, we can increase the logical fidelity by the method of ion mapping, which is based on knowledge of the specific noise distribution of different ions. Some powerful evidence from the numerical results demonstrates the possibility to access fault-tolerant quantum computation with the trapped-ion system.
Fault-tolerant quantum error correction provides a strategy to protect information processed by a quantum computer against noise which would otherwise corrupt the data. A fault-tolerant universal quantum computer must implement a universal gate set on the logical level in order to perform arbitrary calculations to in principle unlimited precision. In this paper we characterize the recent demonstration of a fault-tolerant universal gate set in a trapped-ion quantum computer [Postler et al., Nature (London) 605, 7911 (2022)] and identify aspects to improve the design of experimental setups to reach an advantage of logical over physical qubit operation. We show that various criteria to assess the break-even point for fault-tolerant quantum operations are within reach for the ion trap quantum computing architecture under consideration. Furthermore, we analyze the influence of crosstalk in entangling gates for logical state preparation circuits. These circuits can be designed to respect fault tolerance for specific microscopic noise models. We find that an experimentally informed depolarizing noise model captures the essential noise dynamics of the fault-tolerant experiment that we consider, and crosstalk is negligible in the currently accessible regime of physical error rates. For deterministic Pauli state preparation, we provide a fault-tolerant unitary logical qubit initialization circuit, which can be realized without in-sequence measurement and feed-forward of classical information. Additionally, we show that nondeterministic state preparation schemes, i.e., repeat until success, for logical Pauli and magic states perform with higher logical fidelity over their deterministic counterparts for the current and anticipated future regime of physical error rates. Our results offer guidance on improvements of physical qubit operations and validate the experimentally informed noise model as a tool to predict logical failure rates in quantum computing architectures based on trapped ions.
Fault-tolerant quantum error correction (QEC) is crucial for unlocking the true power of quantum computers. QEC codes use multiple physical qubits to encode a logical qubit, which is protected against errors at the physical qubit level. Here, we use a trapped-ion system to experimentally prepare m-qubit Greenberger-Horne-Zeilinger states and sample the measurement results to construct $m\ifmmode\times\else\texttimes\fi{}m$ logical states of the [[m2,1,m]] Shor code, up to m=7. The synthetic logical fidelity shows how deeper encoding can compensate for additional gate errors in state preparation for larger logical states. However, the optimal code size depends on the physical error rate and we find that m=5 has the best performance in our system. We further realize the direct logical encoding of the [[9,1,3]] Shor code on nine qubits in a 13-ion chain for comparison, with $98.8(1)\mathrm{%}$ and $98.5(1)\mathrm{%}$ fidelity for state ${|\ifmmode\pm\else\textpm\fi{}⟩}_{L}$, respectively.
High-rate quantum error correcting (QEC) codes encode many logical qubits in a given number of physical qubits, making them promising candidates for quantum computation. Implementing high-rate codes at a scale that both frustrates classical computing and improves performance by encoding requires both high fidelity gates and long-range qubit connectivity -- both of which are offered by trapped-ion quantum computers. Here, we demonstrate computations that outperform their unencoded counterparts in the high-rate [[k+2,k,2]] iceberg quantum error detecting (QED) and [[(k2+2)(k1+2),k2k1,4]] two-level concatenated iceberg QEC codes, using the 98-qubit Quantinuum Helios trapped-ion quantum processor. Utilizing new gadgets for encoded operations, we realize this "beyond break-even" performance with reasonable postselection rates across a range of fault-tolerant (FT) and partially-fault-tolerant (pFT) component and application benchmarks with between 48 and 94 logical qubits. These benchmarks include FT state preparation and measurement, QEC cycle benchmarking, logical gate benchmarking, GHZ state preparation, and a pFT quantum simulation of the three-dimensional XY model of quantum magnetism. Additionally, we illustrate that postselection rates can be suppressed by increasing the code distance via concatenation. Our results represent state-of-the-art logical component and state fidelities and provide evidence that high-rate QED/QEC codes are viable on contemporary quantum computers for near-term beyond-classical-scale computation.
The full potential of quantum computers will be attainable when large devices can employ quantum error correction and attain fault-tolerance. Great strides have been made, with experimental demonstrations of pivotal components for quantum error correction on a variety of platforms, substantiating that the foundations for fault-tolerant logic are realisable. This thesis explores the kinds of noise we expect to find at the logical level while employing schemes for fault-tolerance in this emerging period and how we may optimise such schemes. We study how a noise characteristic called bias is transformed in the implementation of a fault-tolerant gadget for the T-gate. We show that noise bias is amplified at the logical level due to error correction, which is furthermore affected by the noise-transforming properties of the injection gadget, which separately induces logical phase bias. We experimentally characterise the noise on a trapped-ion quantum computer. We target a logical CNOT with this method such that we are able to: understand and mitigate control layer imperfections, account for the distinction in noise characteristics between individual CNOT and transversal CNOTs, and make predictions on logical level performance if these transversal CNOTs are used as a logical CNOT for the Steane code. This is achieved by leveraging information about correlated noise across the device from the physical characterisation of gates, demonstrating that such diagnostic information can be learned scalably. Finally, noting the transversal gate capabilities of recent architectures, we optimise fault-tolerant circuits for preparing magic states in that setting. We describe an algorithm that recompiles and synthesises such circuits for minimal T-depth and low CNOT depth, while maintaining the total qubit count. We apply this algorithm to fault-tolerant circuits for |CCZ>, |CS> and |T> and show how we can reduce overheads for preparing magic states.
With fault-tolerant quantum computing (FTQC) on the horizon, it is critical to understand sources of logical errors in plausible hardware implementations of quantum error-correcting codes. Detailed error modeling of computational instructions on particular FTQC architectures will enable the better prediction of error propagation in FT-encoded quantum circuits while revealing where greater attention is needed in hardware design. In this work, we consider logical error rates for the surface code implemented on a hypothetical grid-based trapped-ion quantum charge-coupled device architecture. Specifically, we construct logical channels for the idling surface code and examine its diamond error under a mixed coherent and stochastic circuit-level noise model inspired by trapped ions. We include the coherent dephasing noise that is known to accumulate during physical qubit idling and transport in these systems, determining idling and transport durations using the time-resolved output of an open-source trapped-ion surface code compiler. To estimate expectation values of logical Pauli observables following hardware circuits containing non-Clifford sources of noise, we utilize a Monte Carlo technique to sample from an underlying quasiprobability distribution of Clifford circuits that we independently simulate in a phase-sensitive fashion. We verify error suppression up to code distance d = 11 at coherent dephasing rates near and below those of current-generation trapped-ion quantum computers and find that logical error rates align with those of analogous fully stochastic simulations in this regime. Exploring higher dephasing rates at d = 3 − 5 , we find evidence for growing coherent rotations about all three logical Pauli axes, increased diagonal logical error process matrix elements relative to those of stochastic simulations, and a reduced dephasing rate threshold. Overall, our work paves a way toward realistic hardware emulation of small fault-tolerant quantum processes, e.g., members of an FTQC instruction set.
Quantum error correction (QEC), the lynchpin of fault-tolerant quantum computing (FTQC), is designed and validated against well-behaved Pauli stochastic error models. But in real-world deployment, QEC protocols encounter a vast array of other errors -- coherent and non-Pauli errors -- whose impacts on quantum circuits are vastly different than those of stochastic Pauli errors. The impacts of these errors on QEC and FTQC protocols have been largely unpredictable to date due to exponential classical simulation cost. Here, we show how to accurately and efficiently model the effects of coherent and non-Pauli errors on FTQC, and we study the effects of such errors on syndrome extraction for surface and bivariate bicycle codes, and on magic state cultivation. Our analysis suggests that coherent error can shift fault-tolerance thresholds, increase the space-time cost of magic state cultivation, and can increase logical error rates by an order of magnitude compared to equivalent stochastic errors. These analyses are enabled by a new technique for mapping any Markovian circuit-level error model with sufficiently small error rates onto a detector error model (DEM) for an FTQC circuit. The resulting DEM enables Monte Carlo estimation of logical error rates and noise-adapted decoding, and its parameters can be analytically related to the underlying physical noise parameters to enable approximate strong simulation.