ИССЛЕДОВАНИЕ И РАЗРАБОТКА КВАНТОВОГО КОДА ДЛЯ ИСПРАВЛЕНИЯ ОШИБОК

<p indent="0mm">Recently, the achievement, entitled “lifetime enhancement for a logic qubit by bosonic encoding error correction”, has been selected as one of “The Ten Major Advances in Chinese Science 2023”. Here, we will introduce briefly the importance of this work. It is known that quantum states are fragile due to decoherence induced by environment. Error will happen on a qubit, resulting in continuously changes of the parameters for this qubit, such as amplitude and phase parameters. The aim of quantum error correction is to amend those errors by using various quantum error correction codes, in which a logic qubit can be represented by several physical qubits. It should be noted that the quantum error correction can be realized by correcting a discrete set of errors, such as bit-flip error, phase-flip error or both of them. On the other hand, types of errors occurred depend on specific platforms of quantum computation. For example, photon loss is more inclined to occur in photonic systems. In this case, the approach of bosonic encoding of quantum error correction is more efficient. The work “lifetime enhancement for a logic qubit by bosonic encoding error correction” is for such a scenario. The challenge of quantum error correction is that operation itself is not perfect which may incur new errors. So it is actually difficult to demonstrate that we can benefit from quantum error correction. The achievement of lifetime enhancement by bosonic encoding error correction succeeds in beating this break-even point. The experiments are performed by circuit quantum electrodynamics on a superconducting processor with a qubit and a coupled resonate cavity. The logic qubit is realized by the bosonic mode of the cavity, while the superconducting qubit plays the role of the ancillary qubit in error correction. The initial states are prepared by the superconducting qubit, and are transferred to the logic qubit in the cavity. Then the error correction can be performed based on detection of photon loss by the superconducting qubit. The results show that the lifetime of the logic qubit increases from <sc>694 μs</sc> to <sc>805 μs</sc> with 16% enhancement. Besides high quality devices, the success of this experiment depends on the feedback control to detect and correct the error of photon loss in a short time. The result of beating the break-even point of bosonic encoding error correction is for one logic qubit. In the near future, it is hopeful to implement logic gates, such as single-qubit rotation gate and two-qubit gate, on a couple of logic qubits by bosonic encoding. The importance of the results will depend on whether higher fidelity can be achieved compared with those of gates with physical qubits. In this way, the advantage of the logic qubits for quantum computation is presented. It will be significant that the approach of bosonic encoding with a superconducting processor is scalable to hundreds of logic qubits, which may take a few years to realize. Our aim is to realize a fault-tolerant universal quantum computer based on a large quantity of high-precision logic qubits. The work “lifetime enhancement for a logic qubit by bosonic encoding error correction” is an important step toward this aim.

With the demonstrated ability to perform calculations in seconds that would take classical supercomputers thousands of years, quantum computers namely hold the promise of radically advancing sustainable IT. However, quantum computers face challenges due to the inherent noise in physical qubits, necessitating error correction for reliable operation in solving industrial-scale problems, which will require more computation time, energy, and electronic components than initial laboratory-scale experiments. Yet, while researchers have modeled and analyzed the environmental impacts of classical computers using Life cycle assessment the environmental performance of quantum computing remains unknown to date. This study contributes to filling this critical gap in two ways: (1) by establishing an environmental profile for quantum computers based on superconducting qubits; and (2) by comparing it to a functionally equivalent profile of a state-of-the-art supercomputer. With the comparison based on the problem size, the paper shows how the usage time can drive an environmental advantage for quantum computers under specific scaling conditions and quantum error correcting codes. The results emphasize that quantum error correction hardware has a substantial environmental impact due to the numerous electronic components needed to achieve 100 logical qubits. This paper can serve as a basis for designing more environmentally friendly quantum computers and for establishing their environmental profiles, as well as those of the human activities that will use them.

Scalable quantum computation and communication require error control to protect quantum information against unavoidable noise. Quantum error correction protects information stored in two-level quantum systems (qubits) by rectifying errors with operations conditioned on the measurement outcomes. Error-correction protocols have been implemented in nuclear magnetic resonance experiments, but the inherent limitations of this technique prevent its application to quantum information processing. Here we experimentally demonstrate quantum error correction using three beryllium atomic-ion qubits confined to a linear, multi-zone trap. An encoded one-qubit state is protected against spin-flip errors by means of a three-qubit quantum error-correcting code. A primary ion qubit is prepared in an initial state, which is then encoded into an entangled state of three physical qubits (the primary and two ancilla qubits). Errors are induced simultaneously in all qubits at various rates. The encoded state is decoded back to the primary ion one-qubit state, making error information available on the ancilla ions, which are separated from the primary ion and measured. Finally, the primary qubit state is corrected on the basis of the ancillae measurement outcome. We verify error correction by comparing the corrected final state to the uncorrected state and to the initial state. In principle, the approach enables a quantum state to be maintained by means of repeated error correction, an important step towards scalable fault-tolerant quantum computation using trapped ions.

Quantum computing is a multidisciplinary field at the intersectionof quantum physics, computer science, and mathematics, aiming to har-ness quantum mechanical phenomena for computational advantage. Thisarticle provides a comprehensive review of both the foundational princi-ples and advanced theoretical models of quantum computing. We beginwith the basic framework of quantum computation, introducing the con-cept of qubits as two-level quantum systems, the linear algebra of Hilbertspaces, Dirac’s bra–ket notation, and the postulates of quantum mechan-ics (state superposition, unitary evolution, and projective measurement).We then discuss quantum logic gates and circuits, highlighting how clas-sical reversible computation principles extend to universal quantum gatesets. Next, we survey key quantum algorithms (from Shor’s factoring toGrover’s searching and beyond) and the complexity-theoretic implicationsof quantum computers, contrasting the class BQP with classical complex-ity classes. We cover the theoretical foundations of quantum error cor-rection and fault tolerance, explaining how quantum information can beprotected from decoherence using redundancy and how fault-tolerant pro-tocols can, in principle, allow scalable quantum computation despite noisyhardware. We then explore advanced models and paradigms of quantumcomputation, including measurement-based quantum computing (one-wayquantum computing), topological quantum computation with anyons, adi-abatic quantum computing and quantum annealing, continuous-variablequantum computing, and related approaches. We also provide an overviewof quantum information theory as it relates to computation, discussingentanglement, quantum entropy, and information-theoretic limits like theHolevo bound. Finally, we review the leading physical implementations(quantum hardware models) of quantum computers—trapped ions, super-conducting circuits, photonic systems, solid-state spin qubits, and oth-ers—outlining the challenges and achievements of each approach.

Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks

Quantum computers are growing at a rapid pace. Trends show that the current decade could be the one in which quantum computers deliver commercial value. Today's quantum computers are overwhelmed by errors. To unlock their massive potential, a significant reduction in errors is required. Several qubit technologies are under research, e.g. superconducting, neutral atoms, trapped ions, photons and silicon. For useful quantum computing on these qubit technologies, Quantum Error Correction (QEC) turns unreliable physical qubits into useful logical qubits. QEC requires solving a real-time, large data volume decoding problem that determines the effect of physical noise on the computation carried out on logical qubits. The nature of the decoding problem depends on the qubit technology, choice of QEC code and mode of quantum computation. These are actively researched subjects in the quantum computing world. Hardware-based decoders that use Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuits (ASIC) technologies are the most promising solutions in terms of speed and scalability. However, the design methodology for these solutions typically suffers from long lead times and a lack of flexibility. We propose GridGenQ, a hardware design utility that automates part of the FPGA/ASIC design methodology for distributed QEC decoders. We show that with our novel solution, development time for such distributed hardware decoders can be reduced from several weeks to few minutes. This allows for fast prototyping of distributed decoder architectures to meet the requirements posed by a dynamic QEC landscape.

Quantum error correction is a set of methods to protect quantum information—that is, quantum states—from unwanted environmental interactions (decoherence) and other forms of noise. The information is stored in a quantum error-correcting code, which is a subspace in a larger Hilbert space. This code is designed so that the most common errors move the state into an error space orthogonal to the original code space while preserving the information in the state. It is possible to determine whether an error has occurred by a suitable measurement and to apply a unitary correction that returns the state to the code space without measuring (and hence disturbing) the protected state itself. In general, codewords of a quantum code are entangled states. No code that stores information can protect against all possible errors; instead, codes are designed to correct a specific error set, which should be chosen to match the most likely types of noise. An error set is represented by a set of operators that can multiply the codeword state. Most work on quantum error correction has focused on systems of quantum bits, or qubits, which are two-level quantum systems. These can be physically realized by the states of a spin-1/2 particle, the polarization of a single photon, two distinguished levels of a trapped atom or ion, the current states of a microscopic superconducting loop, or many other physical systems. The most widely used codes are the stabilizer codes, which are closely related to classical linear codes. The code space is the joint +1 eigenspace of a set of commuting Pauli operators on n qubits, called stabilizer generators; the error syndrome is determined by measuring these operators, which allows errors to be diagnosed and corrected. A stabilizer code is characterized by three parameters [[n,k,d]], where n is the number of physical qubits, k is the number of encoded logical qubits, and d is the minimum distance of the code (the smallest number of simultaneous qubit errors that can transform one valid codeword into another). Every useful code has n&gt;k; this physical redundancy is necessary to detect and correct errors without disturbing the logical state. Quantum error correction is used to protect information in quantum communication (where quantum states pass through noisy channels) and quantum computation (where quantum states are transformed through a sequence of imperfect computational steps in the presence of environmental decoherence to solve a computational problem). In quantum computation, error correction is just one component of fault-tolerant design. Other approaches to error mitigation in quantum systems include decoherence-free subspaces, noiseless subsystems, and dynamical decoupling.

Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML implementations. To address this, we investigate the performance of a simple, parity-classifying Variational Quantum Classifier (VQC) implemented with the [[4,2,2]] error-detecting stabiliser code in a simulated noisy environment. To our knowledge, this is the first implementation of a stabiliser-based error detection code to a QML algorithm. We invoke ancilla qubits to logically encode rotation gates, and classically simulate the logically-encoded VQC under two simple noise models representing gate noise and environmental noise. We demonstrate that the stabiliser code improves the training accuracy at convergence compared to noisy implementations without error detection. However, we find that the effectiveness and reliability of error detection is contingent upon keeping the ancilla qubit error rates below a specific threshold, due to the propagation of ancilla errors to the physical qubits. Our results provide an important insight that extends to QML implemented with full error-correction: when the QEC code requires ancilla qubits for logical rotations but cannot fully correct errors propagated between ancilla and physical qubits, the maximum achievable accuracy of the QML model is constrained. This highlights the need for additional error correction or mitigation strategies to support the practical implementation of QML algorithms with QEC on quantum devices.

To implement fault-tolerant quantum computation with continuous variables, the Gottesman--Kitaev--Preskill (GKP) qubit has been recognized as an important technological element. We have proposed a method to reduce the required squeezing level to realize large scale quantum computation with the GKP qubit [Phys. Rev. X. {\bf 8}, 021054 (2018)], harnessing the virtue of analog information in the GKP qubits. In the present work, to reduce the number of qubits required for large scale quantum computation, we propose the tracking quantum error correction, where the logical-qubit level quantum error correction is partially substituted by the single-qubit level quantum error correction. In the proposed method, the analog quantum error correction is utilized to make the performances of the single-qubit level quantum error correction almost identical to those of the logical-qubit level quantum error correction in a practical noise level. The numerical results show that the proposed tracking quantum error correction reduces the number of qubits during a quantum error correction process by the reduction rate $\left\{{2(n-1)\times4^{l-1}-n+1}\right\}/({2n \times 4^{l-1}})$ for $n$-cycles of the quantum error correction process using the Knill's $C_{4}/C_{6}$ code with the concatenation level $l$. Hence, the proposed tracking quantum error correction has great advantage in reducing the required number of physical qubits, and will open a new way to bring up advantage of the GKP qubits in practical quantum computation.

A quantum computer is a powerful machine that provides a new approach to information processing based on quantum mechanics. However, basically, qubits are very vulnerable to errors, thus a quantum error correction code (QECC) is needed to build a reliable quantum computer. Moreover, simply using QECCs is not sufficient in order that a quantum computer can tolerate errors. In quantum computation, each operation must be performed on the encoded states. Even when the gates used to perform these operations are noisy, the quantum computation must be tolerable to the errors. This is called fault-tolerant quantum computation and it is an essential condition for implementing a quantum computer. However, considering the fault tolerance, the overhead of the quantum computer increases exponentially. In this paper, we introduce fault-tolerant quantum computation and schemes to reduce the its overhead.

Quantum error correction (QEC) can overcome the errors experienced by qubits and is therefore an essential component of a future quantum computer. To implement QEC, a qubit is redundantly encoded in a higher-dimensional space using quantum states with carefully tailored symmetry properties. Projective measurements of these parity-type observables provide error syndrome information, with which errors can be corrected via simple operations. The 'break-even' point of QEC--at which the lifetime of a qubit exceeds the lifetime of the constituents of the system--has so far remained out of reach. Although previous works have demonstrated elements of QEC, they primarily illustrate the signatures or scaling properties of QEC codes rather than test the capacity of the system to preserve a qubit over time. Here we demonstrate a QEC system that reaches the break-even point by suppressing the natural errors due to energy loss for a qubit logically encoded in superpositions of Schrödinger-cat states of a superconducting resonator. We implement a full QEC protocol by using real-time feedback to encode, monitor naturally occurring errors, decode and correct. As measured by full process tomography, without any post-selection, the corrected qubit lifetime is 320 microseconds, which is longer than the lifetime of any of the parts of the system: 20 times longer than the lifetime of the transmon, about 2.2 times longer than the lifetime of an uncorrected logical encoding and about 1.1 longer than the lifetime of the best physical qubit (the |0〉f and |1〉f Fock states of the resonator). Our results illustrate the benefit of using hardware-efficient qubit encodings rather than traditional QEC schemes. Furthermore, they advance the field of experimental error correction from confirming basic concepts to exploring the metrics that drive system performance and the challenges in realizing a fault-tolerant system.

Noisy intermediate scale quantum (NISQ) systems are susceptible to errors that culminate in near-one hundred percent data loss. This is due to quantum state fragility and the incredibly high quantum communication error rates caused by decoherence, or quantum noise. As such, stabilizing qubit operational imprecision in quantum information processing is a critical area of research in quantum computing. Adaptive quantum machine learning (QML) methods, like unsupervised and fully entangled quantum generative adversarial networks is one such technology theorized to provide a breakthrough in quantum error suppression. Mechanizing the quantum error detection and correction process with QML provides a path forward from today’s monolithic quantum computers running almost exclusively single-core quantum processing unit (QPU) designs, to the next generation of federated quantum computers using multi-core QPUs. Automating the detection and correction of quantum errors in powerful NISQ devices will pave the way for fault-tolerant quantum computing, making quantum speeds at quantum scale suddenly achievable.

In a ground breaking discovery in 1994, Shor has shown that quantum computers, if built, can factor numbers efficiently. Since then quantum computing has become a burgeoning field of research, attracting theoreticians and experimentalists alike, and regrouping researchers from fields like computer science, physics, mathematics and engineering. Quantum information is very fragile and prone to decoherence. Yet by the middle of 1996 it has been shown that fault-tolerant quantum computation is possible. We give a simple description of the elements of quantum error-correction and quantum fault-tolerance. After characterizing quantum errors we present several error correction schemes and outline the elements of a full fledged fault-tolerant computation, which works error-free even though all of its components can be faulty. We also mention alternative approaches to error-correction, so called error-avoiding or decoherence-free schemes.

We analyze simple quantum error detection and quantum error correction protocols relevant to current experiments with superconducting qubits. We show that for qubits with energy relaxation the repetitive $N$-qubit codes cannot be used for quantum error correction, but can be used for quantum error detection. In the latter case it is sufficient to use only two qubits for the encoding. In the analysis we demonstrate a useful technique of unraveling the qubit energy relaxation into ``relaxation'' and ``no relaxation'' scenarios. Also, we propose and numerically analyze several two-qubit algorithms for quantum error detection and correction, which can be readily realized at the present-day level of the phase qubit technology.

We propose a method to assist fault mitigation in quantum computation through the use of sensors co-located near physical qubits. Specifically, we consider using transition edge sensors co-located on silicon substrates hosting superconducting qubits to monitor for energy injection from ionizing radiation, which has been demonstrated to increase decoherence in transmon qubits. We generalize from these two physical device concepts and explore the potential advantages of co-located sensors to assist fault mitigation in quantum computation. In the simplest scheme, co-located sensors beneficially assist rejection of calculations potentially affected by environmental disturbances. Investigating the potential computational advantage further required development of an extension to the standard formulation of quantum error correction. In a specific case of the standard three-qubit, bit-flip quantum error-correction code, we show that given a 20% overall error probability per qubit, approximately 90% of repeated calculation attempts are correctable. However, when sensor-detectable errors account for 45% of overall error probability, the use of co-located sensors uniquely associated with independent qubits boosts the fraction of correct final-state calculations to 96%, at the cost of rejecting 7% of repeated calculation attempts.