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Fault-Tolerant Quantum Solvability of Noisy Binary Linear Problem

Wooyeong Song, Youngrong Lim, Kabgyun Jeong, Jinhyoung Lee, Jung Jun Park, M. S. Kim, Jeongho Bang

Published Sep 23, 2021• 0 citations
OpenAlex

Abstract

The noisy binary linear problem (NBLP) is a known computationally intractable problem. Thus, NBLP offers primitives for post-quantum cryptography. An efficient quantum NBLP algorithm that exhibits a polynomial quantum sample and time complexities has recently been proposed. However, a large number of samples should be loaded in a highly entangled state, and it is unclear whether such a precondition does not affect the quantum speedup obtained. Here, we analyse the quantum solvability of NBLP by considering the entire algorithm process, namely from the preparation of the quantum sample to the main computation. Assuming that the algorithm runs on fault-tolerant quantum circuitry, the cost is defined in terms of the overall number of layers of $T$ gates, often referred to as $T$-depth complexity. We show that the cost of solving the NBLP can be polynomial in the problem size, at the expense of an exponentially increasing number of logical qubits.

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The noisy binary linear problem (NBLP) is a known computationally intractable problem. Thus, NBLP offers primitives for post-quantum cryptography. An efficient quantum NBLP algorithm that exhibits a polynomial quantum sample and time complexities has recently been proposed. However, a large number of samples should be loaded in a highly entangled state, and it is unclear whether such a precondition does not affect the quantum speedup obtained. Here, we analyse the quantum solvability of NBLP by considering the entire algorithm process, namely from the preparation of the quantum sample to the main computation. Assuming that the algorithm runs on fault-tolerant quantum circuitry, the cost is defined in terms of the overall number of layers of TTT gates, often referred to as TTT-depth complexity. We show that the cost of solving the NBLP can be polynomial in the problem size, at the expense of an exponentially increasing number of logical qubits.
Abstract The noisy binary linear problem (NBLP) is known as a computationally hard problem, and therefore, it offers primitives for post-quantum cryptography. An efficient quantum NBLP algorithm that exhibits a polynomial quantum sample and time complexities has recently been proposed. However, the algorithm requires a large number of samples to be loaded in a highly entangled state and it is unclear whether such a precondition on the quantum speedup can be obtained efficiently. Here, we present a complete analysis of the quantum solvability of the NBLP by considering the entire algorithm process, namely from the preparation of the quantum sample to the main computation. By assuming that the algorithm runs on ‘fault-tolerant’ quantum circuitry, we introduce a reasonable measure of the computational time cost. The measure is defined in terms of the overall number of T gate layers, referred to as T-depth complexity. We show that the cost of solving the NBLP can be polynomial in the problem size, at the expense of an exponentially increasing logical qubits.
Abstract Noisy linear problems have been studied in various science and engineering disciplines. A class of ‘hard’ noisy linear problems can be formulated as follows: Given a matrix A ^ and a vector b constructed using a finite set of samples, a hidden vector or structure involved in b is obtained by solving a noise-corrupted linear equation A ^ x ≈ b + η , where η is a noise vector that cannot be identified. For solving such a noisy linear problem, we consider a quantum algorithm based on a divide-and-conquer strategy, wherein a large core process is divided into smaller subprocesses. The algorithm appropriately reduces both the computational complexities and size of a quantum sample. More specifically, if a quantum computer can access a particular reduced form of the quantum samples, polynomial quantum-sample and time complexities are achieved in the main computation. The size of a quantum sample and its executing system can be reduced, e.g., from exponential to sub-exponential with respect to the problem length, which is better than other results we are aware. We analyse the noise model conditions for such a quantum advantage, and show when the divide-and-conquer strategy can be beneficial for quantum noisy linear problems.
Quantum computation holds the promise of solving certain complex problems exponentially faster than classical computers. However, the high prevalent noise in current quantum devices impedes the accurate execution of even basic algorithms. This can be remedied by protecting quantum information with a quantum error-correcting code, where the logical information of an algorithmic qubit is spread across multiple physical qubits. Individual quantum errors are then located and corrected by the fault-tolerant measurement of multi-qubit stabilizer operators (parity checks). Unfortunately, error correction and fault tolerance both impose large demands on the qubit overhead: hundreds to thousands of physical qubits per logical qubit. We reduce the spacetime cost of fault tolerance by redesigning key building blocks of an error-corrected quantum computer. First, we develop a combinatorial proof with flag fault tolerance that exponentially reduces the extra qubits needed to measure a stabilizer of any size, while tolerating one fault. We leverage these proofs to then design state preparation circuits for the Steane and Golay codes with 100% yield. Next, we improve error correction on a planar layout by showing that a distance-four code encoding six logical qubits protects information as well as the distance-five surface code, using one-tenth as many physical qubits. Finally, we optimize the time overhead of logical gates in surface code quantum computers by protecting measurement results with a classical code, cutting computation time by a factor of two to six. Our hardware-agnostic optimizations of fault tolerance overheads thus suggest new routes to advance the timeline of error-free quantum computing.
Noisy linear problems have been studied in various science and engineering disciplines. A class of hard noisy linear problems can be formulated as follows: Given a matrix A^\hat{A}A^ and a vector b\mathbf{b}b constructed using a finite set of samples, a hidden vector or structure involved in b\mathbf{b}b is obtained by solving a noise-corrupted linear equation A^x≈b+η\hat{A}\mathbf{x} \approx \mathbf{b} + \boldsymbol\etaA^x≈b+η, where η\boldsymbol\etaη is a noise vector that cannot be identified. For solving such a noisy linear problem, we consider a quantum algorithm based on a divide-and-conquer strategy, wherein a large core process is divided into smaller subprocesses. The algorithm appropriately reduces both the computational complexities and size of a quantum sample. More specifically, if a quantum computer can access a particular reduced form of the quantum samples, polynomial quantum-sample and time complexities are achieved in the main computation. The size of a quantum sample and its executing system can be reduced, e.g., from exponential to sub-exponential with respect to the problem length, which is better than other results we are aware. We analyse the noise model conditions for such a quantum advantage, and show when the divide-and-conquer strategy can be beneficial for quantum noisy linear problems.
A defining feature in the field of quantum computing is the potential of a quantum device to outperform its classical counterpart for a specific computational task.By now, several proposals exist showing that certain sampling problems can be done efficiently quantumly, but are not possible efficiently classically, assuming strongly held conjectures in complexity theory, a feature dubbed quantum speedup.However, the effect of noise on these proposals is not well understood in general, and in certain cases it is known that simple noise can destroy the quantum speedup.Here we develop a fault-tolerant version of one family of these sampling problems, based on nonadaptive measurements on regular graph states composed of m qubits.We show that these sampling problems can be implemented fault tolerantly using quantum circuits of constant depth.We present two constructions, each taking poly(m) physical qubits, some of which are prepared in noisy magic states.The first of our constructions is a constant depth quantum circuit composed of single-and two-qubit nearest-neighbor Clifford gates in four dimensions.This circuit has one layer of interaction with a classical computer before final measurements.Our second construction is a constant depth quantum circuit with single-and two-qubit nearest-neighbor Clifford gates in three dimensions, but with two layers of interaction with a classical computer before the final measurements.For each of these constructions, we show that there is no classical algorithm which can sample according to its output distribution in poly(m) time, assuming two standard complexity theoretic conjectures hold.The noise model we assume is the so-called local stochastic quantum noise.Along the way, we introduce various concepts such as constant depth magic state distillation and constant depth output routing, which arise naturally in measurement based quantum computation, but have no constant-depth analog in the circuit model.
We prove a fault-tolerance theorem for quantum computation against adversarial noise. For every quantum circuit on Nˉ\bar{N}Nˉ logical qudits of depth Tˉ\bar{T}Tˉ, we construct a fault-tolerant circuit on N=poly(Nˉ)N=\text{poly}(\bar{N})N=poly(Nˉ) physical qudits of depth Tˉ⋅Nˉo(1)\bar{T}\cdot\bar{N}^{o(1)}Tˉ⋅Nˉo(1), which is robust against an adversary who may arbitrarily choose and corrupt an almost-linear number N1−o(1)N^{1-o(1)}N1−o(1) of physical qudits at each time step. This robustness significantly improves upon prior fault-tolerance theorems, which assumed corruptions were either local and stochastic, or else only act on a polynomially vanishing fraction of qudits. Our fault-tolerance scheme addresses a key bottleneck towards constructing quantum PCPs via the circuit-to-Hamiltonian mapping of Anshu, Breuckmann, and Nguyen (STOC'24). More fundamentally, our result demonstrates that fault-tolerant quantum computation remains possible under noise models that are global, worst-case, and non-Markovian over the full duration of the computation, directly countering concerns that correlated noise could fundamentally undermine quantum fault tolerance. Our construction is based on a new family of subsystem product codes we develop, which have large dimension and distance along with low-weight parity-checks, and which support transversal non-Clifford gates. We show how to perform single-shot fault-tolerant error correction on these codes using a Floquet-like procedure based on the local testability of classical tensor codes. We then obtain a universal fault-tolerance scheme using repeated code switching in a hypercubic qudit architecture. Finally, we recursively compose our scheme with itself to reduce an initially exponential qudit dimension down to a constant.