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Machine Learning for Designing Fast Quantum Gates

Ehsan Zahedinejad

Published Jan 1, 2016• 0 citations
Publisher

Abstract

Fault-tolerant quantum computing requires encoding the quantum information into logical qubits and performing the quantum information processing in a code-space. Quantum error correction codes, then, can be employed to diagnose and remove the possible errors in the quantum information, thereby avoiding the loss of information. Although a series of single- and two-qubit gates can be employed to construct a quantum error correcting circuit, however this decomposition approach is not practically desirable because it leads to circuits with long operation times. An alternative approach to designing a fast quantum circuit is to design quantum gates that act on a multi-qubit gate. Here I devise quantum control schemes to design high-fidelity single-shot multi-qubit (up to three) quantum gates. Quantum control task is to steer quantum dynamics towards closely realizing specific quantum operation by varying the external control parameters (external field) such that the resultant evolution closely approximates the desired evolution. A set of instructions that determines the control parameters, and hence the effectiveness of the control scheme, is called a policy. Machine learning algorithms can be employed to find successful policies for designing quantum gates. In particular, we employ supervised machine learning techniques to generate these successful policies. Finding successful policies is a feasibility problem for which optimization algorithms can be employed. Greedy algorithms are at the heart of machine learning techniques. They converge faster onto a successful policy and require less-computational resource than non-greedy algorithms. However, there is no guarantee that greedy algorithms succeed to a feasible solution when there exist constraints on i) gate operation time ii) computational resources, and iii) experimental resources. Our results show the failure of standard greedy machine learning algorithms and the superiority of non-greedy machine learning algorithms over greedy ones for designing quantum logic gates, when there exist constraints on the quantum system. We have also observed the failure of existing greedy and non-greedy techniques for designing high-fidelity three-qubit gates. Hence, we devised our machine learning technique called Subspace-Selective Self-adaptive Differential Evolution (SuSSADE). Each three-qubit gate designed by SuSSADE operates as fast as an entangling two-qubit gate under the same experimental constraints.

DOI
10.11575/prism/26805

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Fault-tolerant quantum computing requires encoding the quantum information into logical qubits and performing the quantum information processing in a code-space. Quantum error correction codes, then, can be employed to diagnose and remove the possible errors in the quantum information, thereby avoiding the loss of information. Although a series of single- and two-qubit gates can be employed to construct a quantum error correcting circuit, however this decomposition approach is not practically desirable because it leads to circuits with long operation times. An alternative approach to designing a fast quantum circuit is to design quantum gates that act on a multi-qubit gate. Here I devise quantum control schemes to design high-fidelity single-shot multi-qubit (up to three) quantum gates. Quantum control task is to steer quantum dynamics towards closely realizing specific quantum operation by varying the external control parameters (external field) such that the resultant evolution closely approximates the desired evolution. A set of instructions that determines the control parameters, and hence the effectiveness of the control scheme, is called a policy. Machine learning algorithms can be employed to find successful policies for designing quantum gates. In particular, we employ supervised machine learning techniques to generate these successful policies. Finding successful policies is a feasibility problem for which optimization algorithms can be employed. Greedy algorithms are at the heart of machine learning techniques. They converge faster onto a successful policy and require less-computational resource than non-greedy algorithms. However, there is no guarantee that greedy algorithms succeed to a feasible solution when there exist constraints on i) gate operation time ii) computational resources, and iii) experimental resources. Our results show the failure of standard greedy machine learning algorithms and the superiority of non-greedy machine learning algorithms over greedy ones for designing quantum logic gates, when there exist constraints on the quantum system. We have also observed the failure of existing greedy and non-greedy techniques for designing high-fidelity three-qubit gates. Hence, we devised our machine learning technique called Subspace-Selective Self-adaptive Differential Evolution (SuSSADE). Each three-qubit gate designed by SuSSADE operates as fast as an entangling two-qubit gate under the same experimental constraints.
To build a quantum computer, one can simplify the design of multi-qubit elements (logic gates) by treating them as series of well known one- and two-qubit gates---at the price of a slower processor. What if there were a handy means for $d\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}t$ multi-qubit design? The authors show numerically that, under existing experimental constraints, their recently developed SuSSADE scheme can yield three-qubit gates for single-shot entangling operations with fidelity greater than 99.9%. This powerful approach brings us that much closer to a fault-tolerant solid-state quantum computer.
This paper explores the development of dynamically reconfigurable quantum circuits tailored for machine learning applications. The core claim is the design of a hardware architecture capable of on-the-fly circuit reconfiguration, optimizing performance and resource utilization. This is achieved through the employment of programmable quantum gates and dynamic routing of quantum information dictated by the input data and algorithm requirements. The research addresses the limitations of static quantum circuit design by enabling adaptive and efficient quantum computation. Specifically, we investigate methods for mapping machine learning algorithms onto reconfigurable quantum circuits, considering factors such as circuit depth, qubit connectivity, and gate fidelity. We propose a framework for defining a quantum circuit's reconfiguration policy, incorporating real-time feedback from the execution of the algorithm. This framework allows the circuit to adapt its structure and operations to maximize computational efficiency. The theoretical analysis and design principles presented here contribute to the advancement of quantum machine learning by offering a pathway toward more flexible and powerful quantum computing architectures. The ultimate goal is to create quantum systems that can learn and adapt in a way analogous to biological neural networks, unlocking the full potential of quantum computation for complex machine learning problems.
In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility.
Adaptive quantum computing control is important for the stability and efficiency of quantum systems, especially in the presence of dynamic environmental noise and hardware fluctuations. We present a unified strategy for machine learning (ML) techniques to improve adaptive quantum control in two main directions: optimization speed and out-of-sample error. Here, we propose a hybrid learning method combining reinforcement learning (RL), deep neural networks (DNNs), and quantum-inspired models to actively and adaptively update quantum gate parameters and error mitigation strategies on the fly. Embedding of quantum noise models directly into the learning allows the system to predict and correct for decoherence, gate errors and measurement inaccuracy before they affect computational fidelity. Using extensive simulations as well as experimental validation on noisy intermediate-scale quantum (NISQ) processors, our improved ML model achieves a substantially higher fidelity and robustness compared to standard model-free and static control approaches. We introduce a novel feedback-loop mechanism based on online learning algorithms enabling the system to adjust for changing noise profiles and hardware drift without retraining. The optimization part uses metaheuristic methods to quickly converge to control parameters that perform well, and the error prevention mechanism features anomaly detection and prediction correction layers to minimize the effects of cumulative decoherence. The results demonstrate that our adaptive control framework can be used to not only speed up the convergence in training quantum algorithms, but also sustain the performance of gates against error at a variety of error rates with the reduction of the real-time impact of the error up to 35 %. This work highlights the promise of machine learningaugmented quantum control for improving reliability and scalability in quantum computing and serves as a stepping stone for future incorporation with fault-tolerant designs.
Quantum computation has the promise to solve problems that are not efficiently solvable by classical means. Yet, due to the fragile nature of quantum states, it is unavoidable that quantum systems are subject to decoherence, gate imperfections, and measurement errors. These problems stand as a major roadblock to the construction of large-scale fault-tolerant quantum computers. Conventional quantum error correction (QEC) codes, such as the surface code or Shor code, need to use an extensive amount of redundancy and classical control, which can be both computationally expensive and challenging to scale. In this context, we propose to investigate the use of machine learning (ML) techniques by realizing on-line identification, localization and correction of these quantum errors for fault-tolerant quantum computation by means of fault-tolerant quantum computing processes. Our model introduces a hybrids ML-QEC architecture that combines classical neural networks and decision-tree classifiers with quantum syndrome decoding. These models are trained on error syndromes produced using the approximation of common noise models (for example, depolarizing noise, dephasing and amplitude damping), simulated under different QEC schemes. The machine learning methods are trained with quantum error patterns and have a capability to rapidly infer where errors are likely to occur, as well as recommend correction operations with high confidence. As a result, the system reduces dramatically the delay and computational costs when compared to conventional decoding methods. There is also a self-adaptive learning module in the work, which recommences training on fresh quantum error distributions, meaning the system can adapt to changes in the environment (and also the decay of the hardware) as time progresses. The study further carries out ML predictions inside a real-time loop and cross-checks them with quantum state tomography and entanglement fidelity such as indicators. Experimental characterization on dedicated devices as well as noise models show that our ML-enhanced decoders reach higher fidelity and lower logical error rates compared to baseline decoders, especially in non-uniform or time-dependent noise. This work is a significant stride towards realizing practical fault-tolerant quantum computation by combining quantum error correction with the versatility and scalability of machine learning. With intelligent models to dictate learning, our proposed framework is able to eliminate a lot of trial-and-error manual processes in the conventional heuristic-based approach, and points to a potential path to achieve scalable quantum information processing platforms.
Precise and resilient quantum gate design is important for the building of quantum devices. In this paper, we consider the optimal and robust quantum gate design problem for three classes of two-level quantum systems. The aim is to construct quantum gates in a given fixed time with limited control resources. A modified dueling deep Q-learning (MDuDQL) is employed for the optimal and robust gate design problem. To improve the performance of the classical DuDQL method, we propose a unique semi-Markov DuDQL algorithm based on a modified action selection procedure, modified replay memory, and soft update procedure. The proposed algorithm outperforms ordinary DuDQL in terms of discovering global optimal or near-global optimal control protocols and faster convergence to a better policy. Moreover, the modified DuDQL agent shows improved performance in finding robust control protocols which achieve high-fidelity quantum gate design for varying uncertainties in a certain range. The effectiveness of the proposed algorithm for the optimal and robust gate design problems has been illustrated by numerical results.